R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("LogicReg-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('LogicReg')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "cumhaz"
> 
> ### * cumhaz
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cumhaz
> ### Title: Cumulative hazard transformation
> ### Aliases: cumhaz
> ### Keywords: logic methods nonparametric tree
> 
> ### ** Examples
> 
> data(logreg.testdat)
> #
> # this is not survival data, but it shows the functionality
> yy <- cumhaz(exp(logreg.testdat[,1]), logreg.testdat[, 2])
Loading required package: splines
> # then we would use
> # logreg(resp=yy, cens=logreg.testdat[,2], type=5, ...
> # insted of
> # logreg(resp=logreg.testdat[,1], cens=logreg.testdat[,2], type=4, ...
> 
> 
> 
> cleanEx(); ..nameEx <- "eval.logreg"
> 
> ### * eval.logreg
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: eval.logreg
> ### Title: Evaluate a Logic Regression tree
> ### Aliases: eval.logreg
> ### Keywords: logic methods nonparametric tree
> 
> ### ** Examples
> 
> data(logreg.savefit1)
> # myanneal <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 1000)
> # logreg.savefit1 <- logreg(resp = logreg.testdat[,1], bin=logreg.testdat[, 2:21], 
> #                type = 2, select = 1, ntrees = 2, anneal.control = myanneal)
> tree1 <- eval.logreg(logreg.savefit1$model$trees[[1]], logreg.savefit1$binary)
> tree2 <- eval.logreg(logreg.savefit1$model$trees[[2]], logreg.savefit1$binary)
> alltrees <- eval.logreg(logreg.savefit1$model, logreg.savefit1$binary)
> 
> 
> 
> cleanEx(); ..nameEx <- "frame.logreg"
> 
> ### * frame.logreg
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: frame.logreg
> ### Title: Constructs a data frame for one or more Logic Regression models
> ### Aliases: frame.logreg
> ### Keywords: logic methods nonparametric tree
> 
> ### ** Examples
> 
> data(logreg.savefit1,logreg.savefit2,logreg.savefit6)
> #
> # fit a single mode
> # myanneal <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 1000)
> # logreg.savefit1 <- logreg(resp = logreg.testdat[,1], bin=logreg.testdat[, 2:21], 
> #               type = 2, select = 1, ntrees = 2, anneal.control = myanneal)
> frame1 <- frame.logreg(logreg.savefit1)
> #
> # a complete sequence
> # myanneal2 <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 0)
> # logreg.savefit2 <- logreg(select = 2, ntrees = c(1,2), nleaves =c(1,7), 
> #               oldfit = logreg.savefit1, anneal.control = myanneal2)
> frame2 <- frame.logreg(logreg.savefit2)
> #
> # a greedy sequence
> # logreg.savefit6 <- logreg(select = 6, ntrees = 2, nleaves =c(1,12), oldfit = logreg.savefit1)
> frame6  <- frame.logreg(logreg.savefit6, msz = 3:5) # restrict the size
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "logreg"
> 
> ### * logreg
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: logreg
> ### Title: Logic Regression
> ### Aliases: logreg
> ### Keywords: logic methods nonparametric tree
> 
> ### ** Examples
> 
> data(logreg.savefit1,logreg.savefit2,logreg.savefit3,logreg.savefit4,
+      logreg.savefit5,logreg.savefit6,logreg.savefit7,logreg.testdat)
> 
> myanneal <- logreg.anneal.control(start = -1, end = -4, iter = 2500, update = 100)
> # in practie we would use 25000 iterations or more - the use of 2500 is only
> # to have the examples run fast
> ## Not run: myanneal <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 1000)
> fit1 <- logreg(resp = logreg.testdat[,1], bin=logreg.testdat[, 2:21], type = 2,
+                select = 1, ntrees = 2, anneal.control = myanneal)
  
log-temp current score    best score        acc / rej /sing    current parameters
  -1.000        1.4941        1.4941     0(  0)     0     0    2.886    0.000    0.000
  -1.120        1.2097        1.0333    59(  1)    39     1    2.576   -0.654    1.778
  -1.240        1.1512        1.0333    43(  2)    53     2    1.876    0.003    1.912
  -1.360        1.1463        1.0333    44(  0)    56     0    2.185   -0.341    1.905
  -1.480        1.0480        1.0333    46(  4)    50     0    4.159   -0.176   -2.130
  -1.600        1.1504        1.0333    47(  7)    46     0    3.772    0.113   -1.917
  -1.720        1.1300        1.0333    39(  7)    53     1    3.038    0.809   -1.898
  -1.840        1.0457        1.0333    35(  3)    61     1    3.918   -2.141    0.169
  -1.960        1.0414        1.0333    40(  2)    58     0    3.938   -2.152    0.297
  -2.080        1.0482        1.0333    41(  3)    56     0    3.980   -2.134    0.117
  -2.200        1.0486        1.0333    43(  0)    57     0    3.963   -2.133    0.065
  -2.320        1.0376        1.0333    27( 12)    60     1    4.116   -2.118   -0.317
  -2.440        1.0033        1.0033    13(  4)    83     0    4.090   -2.127   -1.090
  -2.560        0.9885        0.9885     5(  8)    87     0    4.115   -2.116   -1.111
  -2.680        0.9885        0.9878     3(  7)    90     0    4.115   -2.116   -1.111
  -2.800        0.9844        0.9844     5(  8)    87     0    4.116   -2.103   -1.122
  -2.920        0.9844        0.9844     0(  3)    97     0    4.116   -2.103   -1.122
  -3.040        0.9834        0.9834     1(  2)    96     1    4.118   -2.107   -1.138
  -3.160        0.9841        0.9834     1( 10)    89     0    4.112   -2.079   -1.080
  -3.280        0.9789        0.9789     2(  9)    89     0    4.116   -2.109   -1.222
  -3.400        0.9783        0.9783     1(  5)    94     0    4.119   -2.118   -1.248
  -3.520        0.9771        0.9771     1(  6)    93     0    4.119   -2.113   -1.248
  -3.640        0.9762        0.9762     1(  7)    92     0    4.105   -2.114   -1.234
  -3.760        0.9733        0.9733     1(  3)    96     0    4.113   -2.119   -1.239
  -3.880        0.9726        0.9726     1(  1)    98     0    4.116   -2.120   -1.224
  -4.000        0.9726        0.9726     0(  6)    92     2    4.116   -2.120   -1.224
> # the best score should be in the 0.97-0.98 range
> plot(fit1)
> # you'll probably see X1-X4 as well as a few noise predictors
> # use logreg.savefit1 for the results with 25000 iterations
> plot(logreg.savefit1)
> print(logreg.savefit1)
score 0.966 
 -1.3 * (((X14 or (not X5)) and ((not X1) and (not X2))) and (((not X3) or X1) or ((not X20) and (not X2)))) +2.15 * (((not X4) or ((not X13) and (not X11))) and (not X3))
> z <- predict(logreg.savefit1)
> plot(z, logreg.testdat[,1]-z, xlab="fitted values", ylab="residuals")
> # there are some streaks, thanks to the very discrete predictions
> #
> # a bit less output
> myanneal2 <- logreg.anneal.control(start = -1, end = -4, iter = 1000, update = 0)
> # in practie we would use 25000 iterations or more - the use of 1000 is only 
> # to have the examples run fast
> ## Not run: myanneal2 <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 0)
> #
> # fit multiple models
> fit2 <- logreg(resp = logreg.testdat[,1], bin=logreg.testdat[, 2:21], type = 2,
+               select = 2, ntrees = c(1,2), nleaves =c(1,7), anneal.control = myanneal2)
  
The number of trees in these models is   1
  
The model size is   1
The best model with   1 trees of size  1 has a score of       1.1500
  
The model size is   2
The best model with   1 trees of size  2 has a score of       1.0481
  
The model size is   3
The best model with   1 trees of size  3 has a score of       1.0457
  
The model size is   4
The best model with   1 trees of size  4 has a score of       1.1500
  
The model size is   5
The best model with   1 trees of size  5 has a score of       1.0481
  
The model size is   6
The best model with   1 trees of size  6 has a score of       1.1500
  
The model size is   7
The best model with   1 trees of size  7 has a score of       1.0481
  
The number of trees in these models is   2
  
The size for this model is smaller than  the number of trees you requested. 
( 1 versus  2)
To save CPU time, we will skip this run.
On to the next model...
  
The model size is   2
The best model with   2 trees of size  2 has a score of       1.1168
  
The model size is   3
The best model with   2 trees of size  3 has a score of       1.0333
  
The model size is   4
The best model with   2 trees of size  4 has a score of       1.0271
  
The model size is   5
The best model with   2 trees of size  5 has a score of       1.0484
  
The model size is   6
The best model with   2 trees of size  6 has a score of       0.9814
  
The model size is   7
The best model with   2 trees of size  7 has a score of       0.9875
> # equivalent
> fit2 <- logreg(select = 2, ntrees = c(1,2), nleaves =c(1,7), oldfit = fit1,
+                anneal.control = myanneal2)
  
The number of trees in these models is   1
  
The model size is   1
The best model with   1 trees of size  1 has a score of       1.1500
  
The model size is   2
The best model with   1 trees of size  2 has a score of       1.0481
  
The model size is   3
The best model with   1 trees of size  3 has a score of       1.0481
  
The model size is   4
The best model with   1 trees of size  4 has a score of       1.0481
  
The model size is   5
The best model with   1 trees of size  5 has a score of       1.0457
  
The model size is   6
The best model with   1 trees of size  6 has a score of       1.0481
  
The model size is   7
The best model with   1 trees of size  7 has a score of       1.0481
  
The number of trees in these models is   2
  
The size for this model is smaller than  the number of trees you requested. 
( 1 versus  2)
To save CPU time, we will skip this run.
On to the next model...
  
The model size is   2
The best model with   2 trees of size  2 has a score of       1.1168
  
The model size is   3
The best model with   2 trees of size  3 has a score of       1.0333
  
The model size is   4
The best model with   2 trees of size  4 has a score of       1.0484
  
The model size is   5
The best model with   2 trees of size  5 has a score of       1.0822
  
The model size is   6
The best model with   2 trees of size  6 has a score of       0.9816
  
The model size is   7
The best model with   2 trees of size  7 has a score of       1.0142
> plot(fit2)
> print(fit2)
1 trees with 1 leaves: score is 1.15 
 +1.91 * (not X3)
1 trees with 2 leaves: score is 1.048 
 +2.13 * ((not X3) and (not X4))
1 trees with 3 leaves: score is 1.048 
 -2.13 * (X3 or X4)
1 trees with 4 leaves: score is 1.048 
 +2.13 * ((not X3) and (not X4))
1 trees with 5 leaves: score is 1.046 
 -2.14 * (X3 or (X4 and X13))
1 trees with 6 leaves: score is 1.048 
 +2.13 * ((not X3) and (not X4))
1 trees with 7 leaves: score is 1.048 
 +2.13 * ((not X3) and (not X4))
2 trees with 2 leaves: score is 1.117 
 -1.89 * X3 +0.904 * (not X4)
2 trees with 3 leaves: score is 1.033 
 -2.13 * (X3 or X4) +0.401 * X1
2 trees with 4 leaves: score is 1.048 
 -2.03 * X4 +2.16 * ((not X3) or X4)
2 trees with 5 leaves: score is 1.082 
 +1.87 * (not X3) -1.07 * (((not X1) or X4) and (not X2))
2 trees with 6 leaves: score is 0.982 
 +2.12 * ((not X4) and (not X3)) -1.21 * (((not X1) and (not X2)) and ((not X7) or (not X20)))
2 trees with 7 leaves: score is 1.014 
 -0.693 * ((X10 and X4) or ((X19 or (not X2)) and (not X1))) -2.12 * (X3 or X4)
> # use logreg.savefit2 for the results with 25000 iterations
> plot(logreg.savefit2)
> print(logreg.savefit2)
1 trees with 1 leaves: score is 1.15 
 -1.91 * X3
1 trees with 2 leaves: score is 1.048 
 +2.13 * ((not X3) and (not X4))
1 trees with 3 leaves: score is 1.046 
 +2.14 * ((not X3) and ((not X13) or (not X4)))
1 trees with 4 leaves: score is 1.042 
 +2.14 * ((((not X13) and (not X11)) or (not X4)) and (not X3))
1 trees with 5 leaves: score is 1.042 
 +2.14 * ((not X3) and ((not X4) or ((not X13) and (not X11))))
1 trees with 6 leaves: score is 1.042 
 -2.14 * (X3 or (X4 and (X13 or (not X19))))
1 trees with 7 leaves: score is 1.04 
 +2.15 * ((((not X4) or (not X13)) and (not X3)) or (((not X6) and X14) and ((not X1) and (not X12))))
2 trees with 2 leaves: score is 1.117 
 +1.89 * (not X3) -0.904 * X4
2 trees with 3 leaves: score is 1.033 
 +0.401 * X1 +2.13 * ((not X3) and (not X4))
2 trees with 4 leaves: score is 0.988 
 -1.11 * ((not X1) and (not X2)) -2.12 * (X3 or X4)
2 trees with 5 leaves: score is 0.982 
 +1.22 * ((X2 or X1) or X20) +2.12 * ((not X4) and (not X3))
2 trees with 6 leaves: score is 0.979 
 +2.13 * ((not X3) and (not X4)) +1.23 * ((X2 or X1) or (X20 and X3))
2 trees with 7 leaves: score is 0.978 
 +2.13 * ((not X3) and (not X4)) -1.29 * (((not X7) or ((not X5) or (not X15))) and ((not X1) and (not X2)))
> # After an initial steep decline, the scores only get slightly better
> # for models with more than four leaves and two trees. 
> #
> # cross validation
> fit3 <- logreg(resp = logreg.testdat[,1], bin=logreg.testdat[, 2:21], type = 2,
+                select = 3, ntrees = c(1,2), nleaves=c(1,7), anneal.control = myanneal2)
  
The number of trees in these models is   1
  
The model size is   1
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  1 trees;   1 leaves] CV score:         1.1575       1.1575       1.1038       1.1038
Step  2 of 10 [  1 trees;   1 leaves] CV score:         1.1619       1.1597       1.0589       1.0813
Step  3 of 10 [  1 trees;   1 leaves] CV score:         1.1566       1.1587       1.1126       1.0918
Step  4 of 10 [  1 trees;   1 leaves] CV score:         1.1218       1.1494       1.4067       1.1705
Step  5 of 10 [  1 trees;   1 leaves] CV score:         1.1096       1.1415       1.4940       1.2352
Step  6 of 10 [  1 trees;   1 leaves] CV score:         1.1607       1.1447       1.0714       1.2079
Step  7 of 10 [  1 trees;   1 leaves] CV score:         1.1705       1.1484       0.9667       1.1734
Step  8 of 10 [  1 trees;   1 leaves] CV score:         1.1653       1.1505       1.0252       1.1549
Step  9 of 10 [  1 trees;   1 leaves] CV score:         1.1450       1.1499       1.2202       1.1622
Step 10 of 10 [  1 trees;   1 leaves] CV score:         1.1517       1.1501       1.1583       1.1618
  
The model size is   2
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  1 trees;   2 leaves] CV score:         1.0552       1.0552       1.0024       1.0024
Step  2 of 10 [  1 trees;   2 leaves] CV score:         1.0541       1.0546       1.0142       1.0083
Step  3 of 10 [  1 trees;   2 leaves] CV score:         1.1566       1.0886       1.1126       1.0431
Step  4 of 10 [  1 trees;   2 leaves] CV score:         1.0366       1.0756       1.1738       1.0758
Step  5 of 10 [  1 trees;   2 leaves] CV score:         1.0190       1.0643       1.3066       1.1219
Step  6 of 10 [  1 trees;   2 leaves] CV score:         1.0449       1.0611       1.0987       1.1181
Step  7 of 10 [  1 trees;   2 leaves] CV score:         1.0577       1.0606       0.9787       1.0982
Step  8 of 10 [  1 trees;   2 leaves] CV score:         1.0561       1.0600       0.9940       1.0851
Step  9 of 10 [  1 trees;   2 leaves] CV score:         1.0364       1.0574       1.1725       1.0948
Step 10 of 10 [  1 trees;   2 leaves] CV score:         1.0521       1.0569       1.0320       1.0886
  
The model size is   3
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  1 trees;   3 leaves] CV score:         1.0526       1.0526       1.0018       1.0018
Step  2 of 10 [  1 trees;   3 leaves] CV score:         1.0516       1.0521       1.0126       1.0072
Step  3 of 10 [  1 trees;   3 leaves] CV score:         1.0662       1.0568       0.8578       0.9574
Step  4 of 10 [  1 trees;   3 leaves] CV score:         1.0366       1.0517       1.1738       1.0115
Step  5 of 10 [  1 trees;   3 leaves] CV score:         1.1096       1.0633       1.4940       1.1080
Step  6 of 10 [  1 trees;   3 leaves] CV score:         1.0449       1.0602       1.0987       1.1065
Step  7 of 10 [  1 trees;   3 leaves] CV score:         1.0577       1.0599       0.9787       1.0882
Step  8 of 10 [  1 trees;   3 leaves] CV score:         1.0561       1.0594       0.9940       1.0764
Step  9 of 10 [  1 trees;   3 leaves] CV score:         1.0339       1.0566       1.1703       1.0869
Step 10 of 10 [  1 trees;   3 leaves] CV score:         1.0495       1.0559       1.0318       1.0814
  
The model size is   4
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  1 trees;   4 leaves] CV score:         1.0486       1.0486       1.0027       1.0027
Step  2 of 10 [  1 trees;   4 leaves] CV score:         1.0475       1.0480       1.0142       1.0084
Step  3 of 10 [  1 trees;   4 leaves] CV score:         1.0662       1.0541       0.8578       0.9582
Step  4 of 10 [  1 trees;   4 leaves] CV score:         1.0366       1.0497       1.1738       1.0121
Step  5 of 10 [  1 trees;   4 leaves] CV score:         1.0140       1.0426       1.3054       1.0708
Step  6 of 10 [  1 trees;   4 leaves] CV score:         1.1607       1.0623       1.0714       1.0709
Step  7 of 10 [  1 trees;   4 leaves] CV score:         1.0577       1.0616       0.9787       1.0577
Step  8 of 10 [  1 trees;   4 leaves] CV score:         1.0561       1.0609       0.9940       1.0497
Step  9 of 10 [  1 trees;   4 leaves] CV score:         1.0364       1.0582       1.1725       1.0634
Step 10 of 10 [  1 trees;   4 leaves] CV score:         1.0455       1.0569       1.0319       1.0602
  
The model size is   5
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  1 trees;   5 leaves] CV score:         1.0502       1.0502       1.0023       1.0023
Step  2 of 10 [  1 trees;   5 leaves] CV score:         1.0516       1.0509       1.0126       1.0075
Step  3 of 10 [  1 trees;   5 leaves] CV score:         1.0689       1.0569       0.8567       0.9572
Step  4 of 10 [  1 trees;   5 leaves] CV score:         1.0366       1.0518       1.1738       1.0113
Step  5 of 10 [  1 trees;   5 leaves] CV score:         1.0190       1.0453       1.3066       1.0704
Step  6 of 10 [  1 trees;   5 leaves] CV score:         1.0382       1.0441       1.0992       1.0752
Step  7 of 10 [  1 trees;   5 leaves] CV score:         1.0525       1.0453       1.0038       1.0650
Step  8 of 10 [  1 trees;   5 leaves] CV score:         1.0494       1.0458       1.0098       1.0581
Step  9 of 10 [  1 trees;   5 leaves] CV score:         1.0339       1.0445       1.1703       1.0705
Step 10 of 10 [  1 trees;   5 leaves] CV score:         1.0521       1.0452       1.0320       1.0667
  
The model size is   6
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  1 trees;   6 leaves] CV score:         1.0523       1.0523       1.0323       1.0323
Step  2 of 10 [  1 trees;   6 leaves] CV score:         1.0541       1.0532       1.0142       1.0233
Step  3 of 10 [  1 trees;   6 leaves] CV score:         1.0689       1.0584       0.8567       0.9677
Step  4 of 10 [  1 trees;   6 leaves] CV score:         1.0299       1.0513       1.2437       1.0367
Step  5 of 10 [  1 trees;   6 leaves] CV score:         1.1096       1.0630       1.4940       1.1282
Step  6 of 10 [  1 trees;   6 leaves] CV score:         1.0449       1.0599       1.0987       1.1233
Step  7 of 10 [  1 trees;   6 leaves] CV score:         1.0525       1.0589       1.0038       1.1062
Step  8 of 10 [  1 trees;   6 leaves] CV score:         1.0561       1.0585       0.9940       1.0922
Step  9 of 10 [  1 trees;   6 leaves] CV score:         1.1450       1.0681       1.2202       1.1064
Step 10 of 10 [  1 trees;   6 leaves] CV score:         1.0495       1.0663       1.0318       1.0989
  
The model size is   7
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  1 trees;   7 leaves] CV score:         1.0552       1.0552       1.0024       1.0024
Step  2 of 10 [  1 trees;   7 leaves] CV score:         1.0541       1.0546       1.0142       1.0083
Step  3 of 10 [  1 trees;   7 leaves] CV score:         1.0689       1.0594       0.8567       0.9578
Step  4 of 10 [  1 trees;   7 leaves] CV score:         1.0366       1.0537       1.1738       1.0118
Step  5 of 10 [  1 trees;   7 leaves] CV score:         1.0140       1.0457       1.3054       1.0705
Step  6 of 10 [  1 trees;   7 leaves] CV score:         1.0449       1.0456       1.0987       1.0752
Step  7 of 10 [  1 trees;   7 leaves] CV score:         1.0525       1.0466       1.0038       1.0650
Step  8 of 10 [  1 trees;   7 leaves] CV score:         1.0554       1.0477       0.9959       1.0564
Step  9 of 10 [  1 trees;   7 leaves] CV score:         1.0364       1.0464       1.1725       1.0693
Step 10 of 10 [  1 trees;   7 leaves] CV score:         1.0471       1.0465       1.0319       1.0655
  
The number of trees in these models is   2
  
The size for this model is smaller than  the number of trees you requested. 
( 1 versus  2)
To save CPU time, we will skip this run.
On to the next model...
  
The model size is   2
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  2 trees;   2 leaves] CV score:         1.0552       1.0552       1.0024       1.0024
Step  2 of 10 [  2 trees;   2 leaves] CV score:         1.1287       1.0919       1.0371       1.0198
Step  3 of 10 [  2 trees;   2 leaves] CV score:         1.1301       1.1047       1.0263       1.0220
Step  4 of 10 [  2 trees;   2 leaves] CV score:         1.0968       1.1027       1.3287       1.0986
Step  5 of 10 [  2 trees;   2 leaves] CV score:         1.0763       1.0974       1.4746       1.1738
Step  6 of 10 [  2 trees;   2 leaves] CV score:         1.1194       1.1011       1.1325       1.1669
Step  7 of 10 [  2 trees;   2 leaves] CV score:         1.1375       1.1063       0.9415       1.1347
Step  8 of 10 [  2 trees;   2 leaves] CV score:         1.1241       1.1085       1.0894       1.1291
Step  9 of 10 [  2 trees;   2 leaves] CV score:         1.1046       1.1081       1.2635       1.1440
Step 10 of 10 [  2 trees;   2 leaves] CV score:         1.1228       1.1095       1.0979       1.1394
  
The model size is   3
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  2 trees;   3 leaves] CV score:         1.0421       1.0421       0.9821       0.9821
Step  2 of 10 [  2 trees;   3 leaves] CV score:         1.0404       1.0413       0.9989       0.9905
Step  3 of 10 [  2 trees;   3 leaves] CV score:         1.0485       1.0437       0.9222       0.9677
Step  4 of 10 [  2 trees;   3 leaves] CV score:         1.0377       1.0422       1.1853       1.0221
Step  5 of 10 [  2 trees;   3 leaves] CV score:         1.0080       1.0354       1.2800       1.0737
Step  6 of 10 [  2 trees;   3 leaves] CV score:         1.0344       1.0352       1.0602       1.0714
Step  7 of 10 [  2 trees;   3 leaves] CV score:         1.0371       1.0355       1.0354       1.0663
Step  8 of 10 [  2 trees;   3 leaves] CV score:         1.0413       1.0362       0.9897       1.0567
Step  9 of 10 [  2 trees;   3 leaves] CV score:         1.0279       1.0353       1.1230       1.0641
Step 10 of 10 [  2 trees;   3 leaves] CV score:         1.0360       1.0353       1.0413       1.0618
  
The model size is   4
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  2 trees;   4 leaves] CV score:         1.0326       1.0326       1.0188       1.0188
Step  2 of 10 [  2 trees;   4 leaves] CV score:         0.9950       1.0138       0.9590       0.9889
Step  3 of 10 [  2 trees;   4 leaves] CV score:         1.0057       1.0111       0.8454       0.9411
Step  4 of 10 [  2 trees;   4 leaves] CV score:         0.9699       1.0008       1.1859       1.0023
Step  5 of 10 [  2 trees;   4 leaves] CV score:         0.9711       0.9949       1.1763       1.0371
Step  6 of 10 [  2 trees;   4 leaves] CV score:         1.0308       1.0009       1.0918       1.0462
Step  7 of 10 [  2 trees;   4 leaves] CV score:         1.1217       1.0181       0.9450       1.0317
Step  8 of 10 [  2 trees;   4 leaves] CV score:         0.9896       1.0146       1.0118       1.0292
Step  9 of 10 [  2 trees;   4 leaves] CV score:         1.0198       1.0151       1.1362       1.0411
Step 10 of 10 [  2 trees;   4 leaves] CV score:         1.0327       1.0169       1.0166       1.0387
  
The model size is   5
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  2 trees;   5 leaves] CV score:         1.0412       1.0412       0.9936       0.9936
Step  2 of 10 [  2 trees;   5 leaves] CV score:         0.9884       1.0148       0.9573       0.9754
Step  3 of 10 [  2 trees;   5 leaves] CV score:         1.0047       1.0114       0.8455       0.9321
Step  4 of 10 [  2 trees;   5 leaves] CV score:         1.0158       1.0125       1.2298       1.0065
Step  5 of 10 [  2 trees;   5 leaves] CV score:         1.0669       1.0234       1.4205       1.0893
Step  6 of 10 [  2 trees;   5 leaves] CV score:         0.9872       1.0174       0.9702       1.0695
Step  7 of 10 [  2 trees;   5 leaves] CV score:         0.9828       1.0124       1.0101       1.0610
Step  8 of 10 [  2 trees;   5 leaves] CV score:         1.0392       1.0158       1.0072       1.0543
Step  9 of 10 [  2 trees;   5 leaves] CV score:         1.0261       1.0169       1.1429       1.0641
Step 10 of 10 [  2 trees;   5 leaves] CV score:         1.0360       1.0188       1.0413       1.0618
  
The model size is   6
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  2 trees;   6 leaves] CV score:         0.9898       0.9898       0.9457       0.9457
Step  2 of 10 [  2 trees;   6 leaves] CV score:         0.9877       0.9887       0.9545       0.9501
Step  3 of 10 [  2 trees;   6 leaves] CV score:         1.0057       0.9944       0.8454       0.9152
Step  4 of 10 [  2 trees;   6 leaves] CV score:         0.9681       0.9878       1.1430       0.9722
Step  5 of 10 [  2 trees;   6 leaves] CV score:         0.9711       0.9845       1.1763       1.0130
Step  6 of 10 [  2 trees;   6 leaves] CV score:         1.0268       0.9916       1.0523       1.0195
Step  7 of 10 [  2 trees;   6 leaves] CV score:         1.0213       0.9958       1.0587       1.0251
Step  8 of 10 [  2 trees;   6 leaves] CV score:         0.9817       0.9940       1.0223       1.0248
Step  9 of 10 [  2 trees;   6 leaves] CV score:         1.0191       0.9968       1.1481       1.0385
Step 10 of 10 [  2 trees;   6 leaves] CV score:         0.9795       0.9951       1.0421       1.0389
  
The model size is   7
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  2 trees;   7 leaves] CV score:         1.0346       1.0346       0.9920       0.9920
Step  2 of 10 [  2 trees;   7 leaves] CV score:         0.9937       1.0142       0.9583       0.9751
Step  3 of 10 [  2 trees;   7 leaves] CV score:         1.0164       1.0149       0.9209       0.9571
Step  4 of 10 [  2 trees;   7 leaves] CV score:         0.9681       1.0032       1.1430       1.0036
Step  5 of 10 [  2 trees;   7 leaves] CV score:         0.9631       0.9952       1.1825       1.0393
Step  6 of 10 [  2 trees;   7 leaves] CV score:         1.0239       1.0000       1.0342       1.0385
Step  7 of 10 [  2 trees;   7 leaves] CV score:         0.9827       0.9975       1.0426       1.0391
Step  8 of 10 [  2 trees;   7 leaves] CV score:         1.0320       1.0018       1.0043       1.0347
Step  9 of 10 [  2 trees;   7 leaves] CV score:         1.0130       1.0031       1.1265       1.0449
Step 10 of 10 [  2 trees;   7 leaves] CV score:         1.0499       1.0077       1.0436       1.0448
> # equivalent
> fit3 <- logreg(select = 3, oldfit = fit2)
  
The number of trees in these models is   1
  
The model size is   1
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  1 trees;   1 leaves] CV score:         1.1574       1.1574       1.1055       1.1055
Step  2 of 10 [  1 trees;   1 leaves] CV score:         1.1471       1.1522       1.2046       1.1551
Step  3 of 10 [  1 trees;   1 leaves] CV score:         1.1633       1.1559       1.0449       1.1183
Step  4 of 10 [  1 trees;   1 leaves] CV score:         1.1667       1.1586       1.0101       1.0913
Step  5 of 10 [  1 trees;   1 leaves] CV score:         1.1637       1.1596       1.0464       1.0823
Step  6 of 10 [  1 trees;   1 leaves] CV score:         1.1566       1.1591       1.1122       1.0873
Step  7 of 10 [  1 trees;   1 leaves] CV score:         1.1363       1.1559       1.2972       1.1173
Step  8 of 10 [  1 trees;   1 leaves] CV score:         1.1118       1.1503       1.4788       1.1625
Step  9 of 10 [  1 trees;   1 leaves] CV score:         1.1457       1.1498       1.2124       1.1680
Step 10 of 10 [  1 trees;   1 leaves] CV score:         1.1513       1.1500       1.1630       1.1675
  
The model size is   2
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  1 trees;   2 leaves] CV score:         1.0576       1.0576       0.9803       0.9803
Step  2 of 10 [  1 trees;   2 leaves] CV score:         1.0421       1.0498       1.1255       1.0529
Step  3 of 10 [  1 trees;   2 leaves] CV score:         1.0591       1.0529       0.9636       1.0231
Step  4 of 10 [  1 trees;   2 leaves] CV score:         1.0504       1.0523       1.0543       1.0309
Step  5 of 10 [  1 trees;   2 leaves] CV score:         1.0581       1.0535       0.9767       1.0201
Step  6 of 10 [  1 trees;   2 leaves] CV score:         1.0527       1.0533       1.0263       1.0211
Step  7 of 10 [  1 trees;   2 leaves] CV score:         1.0460       1.0523       1.0928       1.0314
Step  8 of 10 [  1 trees;   2 leaves] CV score:         1.0217       1.0485       1.2879       1.0634
Step  9 of 10 [  1 trees;   2 leaves] CV score:         1.0586       1.0496       0.9694       1.0530
Step 10 of 10 [  1 trees;   2 leaves] CV score:         1.0333       1.0480       1.1990       1.0676
  
The model size is   3
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  1 trees;   3 leaves] CV score:         1.0576       1.0576       0.9803       0.9803
Step  2 of 10 [  1 trees;   3 leaves] CV score:         1.0393       1.0484       1.1265       1.0534
Step  3 of 10 [  1 trees;   3 leaves] CV score:         1.0591       1.0520       0.9636       1.0234
Step  4 of 10 [  1 trees;   3 leaves] CV score:         1.0504       1.0516       1.0543       1.0312
Step  5 of 10 [  1 trees;   3 leaves] CV score:         1.0581       1.0529       0.9767       1.0203
Step  6 of 10 [  1 trees;   3 leaves] CV score:         1.0477       1.0520       1.0488       1.0250
Step  7 of 10 [  1 trees;   3 leaves] CV score:         1.0421       1.0506       1.1963       1.0495
Step  8 of 10 [  1 trees;   3 leaves] CV score:         1.0217       1.0470       1.2879       1.0793
Step  9 of 10 [  1 trees;   3 leaves] CV score:         1.0586       1.0483       0.9694       1.0671
Step 10 of 10 [  1 trees;   3 leaves] CV score:         1.0333       1.0468       1.1990       1.0803
  
The model size is   4
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  1 trees;   4 leaves] CV score:         1.1574       1.1574       1.1055       1.1055
Step  2 of 10 [  1 trees;   4 leaves] CV score:         1.1471       1.1522       1.2046       1.1551
Step  3 of 10 [  1 trees;   4 leaves] CV score:         1.0591       1.1212       0.9636       1.0912
Step  4 of 10 [  1 trees;   4 leaves] CV score:         1.0504       1.1035       1.0543       1.0820
Step  5 of 10 [  1 trees;   4 leaves] CV score:         1.0516       1.0931       0.9909       1.0638
Step  6 of 10 [  1 trees;   4 leaves] CV score:         1.0527       1.0864       1.0263       1.0575
Step  7 of 10 [  1 trees;   4 leaves] CV score:         1.1363       1.0935       1.2972       1.0918
Step  8 of 10 [  1 trees;   4 leaves] CV score:         1.1118       1.0958       1.4788       1.1402
Step  9 of 10 [  1 trees;   4 leaves] CV score:         1.0578       1.0916       1.1687       1.1433
Step 10 of 10 [  1 trees;   4 leaves] CV score:         1.0308       1.0855       1.1978       1.1488
  
The model size is   5
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  1 trees;   5 leaves] CV score:         1.1574       1.1574       1.1055       1.1055
Step  2 of 10 [  1 trees;   5 leaves] CV score:         1.0421       1.0997       1.1255       1.1155
Step  3 of 10 [  1 trees;   5 leaves] CV score:         1.0567       1.0854       0.9617       1.0642
Step  4 of 10 [  1 trees;   5 leaves] CV score:         1.0504       1.0766       1.0543       1.0618
Step  5 of 10 [  1 trees;   5 leaves] CV score:         1.0516       1.0716       0.9909       1.0476
Step  6 of 10 [  1 trees;   5 leaves] CV score:         1.0527       1.0685       1.0263       1.0440
Step  7 of 10 [  1 trees;   5 leaves] CV score:         1.0421       1.0647       1.1963       1.0658
Step  8 of 10 [  1 trees;   5 leaves] CV score:         1.0217       1.0593       1.2879       1.0936
Step  9 of 10 [  1 trees;   5 leaves] CV score:         1.0586       1.0592       0.9694       1.0798
Step 10 of 10 [  1 trees;   5 leaves] CV score:         1.0308       1.0564       1.1978       1.0916
  
The model size is   6
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  1 trees;   6 leaves] CV score:         1.0576       1.0576       0.9803       0.9803
Step  2 of 10 [  1 trees;   6 leaves] CV score:         1.0421       1.0498       1.1255       1.0529
Step  3 of 10 [  1 trees;   6 leaves] CV score:         1.0567       1.0521       0.9617       1.0225
Step  4 of 10 [  1 trees;   6 leaves] CV score:         1.0455       1.0505       1.0530       1.0301
Step  5 of 10 [  1 trees;   6 leaves] CV score:         1.0516       1.0507       0.9761       1.0193
Step  6 of 10 [  1 trees;   6 leaves] CV score:         1.0527       1.0510       1.0263       1.0205
Step  7 of 10 [  1 trees;   6 leaves] CV score:         1.0351       1.0487       1.1986       1.0459
Step  8 of 10 [  1 trees;   6 leaves] CV score:         1.0189       1.0450       1.2870       1.0761
Step  9 of 10 [  1 trees;   6 leaves] CV score:         1.0531       1.0459       0.9682       1.0641
Step 10 of 10 [  1 trees;   6 leaves] CV score:         1.0282       1.0441       1.1988       1.0775
  
The model size is   7
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  1 trees;   7 leaves] CV score:         1.0576       1.0576       0.9803       0.9803
Step  2 of 10 [  1 trees;   7 leaves] CV score:         1.0311       1.0443       1.1172       1.0487
Step  3 of 10 [  1 trees;   7 leaves] CV score:         1.0567       1.0484       0.9617       1.0197
Step  4 of 10 [  1 trees;   7 leaves] CV score:         1.0504       1.0489       1.0543       1.0283
Step  5 of 10 [  1 trees;   7 leaves] CV score:         1.0581       1.0508       0.9767       1.0180
Step  6 of 10 [  1 trees;   7 leaves] CV score:         1.0477       1.0502       1.0488       1.0232
Step  7 of 10 [  1 trees;   7 leaves] CV score:         1.0460       1.0496       1.0928       1.0331
Step  8 of 10 [  1 trees;   7 leaves] CV score:         1.0151       1.0453       1.2866       1.0648
Step  9 of 10 [  1 trees;   7 leaves] CV score:         1.0586       1.0468       0.9694       1.0542
Step 10 of 10 [  1 trees;   7 leaves] CV score:         1.0308       1.0452       1.1978       1.0685
  
The number of trees in these models is   2
  
The size for this model is smaller than  the number of trees you requested. 
( 1 versus  2)
To save CPU time, we will skip this run.
On to the next model...
  
The model size is   2
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  2 trees;   2 leaves] CV score:         1.1296       1.1296       1.0298       1.0298
Step  2 of 10 [  2 trees;   2 leaves] CV score:         1.1060       1.1178       1.2537       1.1418
Step  3 of 10 [  2 trees;   2 leaves] CV score:         1.1273       1.1210       1.0512       1.1116
Step  4 of 10 [  2 trees;   2 leaves] CV score:         1.1262       1.1223       1.0670       1.1004
Step  5 of 10 [  2 trees;   2 leaves] CV score:         1.1236       1.1225       1.0968       1.0997
Step  6 of 10 [  2 trees;   2 leaves] CV score:         1.1288       1.1236       1.0356       1.0890
Step  7 of 10 [  2 trees;   2 leaves] CV score:         1.1103       1.1217       1.2191       1.1076
Step  8 of 10 [  2 trees;   2 leaves] CV score:         1.0842       1.1170       1.4212       1.1468
Step  9 of 10 [  2 trees;   2 leaves] CV score:         1.1209       1.1174       1.1210       1.1439
Step 10 of 10 [  2 trees;   2 leaves] CV score:         1.1095       1.1166       1.2228       1.1518
  
The model size is   3
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  2 trees;   3 leaves] CV score:         1.0499       1.0499       0.9913       0.9913
Step  2 of 10 [  2 trees;   3 leaves] CV score:         1.0276       1.0388       1.1200       1.0556
Step  3 of 10 [  2 trees;   3 leaves] CV score:         1.0425       1.0400       0.9795       1.0303
Step  4 of 10 [  2 trees;   3 leaves] CV score:         1.0341       1.0385       1.0673       1.0395
Step  5 of 10 [  2 trees;   3 leaves] CV score:         1.0391       1.0386       1.0186       1.0353
Step  6 of 10 [  2 trees;   3 leaves] CV score:         1.0393       1.0388       1.0091       1.0310
Step  7 of 10 [  2 trees;   3 leaves] CV score:         1.1079       1.0486       1.2683       1.0649
Step  8 of 10 [  2 trees;   3 leaves] CV score:         1.0067       1.0434       1.2888       1.0929
Step  9 of 10 [  2 trees;   3 leaves] CV score:         1.0597       1.0452       0.9818       1.0805
Step 10 of 10 [  2 trees;   3 leaves] CV score:         1.0262       1.0433       1.1437       1.0868
  
The model size is   4
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  2 trees;   4 leaves] CV score:         0.9907       0.9907       1.0043       1.0043
Step  2 of 10 [  2 trees;   4 leaves] CV score:         1.0276       1.0091       1.1200       1.0621
Step  3 of 10 [  2 trees;   4 leaves] CV score:         0.9964       1.0049       0.9449       1.0231
Step  4 of 10 [  2 trees;   4 leaves] CV score:         0.9926       1.0018       0.9895       1.0147
Step  5 of 10 [  2 trees;   4 leaves] CV score:         0.9962       1.0007       0.9474       1.0012
Step  6 of 10 [  2 trees;   4 leaves] CV score:         1.0757       1.0132       0.9496       0.9926
Step  7 of 10 [  2 trees;   4 leaves] CV score:         0.9827       1.0088       1.0751       1.0044
Step  8 of 10 [  2 trees;   4 leaves] CV score:         1.0000       1.0077       1.2857       1.0396
Step  9 of 10 [  2 trees;   4 leaves] CV score:         1.0514       1.0126       0.9752       1.0324
Step 10 of 10 [  2 trees;   4 leaves] CV score:         0.9884       1.0102       1.0298       1.0321
  
The model size is   5
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  2 trees;   5 leaves] CV score:         1.0681       1.0681       1.0846       1.0846
Step  2 of 10 [  2 trees;   5 leaves] CV score:         0.9766       1.0224       1.0657       1.0752
Step  3 of 10 [  2 trees;   5 leaves] CV score:         0.9929       1.0126       0.9104       1.0202
Step  4 of 10 [  2 trees;   5 leaves] CV score:         1.0723       1.0275       0.9886       1.0123
Step  5 of 10 [  2 trees;   5 leaves] CV score:         0.9929       1.0206       0.9690       1.0037
Step  6 of 10 [  2 trees;   5 leaves] CV score:         0.9889       1.0153       0.9509       0.9949
Step  7 of 10 [  2 trees;   5 leaves] CV score:         1.0424       1.0192       1.2026       1.0246
Step  8 of 10 [  2 trees;   5 leaves] CV score:         0.9627       1.0121       1.1846       1.0446
Step  9 of 10 [  2 trees;   5 leaves] CV score:         1.1023       1.0221       1.1901       1.0607
Step 10 of 10 [  2 trees;   5 leaves] CV score:         0.9875       1.0187       1.0629       1.0610
  
The model size is   6
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  2 trees;   6 leaves] CV score:         1.0956       1.0956       1.1429       1.1429
Step  2 of 10 [  2 trees;   6 leaves] CV score:         0.9766       1.0361       1.0657       1.1043
Step  3 of 10 [  2 trees;   6 leaves] CV score:         0.9925       1.0216       0.9434       1.0507
Step  4 of 10 [  2 trees;   6 leaves] CV score:         1.0234       1.0220       1.0913       1.0608
Step  5 of 10 [  2 trees;   6 leaves] CV score:         0.9861       1.0149       0.9456       1.0378
Step  6 of 10 [  2 trees;   6 leaves] CV score:         0.9958       1.0117       0.9498       1.0231
Step  7 of 10 [  2 trees;   6 leaves] CV score:         1.0381       1.0154       1.1159       1.0364
Step  8 of 10 [  2 trees;   6 leaves] CV score:         1.0647       1.0216       1.4640       1.0898
Step  9 of 10 [  2 trees;   6 leaves] CV score:         1.0463       1.0244       0.9614       1.0756
Step 10 of 10 [  2 trees;   6 leaves] CV score:         0.9884       1.0208       1.0298       1.0710
  
The model size is   7
                                                 training-now training-ave     test-now     test-ave
Step  1 of 10 [  2 trees;   7 leaves] CV score:         1.0543       1.0543       0.9949       0.9949
Step  2 of 10 [  2 trees;   7 leaves] CV score:         0.9788       1.0166       1.1801       1.0875
Step  3 of 10 [  2 trees;   7 leaves] CV score:         1.0524       1.0285       1.0262       1.0671
Step  4 of 10 [  2 trees;   7 leaves] CV score:         0.9861       1.0179       1.0052       1.0516
Step  5 of 10 [  2 trees;   7 leaves] CV score:         0.9901       1.0124       1.0144       1.0442
Step  6 of 10 [  2 trees;   7 leaves] CV score:         1.0043       1.0110       0.9689       1.0316
Step  7 of 10 [  2 trees;   7 leaves] CV score:         0.9878       1.0077       1.1065       1.0423
Step  8 of 10 [  2 trees;   7 leaves] CV score:         0.9908       1.0056       1.2785       1.0718
Step  9 of 10 [  2 trees;   7 leaves] CV score:         1.0379       1.0092       0.9994       1.0638
Step 10 of 10 [  2 trees;   7 leaves] CV score:         0.9792       1.0062       1.0488       1.0623
> plot(fit3)
> # use logreg.savefit3 for the results with 25000 iterations
> plot(logreg.savefit3)
> # 4 leaves, 2 trees should top
> # null model test
> fit4 <- logreg(resp = logreg.testdat[,1], bin=logreg.testdat[, 2:21], type = 2,
+                select = 4, ntrees = 2, anneal.control = myanneal2)
  
The model of size 0 has score        1.4941
  
The best model has score          0.9814
Permutation number    1  out of   25  has score         1.4512
Permutation number    2  out of   25  has score         1.4561
Permutation number    3  out of   25  has score         1.4692
Permutation number    4  out of   25  has score         1.4370
Permutation number    5  out of   25  has score         1.4559
Permutation number    6  out of   25  has score         1.4676
Permutation number    7  out of   25  has score         1.4667
Permutation number    8  out of   25  has score         1.4422
Permutation number    9  out of   25  has score         1.4550
Permutation number   10  out of   25  has score         1.4537
Permutation number   11  out of   25  has score         1.4622
Permutation number   12  out of   25  has score         1.4542
Permutation number   13  out of   25  has score         1.4520
Permutation number   14  out of   25  has score         1.4310
Permutation number   15  out of   25  has score         1.4390
Permutation number   16  out of   25  has score         1.4716
Permutation number   17  out of   25  has score         1.4570
Permutation number   18  out of   25  has score         1.4413
Permutation number   19  out of   25  has score         1.4617
Permutation number   20  out of   25  has score         1.4614
Permutation number   21  out of   25  has score         1.4607
Permutation number   22  out of   25  has score         1.4659
Permutation number   23  out of   25  has score         1.4563
Permutation number   24  out of   25  has score         1.4721
Permutation number   25  out of   25  has score         1.4625
> # equivalent
> fit4 <- logreg(select = 4, anneal.control = myanneal2, oldfit = fit1)
  
The model of size 0 has score        1.4941
  
The best model has score          0.9782
Permutation number    1  out of   25  has score         1.4576
Permutation number    2  out of   25  has score         1.4690
Permutation number    3  out of   25  has score         1.4652
Permutation number    4  out of   25  has score         1.4716
Permutation number    5  out of   25  has score         1.4498
Permutation number    6  out of   25  has score         1.4533
Permutation number    7  out of   25  has score         1.4605
Permutation number    8  out of   25  has score         1.4428
Permutation number    9  out of   25  has score         1.4692
Permutation number   10  out of   25  has score         1.4425
Permutation number   11  out of   25  has score         1.4620
Permutation number   12  out of   25  has score         1.4597
Permutation number   13  out of   25  has score         1.4510
Permutation number   14  out of   25  has score         1.4265
Permutation number   15  out of   25  has score         1.4632
Permutation number   16  out of   25  has score         1.4443
Permutation number   17  out of   25  has score         1.4589
Permutation number   18  out of   25  has score         1.4434
Permutation number   19  out of   25  has score         1.4402
Permutation number   20  out of   25  has score         1.4586
Permutation number   21  out of   25  has score         1.4614
Permutation number   22  out of   25  has score         1.4408
Permutation number   23  out of   25  has score         1.4464
Permutation number   24  out of   25  has score         1.4653
Permutation number   25  out of   25  has score         1.4709
> plot(fit4)
> # use logreg.savefit4 for the results with 25000 iterations
> plot(logreg.savefit4)
> # A histogram of the 25 scores obtained from the permutation test. Also shown
> # are the scores for the best scoring model with one logic tree, and the null
> # model (no tree). Since the permutation scores are not even close to the score
> # of the best model with one tree (fit on the original data), there is overwhelming
> # evidence against the null hypothesis that there was no signal in the data. 
> fit5 <- logreg(resp = logreg.testdat[,1], bin=logreg.testdat[, 2:21], type = 2,
+                select = 5, ntrees = c(1,2), nleaves=c(1,7), anneal.control = myanneal2,
+                nrep = 10, oldfit = fit2)
  
The model of size 0 has score        1.4941
  
The number of trees in these models is   1
  
The model size is   1
Permutation number    1  out of   10  has score        1.130   model size    with  1 tree(s)
Permutation number    2  out of   10  has score        1.137   model size    with  1 tree(s)
Permutation number    3  out of   10  has score        1.133   model size    with  1 tree(s)
Permutation number    4  out of   10  has score        1.138   model size    with  1 tree(s)
Permutation number    5  out of   10  has score        1.132   model size    with  1 tree(s)
Permutation number    6  out of   10  has score        1.128   model size    with  1 tree(s)
Permutation number    7  out of   10  has score        1.135   model size    with  1 tree(s)
Permutation number    8  out of   10  has score        1.136   model size    with  1 tree(s)
Permutation number    9  out of   10  has score        1.124   model size    with  1 tree(s)
Permutation number   10  out of   10  has score        1.135   model size    with  1 tree(s)
  
The model size is   2
Permutation number    1  out of   10  has score        1.041   model size    with  1 tree(s)
Permutation number    2  out of   10  has score        1.034   model size    with  1 tree(s)
Permutation number    3  out of   10  has score        1.029   model size    with  1 tree(s)
Permutation number    4  out of   10  has score        1.031   model size    with  1 tree(s)
Permutation number    5  out of   10  has score        1.033   model size    with  1 tree(s)
Permutation number    6  out of   10  has score        1.036   model size    with  1 tree(s)
Permutation number    7  out of   10  has score        1.037   model size    with  1 tree(s)
Permutation number    8  out of   10  has score        1.040   model size    with  1 tree(s)
Permutation number    9  out of   10  has score        1.042   model size    with  1 tree(s)
Permutation number   10  out of   10  has score        1.043   model size    with  1 tree(s)
  
The model size is   3
Permutation number    1  out of   10  has score        1.032   model size    with  1 tree(s)
Permutation number    2  out of   10  has score        1.039   model size    with  1 tree(s)
Permutation number    3  out of   10  has score        1.025   model size    with  1 tree(s)
Permutation number    4  out of   10  has score        1.030   model size    with  1 tree(s)
Permutation number    5  out of   10  has score        1.045   model size    with  1 tree(s)
Permutation number    6  out of   10  has score        1.036   model size    with  1 tree(s)
Permutation number    7  out of   10  has score        1.028   model size    with  1 tree(s)
Permutation number    8  out of   10  has score        1.027   model size    with  1 tree(s)
Permutation number    9  out of   10  has score        1.030   model size    with  1 tree(s)
Permutation number   10  out of   10  has score        1.031   model size    with  1 tree(s)
  
The model size is   4
Permutation number    1  out of   10  has score        1.032   model size    with  1 tree(s)
Permutation number    2  out of   10  has score        1.036   model size    with  1 tree(s)
Permutation number    3  out of   10  has score        1.035   model size    with  1 tree(s)
Permutation number    4  out of   10  has score        1.042   model size    with  1 tree(s)
Permutation number    5  out of   10  has score        1.033   model size    with  1 tree(s)
Permutation number    6  out of   10  has score        1.040   model size    with  1 tree(s)
Permutation number    7  out of   10  has score        1.099   model size    with  1 tree(s)
Permutation number    8  out of   10  has score        1.046   model size    with  1 tree(s)
Permutation number    9  out of   10  has score        1.035   model size    with  1 tree(s)
Permutation number   10  out of   10  has score        1.040   model size    with  1 tree(s)
  
The model size is   5
Permutation number    1  out of   10  has score        1.066   model size    with  1 tree(s)
Permutation number    2  out of   10  has score        1.039   model size    with  1 tree(s)
Permutation number    3  out of   10  has score        1.041   model size    with  1 tree(s)
Permutation number    4  out of   10  has score        1.039   model size    with  1 tree(s)
Permutation number    5  out of   10  has score        1.027   model size    with  1 tree(s)
Permutation number    6  out of   10  has score        1.057   model size    with  1 tree(s)
Permutation number    7  out of   10  has score        1.037   model size    with  1 tree(s)
Permutation number    8  out of   10  has score        1.037   model size    with  1 tree(s)
Permutation number    9  out of   10  has score        1.053   model size    with  1 tree(s)
Permutation number   10  out of   10  has score        1.035   model size    with  1 tree(s)
  
The model size is   6
Permutation number    1  out of   10  has score        1.038   model size    with  1 tree(s)
Permutation number    2  out of   10  has score        1.037   model size    with  1 tree(s)
Permutation number    3  out of   10  has score        1.037   model size    with  1 tree(s)
Permutation number    4  out of   10  has score        1.033   model size    with  1 tree(s)
Permutation number    5  out of   10  has score        1.031   model size    with  1 tree(s)
Permutation number    6  out of   10  has score        1.042   model size    with  1 tree(s)
Permutation number    7  out of   10  has score        1.042   model size    with  1 tree(s)
Permutation number    8  out of   10  has score        1.035   model size    with  1 tree(s)
Permutation number    9  out of   10  has score        1.039   model size    with  1 tree(s)
Permutation number   10  out of   10  has score        1.043   model size    with  1 tree(s)
  
The model size is   7
Permutation number    1  out of   10  has score        1.028   model size    with  1 tree(s)
Permutation number    2  out of   10  has score        1.049   model size    with  1 tree(s)
Permutation number    3  out of   10  has score        1.016   model size    with  1 tree(s)
Permutation number    4  out of   10  has score        1.037   model size    with  1 tree(s)
Permutation number    5  out of   10  has score        1.041   model size    with  1 tree(s)
Permutation number    6  out of   10  has score        1.045   model size    with  1 tree(s)
Permutation number    7  out of   10  has score        1.079   model size    with  1 tree(s)
Permutation number    8  out of   10  has score        1.039   model size    with  1 tree(s)
Permutation number    9  out of   10  has score        1.026   model size    with  1 tree(s)
Permutation number   10  out of   10  has score        1.038   model size    with  1 tree(s)
  
The number of trees in these models is   2
  
The size for this model is smaller than  the number of trees you requested. 
( 1 versus  2)
To save CPU time, we will skip this run.
On to the next model...
  
The model size is   2
Permutation number    1  out of   10  has score        1.034   model size    with  2 tree(s)
Permutation number    2  out of   10  has score        1.035   model size    with  2 tree(s)
Permutation number    3  out of   10  has score        1.035   model size    with  2 tree(s)
Permutation number    4  out of   10  has score        1.027   model size    with  2 tree(s)
Permutation number    5  out of   10  has score        1.040   model size    with  2 tree(s)
Permutation number    6  out of   10  has score        1.033   model size    with  2 tree(s)
Permutation number    7  out of   10  has score        1.026   model size    with  2 tree(s)
Permutation number    8  out of   10  has score        1.029   model size    with  2 tree(s)
Permutation number    9  out of   10  has score        1.047   model size    with  2 tree(s)
Permutation number   10  out of   10  has score        1.032   model size    with  2 tree(s)
  
The model size is   3
Permutation number    1  out of   10  has score        1.030   model size    with  2 tree(s)
Permutation number    2  out of   10  has score        1.024   model size    with  2 tree(s)
Permutation number    3  out of   10  has score        1.045   model size    with  2 tree(s)
Permutation number    4  out of   10  has score        1.033   model size    with  2 tree(s)
Permutation number    5  out of   10  has score        1.019   model size    with  2 tree(s)
Permutation number    6  out of   10  has score        1.025   model size    with  2 tree(s)
Permutation number    7  out of   10  has score        1.028   model size    with  2 tree(s)
Permutation number    8  out of   10  has score        1.026   model size    with  2 tree(s)
Permutation number    9  out of   10  has score        1.033   model size    with  2 tree(s)
Permutation number   10  out of   10  has score        1.030   model size    with  2 tree(s)
  
The model size is   4
Permutation number    1  out of   10  has score        1.029   model size    with  2 tree(s)
Permutation number    2  out of   10  has score        1.033   model size    with  2 tree(s)
Permutation number    3  out of   10  has score        1.030   model size    with  2 tree(s)
Permutation number    4  out of   10  has score        1.019   model size    with  2 tree(s)
Permutation number    5  out of   10  has score        1.032   model size    with  2 tree(s)
Permutation number    6  out of   10  has score        1.031   model size    with  2 tree(s)
Permutation number    7  out of   10  has score        0.999   model size    with  2 tree(s)
Permutation number    8  out of   10  has score        1.038   model size    with  2 tree(s)
Permutation number    9  out of   10  has score        1.039   model size    with  2 tree(s)
Permutation number   10  out of   10  has score        1.035   model size    with  2 tree(s)
  
The model size is   5
Permutation number    1  out of   10  has score        1.074   model size    with  2 tree(s)
Permutation number    2  out of   10  has score        1.084   model size    with  2 tree(s)
Permutation number    3  out of   10  has score        1.101   model size    with  2 tree(s)
Permutation number    4  out of   10  has score        1.086   model size    with  2 tree(s)
Permutation number    5  out of   10  has score        1.122   model size    with  2 tree(s)
Permutation number    6  out of   10  has score        1.095   model size    with  2 tree(s)
Permutation number    7  out of   10  has score        1.129   model size    with  2 tree(s)
Permutation number    8  out of   10  has score        1.109   model size    with  2 tree(s)
Permutation number    9  out of   10  has score        1.079   model size    with  2 tree(s)
Permutation number   10  out of   10  has score        1.092   model size    with  2 tree(s)
  
The model size is   6
Permutation number    1  out of   10  has score        0.974   model size    with  2 tree(s)
Permutation number    2  out of   10  has score        1.030   model size    with  2 tree(s)
Permutation number    3  out of   10  has score        0.971   model size    with  2 tree(s)
Permutation number    4  out of   10  has score        0.984   model size    with  2 tree(s)
Permutation number    5  out of   10  has score        0.978   model size    with  2 tree(s)
Permutation number    6  out of   10  has score        0.975   model size    with  2 tree(s)
Permutation number    7  out of   10  has score        0.982   model size    with  2 tree(s)
Permutation number    8  out of   10  has score        1.044   model size    with  2 tree(s)
Permutation number    9  out of   10  has score        0.980   model size    with  2 tree(s)
Permutation number   10  out of   10  has score        0.982   model size    with  2 tree(s)
  
The model size is   7
Permutation number    1  out of   10  has score        1.007   model size    with  2 tree(s)
Permutation number    2  out of   10  has score        1.066   model size    with  2 tree(s)
Permutation number    3  out of   10  has score        1.014   model size    with  2 tree(s)
Permutation number    4  out of   10  has score        1.037   model size    with  2 tree(s)
Permutation number    5  out of   10  has score        1.026   model size    with  2 tree(s)
Permutation number    6  out of   10  has score        1.022   model size    with  2 tree(s)
Permutation number    7  out of   10  has score        1.016   model size    with  2 tree(s)
Permutation number    8  out of   10  has score        1.018   model size    with  2 tree(s)
Permutation number    9  out of   10  has score        1.024   model size    with  2 tree(s)
Permutation number   10  out of   10  has score        0.999   model size    with  2 tree(s)
> # equivalent
> fit5 <- logreg(select = 5, nrep = 10, oldfit = fit2)
  
The model of size 0 has score        1.4941
  
The number of trees in these models is   1
  
The model size is   1
Permutation number    1  out of   10  has score        1.136   model size    with  1 tree(s)
Permutation number    2  out of   10  has score        1.130   model size    with  1 tree(s)
Permutation number    3  out of   10  has score        1.138   model size    with  1 tree(s)
Permutation number    4  out of   10  has score        1.134   model size    with  1 tree(s)
Permutation number    5  out of   10  has score        1.135   model size    with  1 tree(s)
Permutation number    6  out of   10  has score        1.131   model size    with  1 tree(s)
Permutation number    7  out of   10  has score        1.133   model size    with  1 tree(s)
Permutation number    8  out of   10  has score        1.136   model size    with  1 tree(s)
Permutation number    9  out of   10  has score        1.134   model size    with  1 tree(s)
Permutation number   10  out of   10  has score        1.124   model size    with  1 tree(s)
  
The model size is   2
Permutation number    1  out of   10  has score        1.030   model size    with  1 tree(s)
Permutation number    2  out of   10  has score        1.029   model size    with  1 tree(s)
Permutation number    3  out of   10  has score        1.030   model size    with  1 tree(s)
Permutation number    4  out of   10  has score        1.039   model size    with  1 tree(s)
Permutation number    5  out of   10  has score        1.040   model size    with  1 tree(s)
Permutation number    6  out of   10  has score        1.039   model size    with  1 tree(s)
Permutation number    7  out of   10  has score        1.027   model size    with  1 tree(s)
Permutation number    8  out of   10  has score        1.036   model size    with  1 tree(s)
Permutation number    9  out of   10  has score        1.034   model size    with  1 tree(s)
Permutation number   10  out of   10  has score        1.035   model size    with  1 tree(s)
  
The model size is   3
Permutation number    1  out of   10  has score        1.040   model size    with  1 tree(s)
Permutation number    2  out of   10  has score        1.026   model size    with  1 tree(s)
Permutation number    3  out of   10  has score        1.030   model size    with  1 tree(s)
Permutation number    4  out of   10  has score        1.033   model size    with  1 tree(s)
Permutation number    5  out of   10  has score        1.034   model size    with  1 tree(s)
Permutation number    6  out of   10  has score        1.034   model size    with  1 tree(s)
Permutation number    7  out of   10  has score        1.025   model size    with  1 tree(s)
Permutation number    8  out of   10  has score        1.019   model size    with  1 tree(s)
Permutation number    9  out of   10  has score        1.033   model size    with  1 tree(s)
Permutation number   10  out of   10  has score        1.046   model size    with  1 tree(s)
  
The model size is   4
Permutation number    1  out of   10  has score        1.037   model size    with  1 tree(s)
Permutation number    2  out of   10  has score        1.030   model size    with  1 tree(s)
Permutation number    3  out of   10  has score        1.034   model size    with  1 tree(s)
Permutation number    4  out of   10  has score        1.029   model size    with  1 tree(s)
Permutation number    5  out of   10  has score        1.030   model size    with  1 tree(s)
Permutation number    6  out of   10  has score        1.031   model size    with  1 tree(s)
Permutation number    7  out of   10  has score        1.032   model size    with  1 tree(s)
Permutation number    8  out of   10  has score        1.025   model size    with  1 tree(s)
Permutation number    9  out of   10  has score        1.045   model size    with  1 tree(s)
Permutation number   10  out of   10  has score        1.032   model size    with  1 tree(s)
  
The model size is   5
Permutation number    1  out of   10  has score        1.045   model size    with  1 tree(s)
Permutation number    2  out of   10  has score        1.038   model size    with  1 tree(s)
Permutation number    3  out of   10  has score        1.047   model size    with  1 tree(s)
Permutation number    4  out of   10  has score        1.054   model size    with  1 tree(s)
Permutation number    5  out of   10  has score        1.034   model size    with  1 tree(s)
Permutation number    6  out of   10  has score        1.046   model size    with  1 tree(s)
Permutation number    7  out of   10  has score        1.044   model size    with  1 tree(s)
Permutation number    8  out of   10  has score        1.048   model size    with  1 tree(s)
Permutation number    9  out of   10  has score        1.028   model size    with  1 tree(s)
Permutation number   10  out of   10  has score        1.031   model size    with  1 tree(s)
  
The model size is   6
Permutation number    1  out of   10  has score        1.036   model size    with  1 tree(s)
Permutation number    2  out of   10  has score        1.039   model size    with  1 tree(s)
Permutation number    3  out of   10  has score        1.040   model size    with  1 tree(s)
Permutation number    4  out of   10  has score        1.034   model size    with  1 tree(s)
Permutation number    5  out of   10  has score        1.038   model size    with  1 tree(s)
Permutation number    6  out of   10  has score        1.034   model size    with  1 tree(s)
Permutation number    7  out of   10  has score        1.043   model size    with  1 tree(s)
Permutation number    8  out of   10  has score        1.037   model size    with  1 tree(s)
Permutation number    9  out of   10  has score        1.033   model size    with  1 tree(s)
Permutation number   10  out of   10  has score        1.076   model size    with  1 tree(s)
  
The model size is   7
Permutation number    1  out of   10  has score        1.040   model size    with  1 tree(s)
Permutation number    2  out of   10  has score        1.042   model size    with  1 tree(s)
Permutation number    3  out of   10  has score        1.047   model size    with  1 tree(s)
Permutation number    4  out of   10  has score        1.029   model size    with  1 tree(s)
Permutation number    5  out of   10  has score        1.038   model size    with  1 tree(s)
Permutation number    6  out of   10  has score        1.046   model size    with  1 tree(s)
Permutation number    7  out of   10  has score        1.042   model size    with  1 tree(s)
Permutation number    8  out of   10  has score        1.042   model size    with  1 tree(s)
Permutation number    9  out of   10  has score        1.047   model size    with  1 tree(s)
Permutation number   10  out of   10  has score        1.037   model size    with  1 tree(s)
  
The number of trees in these models is   2
  
The size for this model is smaller than  the number of trees you requested. 
( 1 versus  2)
To save CPU time, we will skip this run.
On to the next model...
  
The model size is   2
Permutation number    1  out of   10  has score        1.039   model size    with  2 tree(s)
Permutation number    2  out of   10  has score        1.042   model size    with  2 tree(s)
Permutation number    3  out of   10  has score        1.045   model size    with  2 tree(s)
Permutation number    4  out of   10  has score        1.116   model size    with  2 tree(s)
Permutation number    5  out of   10  has score        1.029   model size    with  2 tree(s)
Permutation number    6  out of   10  has score        1.040   model size    with  2 tree(s)
Permutation number    7  out of   10  has score        1.038   model size    with  2 tree(s)
Permutation number    8  out of   10  has score        1.036   model size    with  2 tree(s)
Permutation number    9  out of   10  has score        1.040   model size    with  2 tree(s)
Permutation number   10  out of   10  has score        1.038   model size    with  2 tree(s)
  
The model size is   3
Permutation number    1  out of   10  has score        1.028   model size    with  2 tree(s)
Permutation number    2  out of   10  has score        1.033   model size    with  2 tree(s)
Permutation number    3  out of   10  has score        1.028   model size    with  2 tree(s)
Permutation number    4  out of   10  has score        1.019   model size    with  2 tree(s)
Permutation number    5  out of   10  has score        1.018   model size    with  2 tree(s)
Permutation number    6  out of   10  has score        1.036   model size    with  2 tree(s)
Permutation number    7  out of   10  has score        1.019   model size    with  2 tree(s)
Permutation number    8  out of   10  has score        1.033   model size    with  2 tree(s)
Permutation number    9  out of   10  has score        1.030   model size    with  2 tree(s)
Permutation number   10  out of   10  has score        1.033   model size    with  2 tree(s)
  
The model size is   4
Permutation number    1  out of   10  has score        1.046   model size    with  2 tree(s)
Permutation number    2  out of   10  has score        1.022   model size    with  2 tree(s)
Permutation number    3  out of   10  has score        1.027   model size    with  2 tree(s)
Permutation number    4  out of   10  has score        1.044   model size    with  2 tree(s)
Permutation number    5  out of   10  has score        1.047   model size    with  2 tree(s)
Permutation number    6  out of   10  has score        1.027   model size    with  2 tree(s)
Permutation number    7  out of   10  has score        1.038   model size    with  2 tree(s)
Permutation number    8  out of   10  has score        1.035   model size    with  2 tree(s)
Permutation number    9  out of   10  has score        1.041   model size    with  2 tree(s)
Permutation number   10  out of   10  has score        1.041   model size    with  2 tree(s)
  
The model size is   5
Permutation number    1  out of   10  has score        1.098   model size    with  2 tree(s)
Permutation number    2  out of   10  has score        1.095   model size    with  2 tree(s)
Permutation number    3  out of   10  has score        1.086   model size    with  2 tree(s)
Permutation number    4  out of   10  has score        1.081   model size    with  2 tree(s)
Permutation number    5  out of   10  has score        1.131   model size    with  2 tree(s)
Permutation number    6  out of   10  has score        1.087   model size    with  2 tree(s)
Permutation number    7  out of   10  has score        1.110   model size    with  2 tree(s)
Permutation number    8  out of   10  has score        1.087   model size    with  2 tree(s)
Permutation number    9  out of   10  has score        1.118   model size    with  2 tree(s)
Permutation number   10  out of   10  has score        1.091   model size    with  2 tree(s)
  
The model size is   6
Permutation number    1  out of   10  has score        1.046   model size    with  2 tree(s)
Permutation number    2  out of   10  has score        0.982   model size    with  2 tree(s)
Permutation number    3  out of   10  has score        0.979   model size    with  2 tree(s)
Permutation number    4  out of   10  has score        0.980   model size    with  2 tree(s)
Permutation number    5  out of   10  has score        0.983   model size    with  2 tree(s)
Permutation number    6  out of   10  has score        0.987   model size    with  2 tree(s)
Permutation number    7  out of   10  has score        0.976   model size    with  2 tree(s)
Permutation number    8  out of   10  has score        0.980   model size    with  2 tree(s)
Permutation number    9  out of   10  has score        1.036   model size    with  2 tree(s)
Permutation number   10  out of   10  has score        0.966   model size    with  2 tree(s)
  
The model size is   7
Permutation number    1  out of   10  has score        1.009   model size    with  2 tree(s)
Permutation number    2  out of   10  has score        1.028   model size    with  2 tree(s)
Permutation number    3  out of   10  has score        1.031   model size    with  2 tree(s)
Permutation number    4  out of   10  has score        1.016   model size    with  2 tree(s)
Permutation number    5  out of   10  has score        1.031   model size    with  2 tree(s)
Permutation number    6  out of   10  has score        1.047   model size    with  2 tree(s)
Permutation number    7  out of   10  has score        1.010   model size    with  2 tree(s)
Permutation number    8  out of   10  has score        1.018   model size    with  2 tree(s)
Permutation number    9  out of   10  has score        0.997   model size    with  2 tree(s)
Permutation number   10  out of   10  has score        1.022   model size    with  2 tree(s)
> plot(fit5)
> # use logreg.savefit5 for the results with 25000 iterations and 25 permutations
> plot(logreg.savefit5)
> # The permutation scores improve until we condition on a model with two trees and
> # four leaves, and then do not change very much anymore. This indicates that the
> # best model has indeed four leaves.
> #
> # greedy selection
> fit6 <- logreg(select = 6, ntrees = 2, nleaves =c(1,12), oldfit = fit1)
Model   0 has a score of       1.4941
Model   1 has a score of       1.1500
Model   2 has a score of       1.0481
Model   3 has a score of       1.0333
Model   4 has a score of       0.9885
Model   5 has a score of       0.9823
Model   6 has a score of       0.9814
Model   7 has a score of       0.9803
No further improvement possible 
> plot(fit6)
empty tree 
empty tree 
empty tree 
empty tree 
> # use logreg.savefit6 for the results with 25000 iterations
> plot(logreg.savefit6)
empty tree 
empty tree 
empty tree 
empty tree 
> #
> # Monte Carlo Logic Regression
> fit7 <- logreg(select = 7, oldfit = fit1, mc.control=
+                logreg.mc.control(nburn=1000, niter=10000, hyperpars=log(2)))
  
iter(10k)  current scr    best score        acc / rej /sing    current parameters
   0.000        0.7470        0.7470     0(  0)     0     0    2.886    0.000    0.000
   0.010       16.8887        0.7470    30(  1)    69     0    2.949   -0.152   -1.991
   0.020       11.1104        0.7470    35(  1)    64     0    3.032   -0.588   -1.991
   0.030        0.7470        0.7470    33(  1)    66     0    2.886   -1.988   -1.991
   0.040        0.7470        0.7470    16(  0)    84     0    2.886   -1.988   -1.991
   0.050        5.8209        0.7470    36(  0)    64     0    2.762    0.226    2.813
   0.060       38.9818        0.7470    53(  0)    47     0    2.985   -0.143   -0.122
   0.070       16.8892        0.7470    65(  3)    32     0    2.916   -0.120   -1.991
   0.080        0.7470        0.7470    20(  0)    80     0    2.886   -1.988   -1.991
   0.090        0.7470        0.7470    25(  1)    74     0    2.886   -1.988   -1.991
   0.100        5.8220        0.7470    20(  1)    79     0    2.856    0.227   -1.991
   0.110        5.8221        0.7470    25(  0)    75     0    2.823    0.147   -1.991
   0.120        0.7470        0.7470    27(  1)    72     0    2.886   -1.988   -1.991
   0.130        0.7470        0.7470    11(  1)    88     0    2.886   -1.988   -1.991
   0.140        0.7470        0.7470    29(  1)    70     0    2.886   -1.988   -1.991
   0.150        0.7470        0.7470    11(  0)    89     0    2.886   -1.988   -1.991
   0.160        0.7470        0.7470    42(  1)    57     0    2.886   -1.988   -1.991
   0.170       11.1212        0.7470    30(  0)    70     0    3.082   -0.130   -0.120
   0.180        0.7470        0.7470    36(  0)    64     0    2.886   -1.988   -1.991
   0.190        0.7470        0.7470     3(  0)    97     0    2.886   -1.988   -1.991
   0.200        0.7470        0.7470     8(  0)    92     0    2.886   -1.988   -1.991
   0.210        0.7470        0.7470    26(  0)    74     0    2.886   -1.988   -1.991
   0.220       22.8785        0.7470    34(  1)    65     0    2.933   -0.370   -1.991
   0.230        5.8222        0.7470    30(  3)    67     0    2.728    0.186   -1.991
   0.240        5.8227        0.7470    36(  2)    62     0    2.916   -0.083   -1.991
   0.250        5.8227        0.7470    41(  1)    58     0    2.844    0.078   -1.991
   0.260        5.8228        0.7470    20(  3)    77     0    2.791    0.105   -1.991
   0.270        0.7470        0.7470    31(  2)    67     0    2.886   -1.988   -1.991
   0.280       16.8896        0.7470    23(  0)    77     0    2.882    0.005   -1.991
   0.290        5.8225        0.7470    22(  0)    78     0    2.857    0.130   -1.991
   0.300        5.8220        0.7470    53(  2)    45     0    3.082   -0.227   -1.991
   0.310        0.7470        0.7470    22(  0)    78     0    2.886   -1.988   -1.991
   0.320        0.7470        0.7470    12(  0)    88     0    2.886   -1.988   -1.991
   0.330        0.7470        0.7470     4(  0)    96     0    2.886   -1.988   -1.991
   0.340       33.7251        0.7470    33(  3)    64     0    2.848    0.105   -0.068
   0.350        5.8061        0.7470    35(  2)    63     0    2.995   -1.023   -1.991
   0.360        0.7470        0.7470     8(  0)    92     0    2.886   -1.988   -1.991
   0.370       16.8889        0.7470    24(  1)    75     0    3.018   -0.164   -1.991
   0.380        0.7470        0.7470    13(  0)    87     0    2.886   -1.988   -1.991
   0.390        0.7470        0.7470    17(  0)    83     0    2.886   -1.988   -1.991
   0.400        0.7470        0.7470    18(  0)    82     0    2.886   -1.988   -1.991
   0.410        5.6502        0.7470    14(  1)    85     0    1.877    1.912   -1.991
   0.420        5.8204        0.7470    28(  1)    71     0    2.933   -0.370   -1.991
   0.430        5.8229        0.7470    18(  0)    82     0    2.943   -0.068   -1.991
   0.440        0.7470        0.7470    30(  2)    68     0    2.886   -1.988   -1.991
   0.450        0.7470        0.7470     0(  0)   100     0    2.886   -1.988   -1.991
   0.460       16.8893        0.7470     7(  0)    93     0    2.824    0.089   -1.991
   0.470        0.7470        0.7470    28(  0)    72     0    2.886   -1.988   -1.991
   0.480       11.1214        0.7470    11(  0)    89     0    2.834    0.102    0.085
   0.490        0.7470        0.7470    32(  2)    66     0    2.886   -1.988   -1.991
   0.500        0.7470        0.7470    29(  1)    70     0    2.886   -1.988   -1.991
   0.510        5.8061        0.7470     8(  0)    92     0    2.995   -1.023   -1.991
   0.520        0.7470        0.7470    34(  0)    66     0    2.886   -1.988   -1.991
   0.530        5.8229        0.7470    36(  0)    64     0    2.875    0.068   -1.991
   0.540       11.1211        0.7470    46(  1)    53     0    2.876    0.133   -1.991
   0.550       16.8893        0.7470    28(  0)    72     0    2.916   -0.083   -1.991
   0.560        0.7470        0.7470    32(  0)    68     0    2.886   -1.988   -1.991
   0.570        0.7470        0.7470    18(  3)    79     0    2.886   -1.988   -1.991
   0.580        0.7470        0.7470     5(  0)    95     0    2.886   -1.988   -1.991
   0.590        0.7470        0.7470     6(  1)    93     0    2.886   -1.988   -1.991
   0.600        0.7470        0.7470    23(  2)    75     0    2.886   -1.988   -1.991
   0.610        0.7470        0.7470     7(  0)    93     0    2.886   -1.988   -1.991
   0.620       11.1215        0.7470    30(  1)    69     0    2.818    0.101    0.049
   0.630        5.8202        0.7470    39(  1)    60     0    2.973   -0.279    2.813
   0.640        5.8229        0.7470    25(  0)    75     0    2.875    0.068   -1.991
   0.650        0.7470        0.7470    24(  2)    74     0    2.886   -1.988   -1.991
   0.660        5.8209        0.7470    23(  1)    76     0    2.987   -0.226    2.813
   0.670        0.7470        0.7470    28(  0)    72     0    2.886   -1.988   -1.991
   0.680        0.7470        0.7470    33(  0)    67     0    2.886   -1.988   -1.991
   0.690       11.1216        0.7470    32(  1)    67     0    2.813    0.086    0.093
   0.700        0.7470        0.7470    38(  0)    62     0    2.886   -1.988   -1.991
   0.710        0.7470        0.7470    26(  0)    74     0    2.886   -1.988   -1.991
   0.720       11.1179        0.7470    35(  2)    63     0    3.064   -0.291   -1.991
   0.730       11.1197        0.7470    45(  1)    54     0    3.115   -0.270   -1.991
   0.740        5.8227        0.7470    43(  2)    55     0    2.844    0.078   -1.991
   0.750        0.7470        0.7470    28(  1)    71     0    2.886   -1.988   -1.991
   0.760        0.7470        0.7470    25(  2)    73     0    2.886   -1.988   -1.991
   0.770        0.7470        0.7470    21(  0)    79     0    2.886   -1.988   -1.991
   0.780        0.7470        0.7470     6(  1)    93     0    2.886   -1.988   -1.991
   0.790        0.7470        0.7470    27(  0)    73     0    2.886   -1.988   -1.991
   0.800        5.8214        0.7470     8(  0)    92     0    2.841    0.248   -1.991
   0.810        0.7470        0.7470    21(  0)    79     0    2.886   -1.988   -1.991
   0.820        5.8226        0.7470    12(  0)    88     0    2.953   -0.103   -1.991
   0.830        5.8221        0.7470    29(  0)    71     0    2.823    0.147    2.813
   0.840       11.1190        0.7470    27(  0)    73     0    2.931   -0.345   -1.991
   0.850        5.8202        0.7470    50(  2)    48     0    2.973   -0.279   -1.991
   0.860        5.8225        0.7470    38(  0)    62     0    2.857    0.130   -1.991
   0.870        0.7470        0.7470    18(  1)    81     0    2.886   -1.988   -1.991
   0.880        5.8227        0.7470    30(  2)    68     0    2.844    0.078   -1.991
   0.890       11.1158        0.7470    26(  0)    74     0    3.073   -0.419   -0.085
   0.900        0.7470        0.7470    33(  1)    66     0    2.886   -1.988   -1.991
   0.910        5.8229        0.7470    30(  0)    70     0    2.849    0.055   -1.991
   0.920        0.7470        0.7470    19(  1)    80     0    2.886   -1.988   -1.991
   0.930       16.8888        0.7470    49(  2)    49     0    2.933    0.345   -0.106
   0.940        0.7470        0.7470    38(  2)    60     0    2.886   -1.988   -1.991
   0.950       11.1119        0.7470    42(  1)    57     0    1.837    1.102   -1.991
   0.960       11.1206        0.7470    41(  1)    58     0    2.813    0.131   -1.991
   0.970        5.8221        0.7470    19(  0)    81     0    2.970   -0.147   -1.991
   0.980        0.7470        0.7470    21(  0)    79     0    2.886   -1.988   -1.991
   0.990        0.7470        0.7470     7(  0)    93     0    2.886   -1.988   -1.991
   1.000        5.8202        0.7470     6(  0)    94     0    2.973   -0.279   -1.991
   1.010       11.1212        0.7470    49(  3)    48     0    2.924   -0.047   -1.991
   1.020       16.8894        0.7470    38(  1)    61     0    2.910   -0.065   -1.991
   1.030        0.7470        0.7470    26(  0)    74     0    2.886   -1.988   -1.991
   1.040       16.8893        0.7470    12(  0)    88     0    2.976   -0.111   -1.991
   1.050        0.7470        0.7470    21(  1)    78     0    2.886   -1.988   -1.991
   1.060        0.7470        0.7470    16(  0)    84     0    2.886   -1.988   -1.991
   1.070       10.9486        0.7470    20(  0)    80     0    3.737   -1.912    0.095
   1.080       16.8891        0.7470    28(  1)    71     0    2.947   -0.110   -1.991
   1.090        0.7470        0.7470    46(  3)    51     0    2.886   -1.988   -1.991
   1.100       22.8797        0.7470    40(  0)    60     0    2.931   -0.234   -1.991
> # we need many more iterations for reasonable results
> ## Not run: 
> ##D logreg.savefit7 <- logreg(select = 7, oldfit = fit1, mc.control=
> ##D                logreg.mc.control(nburn=1000, niter=100000, hyperpars=log(2)))
> ## End(Not run)
> #
> plot(fit7)
> # use logreg.savefit7 for the results with 25000 iterations
> plot(logreg.savefit7)
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "logreg.anneal.control"
> 
> ### * logreg.anneal.control
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: logreg.anneal.control
> ### Title: Control for Logic Regression
> ### Aliases: logreg.anneal.control
> ### Keywords: logic methods nonparametric tree
> 
> ### ** Examples
> 
> myannealcontrol <- logreg.anneal.control(start = 2, end = -2, iter = 50000, update = 1000)
> 
> 
> 
> cleanEx(); ..nameEx <- "logreg.mc.control"
> 
> ### * logreg.mc.control
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: logreg.mc.control
> ### Title: Control for Logic Regression
> ### Aliases: logreg.mc.control
> ### Keywords: logic methods nonparametric tree
> 
> ### ** Examples
> 
> mymccontrol <- logreg.mc.control(nburn = 500, niter = 500000, update = 25000,
+ hyperpars = log(2), output = -2)
> 
> 
> 
> cleanEx(); ..nameEx <- "logreg.myown"
> 
> ### * logreg.myown
> 
> flush(stderr()); flush(stdout())
logreg.myown            package:LogicReg            R Documentation

_W_r_i_t_i_n_g _y_o_u_r _o_w_n _L_o_g_i_c _R_e_g_r_e_s_s_i_o_n _s_c_o_r_i_n_g _f_u_n_c_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Help file for writing your own scoring function for 'logreg'!

_U_s_a_g_e:

     logreg.myown()

_D_e_t_a_i_l_s:

     You can write your own scoring function for 'logreg'!  This may be
     useful if you have a model other than those which we already
     programmed in.

     Essentially you need to provide two routines in the file
     *My_own_scoring.f*:
      (i) A routine _My_own_fitting_ which fits your model: it provides
     a coefficient (beta) for each of the logic trees and provides a
     score of how good the model is. Low scores are good. (So add a
     minus sign if your score is a log-likelihood.)
      (ii) A routine _My_own_scoring_ which - given the betas -
     provides the score of your model. [If you don't use
     cross-validation, this second routine is not needed, though some
     dummy routine to satisfy the compiler should still be provided.]

     After recompilation, you can fit your model using the option '
     type = 0' in 'logreg'.  Below we give an example for a version of
     the My.own functions for conditional logistic regression which are
     also provided as *condlogic.ff* when you downloaded the files.

     *PROGRAMMING DETAILS*

     Below is a list of variables that are passed on. Most of them are
     as you expect - response, predictors (binary ones and continuous
     ones), number of cases, number of predictors.  In addition there
     are two columns - 'dcph' and 'weight' - that can either be used to
     pass on an auxiliary variable for each case (discrete for  'dcph'
     and continuous for 'weight'), or even some overall auxiliary
     variables - as these numbers are not used anywhere else.  If you
     do not need any of the variables - just ignore them!

     'prtr':
       the predictions of the logic trees in the current model: this is
     an integer matrix of size 'n1' times 'ntr' - although only the
     first 'nop' columns contain useful information. 
      'rsp':
      the response variable: this is a real (single precision) vector
     of length 'n1'.   
      'dcph': 
      censor times: this is an integer vector of length 'n1' this could
     be used as an auxiliary (integer) vector - as it is just passed
     on. (There is no check that this is a 0/1 variable, when you use
     your own scoring function.)  For example, you could use this to
     pass on something like cluster membership. 
      'weight':
      weights for the cases this is a real vector of length 'n1'.  this
     could be used as an auxiliary (real) vector - as it is just passed
     on. There is no check that these numbers are positive, when you
     choose your own scoring function. 
      'ordrs':
       the order (by response size) of the cases This is an integer
     vector of length 'n1'. For the case with the smallest response
     this one is 1, for the second smallest 2, and so on. Ties are
     resolved arbitrary. Always computed, although only used for
     proportional hazards models. Use it as you wish. 
      'n1':
       the total number of cases in the data. 
      'ntr':
       the number of logic trees ALLOWED in the tree. 
      'nop':
       the number of logic trees in the CURRENT model.  The subroutines
     should work if 'nop' is 0. 
      'wh':
       the index of the tree that has been edited in the last move -
     i.e. the column of 'prtr' that has changes since the last call. 
      'nsep':
       number of variables that get fit a separate parameter The
     subroutines should work if 'nsep' is 0. 
      'seps':
       array of the above variables - this is a single precision matrix
     of size 'nsep' times 'n1'. Note that 'seps' and 'prtr' are stored
     in different directions.

     For _My_own_fitting_ you should return:
      'betas':
       a vector of parameters of the model that you fit.  'betas(0)'
     should be the parameter for the intercept 'betas(1:nsep)' should
     be the parameters for the continuous variables in seps
     'betas((nsep+1):(nsep+nop))' should be the parameters for the
     binary trees in prtr if you have more parameters, use 'dcph', or
     'weight'; these variables will not be printed however. 
      'score':
       whatever score you assign to your model small should be good
     (i.e. deviance or -log.likelihood). 
      'reject':
       an indicator whether or not to reject the proposed move
     *regardless* of the score (for example when an iteration necessary
     to determine the score failed to converge (0 = move is OK ; 1 =
     reject move) set this one to 0 if there is no such condition.

     You are allowed to change the values of dcph, and weight.

     For _My_own_scoring_ additional input is:
      'betas': the coefficients

     You should return:
      'score': whatever score you assign to your model small should be
     good (i.e. deviance or -log.likelihood). If the model "crashes",
     you should simply return a very large number.

     While we try to prevent that models are singular, it is possible
     that for your model a single or degenerate model is passed on for
     evaluation. For My_own_fitting you can pass the model back with
     'reject = 1', for My_own_scoring you can pass it on with a very
     large value for 'score'.  Currently My_own_scoring.f contains
     empty frames for the scoring functions; condlogic.ff contains an
     example with conditional logistic regression.

     The logic regression program is written in Fortran 77.

     *CONDITIONAL LOGISTIC REGRESSION*

     A function for a conditional logistic regression score function is
     attached as an example function on how to write your own scoring
     function for Logic Regression.  Obviously you can also use it if
     you have conditional logistic data.

     Conditional logistic regression is common model fitting technique
     for matched case-control studies, in which each case is matched
     with one or more controls. (In conditional logistic regression
     several cases could be matched to several controls, in the
     implementation provided here only one case can be matched with
     each group of controls.) Conditional logistic regression models
     are parameterized like regular logistic regression models, except
     that the intercept is no longer identifiable. See, for example,
     Breslow and Day - Volume 1 (1990, Statistical Methods in Cancer
     Research, International Agency for Research on Cancer, Lyon) for
     details.  Conditional logistic regression models are most easily
     fit using a stratified proportional hazards model (if there is
     one-to-one case-control matching it can also be fit using logistic
     regression, but that method breaks down if there is more than one
     control per case). Each group of a case and controls is one
     stratum. All cases get an arbitrarily event time of 1.00, and all
     controls get a censoring time of 2.00.

     In our implementation we use the response column to indicate the
     matching. For all controls this column is 0, for a case it is k,
     indicating that the next k records are the matched controls for
     the current case. Thus, we order our cases so that each case is
     followed by its controls. Cases with a negative response are put
     in a stratum -1, which is not used in any computations.  This has
     implications for cross-validation. See below.

     In _My_own_fitting_ and _My_own_scoring_ we first allocate various
     vectors (strata, index, censoring variable) that are local, as
     well as some work arrays that are used by our fitting routines. 
     (We need to set some of the parameters for that, see the help page
     of 'logreg' for details.)  We then define 'idx(j)=j' for 'j=1,n1',
     and we define the 'strata' and 'delta' vectors.  We use slightly
     modified versions of the proportional hazards routines that are
     already used otherwise in the Logic Regression program, to include
     stratification. After the model is fitted, we assign minus the
     partial likelihood to 'score(1)' and (for _My_own_fitting_) we
     pass on the betas.

     Recompile after replacing _My_own_scoring.f_ by _condlogic.ff_

     The permutation and null model versions are not directly usable
     (we could do some permutation tests, but they require more
     programming), but we can use cross-validation. Obviously we should
     keep cases and controls match. To that extend, we would run
     permutation with a negative seed (see 'logreg') and we would take
     care ourselves that case-control groups are in a random order, and
     that every block has the same number of records. We achieve the
     later by adding some records with response -1.  In particular,
     suppose that we have 19 pairs of case- (single) control data, and
     that we want to do 3-fold cross validation. We would permute the
     sequence of the 19 pairs, and add two records with response -1
     after the 13th pair, and two records with -1 at the end of the
     file, so that the total data file would have 42 records.

_A_u_t_h_o_r(_s):

     Ingo Ruczinski ingo@jhu.edu and Charles Kooperberg clk@fhcrc.org.

_R_e_f_e_r_e_n_c_e_s:

     Ruczinski I, Kooperberg C, LeBlanc ML (2003).  Logic Regression,
     _Journal of Computational and Graphical Statistics_, *12*,
     475-511.

     Ruczinski I, Kooperberg C, LeBlanc ML (2002).  Logic Regression -
     methods and software.  _Proceedings of the MSRI workshop on
     Nonlinear Estimation and Classification_ (Eds: D. Denison, M.
     Hansen, C. Holmes, B. Mallick, B. Yu), Springer: New York,
     333-344.

     Kooperberg C, Ruczinski I, LeBlanc ML, Hsu L (2001). Sequence
     Analysis using Logic Regression, _Genetic Epidemiology_, *21*,
     S626-S631.

     Selected chapters from the dissertation of Ingo Ruczinski,
     available from <URL:
     http://bear.fhcrc.org/~ingor/logic/documents/myphd-logic.pdf>

_S_e_e _A_l_s_o:

     'logreg'

_E_x_a_m_p_l_e_s:

     logreg.myown()      # displays this help file
     help(logreg.myown)  # equivalent


logreg.myown            package:LogicReg            R Documentation

_W_r_i_t_i_n_g _y_o_u_r _o_w_n _L_o_g_i_c _R_e_g_r_e_s_s_i_o_n _s_c_o_r_i_n_g _f_u_n_c_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Help file for writing your own scoring function for 'logreg'!

_U_s_a_g_e:

     logreg.myown()

_D_e_t_a_i_l_s:

     You can write your own scoring function for 'logreg'!  This may be
     useful if you have a model other than those which we already
     programmed in.

     Essentially you need to provide two routines in the file
     *My_own_scoring.f*:
      (i) A routine _My_own_fitting_ which fits your model: it provides
     a coefficient (beta) for each of the logic trees and provides a
     score of how good the model is. Low scores are good. (So add a
     minus sign if your score is a log-likelihood.)
      (ii) A routine _My_own_scoring_ which - given the betas -
     provides the score of your model. [If you don't use
     cross-validation, this second routine is not needed, though some
     dummy routine to satisfy the compiler should still be provided.]

     After recompilation, you can fit your model using the option '
     type = 0' in 'logreg'.  Below we give an example for a version of
     the My.own functions for conditional logistic regression which are
     also provided as *condlogic.ff* when you downloaded the files.

     *PROGRAMMING DETAILS*

     Below is a list of variables that are passed on. Most of them are
     as you expect - response, predictors (binary ones and continuous
     ones), number of cases, number of predictors.  In addition there
     are two columns - 'dcph' and 'weight' - that can either be used to
     pass on an auxiliary variable for each case (discrete for  'dcph'
     and continuous for 'weight'), or even some overall auxiliary
     variables - as these numbers are not used anywhere else.  If you
     do not need any of the variables - just ignore them!

     'prtr':
       the predictions of the logic trees in the current model: this is
     an integer matrix of size 'n1' times 'ntr' - although only the
     first 'nop' columns contain useful information. 
      'rsp':
      the response variable: this is a real (single precision) vector
     of length 'n1'.   
      'dcph': 
      censor times: this is an integer vector of length 'n1' this could
     be used as an auxiliary (integer) vector - as it is just passed
     on. (There is no check that this is a 0/1 variable, when you use
     your own scoring function.)  For example, you could use this to
     pass on something like cluster membership. 
      'weight':
      weights for the cases this is a real vector of length 'n1'.  this
     could be used as an auxiliary (real) vector - as it is just passed
     on. There is no check that these numbers are positive, when you
     choose your own scoring function. 
      'ordrs':
       the order (by response size) of the cases This is an integer
     vector of length 'n1'. For the case with the smallest response
     this one is 1, for the second smallest 2, and so on. Ties are
     resolved arbitrary. Always computed, although only used for
     proportional hazards models. Use it as you wish. 
      'n1':
       the total number of cases in the data. 
      'ntr':
       the number of logic trees ALLOWED in the tree. 
      'nop':
       the number of logic trees in the CURRENT model.  The subroutines
     should work if 'nop' is 0. 
      'wh':
       the index of the tree that has been edited in the last move -
     i.e. the column of 'prtr' that has changes since the last call. 
      'nsep':
       number of variables that get fit a separate parameter The
     subroutines should work if 'nsep' is 0. 
      'seps':
       array of the above variables - this is a single precision matrix
     of size 'nsep' times 'n1'. Note that 'seps' and 'prtr' are stored
     in different directions.

     For _My_own_fitting_ you should return:
      'betas':
       a vector of parameters of the model that you fit.  'betas(0)'
     should be the parameter for the intercept 'betas(1:nsep)' should
     be the parameters for the continuous variables in seps
     'betas((nsep+1):(nsep+nop))' should be the parameters for the
     binary trees in prtr if you have more parameters, use 'dcph', or
     'weight'; these variables will not be printed however. 
      'score':
       whatever score you assign to your model small should be good
     (i.e. deviance or -log.likelihood). 
      'reject':
       an indicator whether or not to reject the proposed move
     *regardless* of the score (for example when an iteration necessary
     to determine the score failed to converge (0 = move is OK ; 1 =
     reject move) set this one to 0 if there is no such condition.

     You are allowed to change the values of dcph, and weight.

     For _My_own_scoring_ additional input is:
      'betas': the coefficients

     You should return:
      'score': whatever score you assign to your model small should be
     good (i.e. deviance or -log.likelihood). If the model "crashes",
     you should simply return a very large number.

     While we try to prevent that models are singular, it is possible
     that for your model a single or degenerate model is passed on for
     evaluation. For My_own_fitting you can pass the model back with
     'reject = 1', for My_own_scoring you can pass it on with a very
     large value for 'score'.  Currently My_own_scoring.f contains
     empty frames for the scoring functions; condlogic.ff contains an
     example with conditional logistic regression.

     The logic regression program is written in Fortran 77.

     *CONDITIONAL LOGISTIC REGRESSION*

     A function for a conditional logistic regression score function is
     attached as an example function on how to write your own scoring
     function for Logic Regression.  Obviously you can also use it if
     you have conditional logistic data.

     Conditional logistic regression is common model fitting technique
     for matched case-control studies, in which each case is matched
     with one or more controls. (In conditional logistic regression
     several cases could be matched to several controls, in the
     implementation provided here only one case can be matched with
     each group of controls.) Conditional logistic regression models
     are parameterized like regular logistic regression models, except
     that the intercept is no longer identifiable. See, for example,
     Breslow and Day - Volume 1 (1990, Statistical Methods in Cancer
     Research, International Agency for Research on Cancer, Lyon) for
     details.  Conditional logistic regression models are most easily
     fit using a stratified proportional hazards model (if there is
     one-to-one case-control matching it can also be fit using logistic
     regression, but that method breaks down if there is more than one
     control per case). Each group of a case and controls is one
     stratum. All cases get an arbitrarily event time of 1.00, and all
     controls get a censoring time of 2.00.

     In our implementation we use the response column to indicate the
     matching. For all controls this column is 0, for a case it is k,
     indicating that the next k records are the matched controls for
     the current case. Thus, we order our cases so that each case is
     followed by its controls. Cases with a negative response are put
     in a stratum -1, which is not used in any computations.  This has
     implications for cross-validation. See below.

     In _My_own_fitting_ and _My_own_scoring_ we first allocate various
     vectors (strata, index, censoring variable) that are local, as
     well as some work arrays that are used by our fitting routines. 
     (We need to set some of the parameters for that, see the help page
     of 'logreg' for details.)  We then define 'idx(j)=j' for 'j=1,n1',
     and we define the 'strata' and 'delta' vectors.  We use slightly
     modified versions of the proportional hazards routines that are
     already used otherwise in the Logic Regression program, to include
     stratification. After the model is fitted, we assign minus the
     partial likelihood to 'score(1)' and (for _My_own_fitting_) we
     pass on the betas.

     Recompile after replacing _My_own_scoring.f_ by _condlogic.ff_

     The permutation and null model versions are not directly usable
     (we could do some permutation tests, but they require more
     programming), but we can use cross-validation. Obviously we should
     keep cases and controls match. To that extend, we would run
     permutation with a negative seed (see 'logreg') and we would take
     care ourselves that case-control groups are in a random order, and
     that every block has the same number of records. We achieve the
     later by adding some records with response -1.  In particular,
     suppose that we have 19 pairs of case- (single) control data, and
     that we want to do 3-fold cross validation. We would permute the
     sequence of the 19 pairs, and add two records with response -1
     after the 13th pair, and two records with -1 at the end of the
     file, so that the total data file would have 42 records.

_A_u_t_h_o_r(_s):

     Ingo Ruczinski ingo@jhu.edu and Charles Kooperberg clk@fhcrc.org.

_R_e_f_e_r_e_n_c_e_s:

     Ruczinski I, Kooperberg C, LeBlanc ML (2003).  Logic Regression,
     _Journal of Computational and Graphical Statistics_, *12*,
     475-511.

     Ruczinski I, Kooperberg C, LeBlanc ML (2002).  Logic Regression -
     methods and software.  _Proceedings of the MSRI workshop on
     Nonlinear Estimation and Classification_ (Eds: D. Denison, M.
     Hansen, C. Holmes, B. Mallick, B. Yu), Springer: New York,
     333-344.

     Kooperberg C, Ruczinski I, LeBlanc ML, Hsu L (2001). Sequence
     Analysis using Logic Regression, _Genetic Epidemiology_, *21*,
     S626-S631.

     Selected chapters from the dissertation of Ingo Ruczinski,
     available from <URL:
     http://bear.fhcrc.org/~ingor/logic/documents/myphd-logic.pdf>

_S_e_e _A_l_s_o:

     'logreg'

_E_x_a_m_p_l_e_s:

     logreg.myown()      # displays this help file
     help(logreg.myown)  # equivalent


> 
> ### Name: logreg.myown
> ### Title: Writing your own Logic Regression scoring function
> ### Aliases: logreg.myown
> ### Keywords: logic methods nonparametric tree
> 
> ### ** Examples
> 
> logreg.myown()      # displays this help file
> help(logreg.myown)  # equivalent
> 
> 
> 
> cleanEx(); ..nameEx <- "logreg.savefit1"
> 
> ### * logreg.savefit1
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: logreg.savefit1
> ### Title: Sample results for Logic Regression
> ### Aliases: logreg.savefit1 logreg.savefit2 logreg.savefit3
> ###   logreg.savefit4 logreg.savefit5 logreg.savefit6 logreg.savefit7
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(logreg.savefit1)
> print(logreg.savefit1$call)
logreg(resp = logreg.testdat[, 1], bin = logreg.testdat[, 2:21], 
    type = 2, select = 1, ntrees = 2, anneal.control = myanneal)
> data(logreg.savefit2)
> print(logreg.savefit2$call)
logreg(resp = logreg.testdat[, 1], bin = logreg.testdat[, 2:21], 
    type = 2, select = 2, ntrees = c(1, 2), nleaves = c(1, 7), 
    anneal.control = myanneal2)
> data(logreg.savefit3)
> print(logreg.savefit3$call)
logreg(resp = logreg.testdat[, 1], bin = logreg.testdat[, 2:21], 
    type = 2, select = 3, ntrees = c(1, 2), nleaves = c(1, 7), 
    anneal.control = myanneal2)
> data(logreg.savefit4)
> print(logreg.savefit4$call)
logreg(resp = logreg.testdat[, 1], bin = logreg.testdat[, 2:21], 
    type = 2, select = 4, ntrees = 2, anneal.control = myanneal2)
> data(logreg.savefit5)
> print(logreg.savefit5$call)
logreg(resp = logreg.testdat[, 1], bin = logreg.testdat[, 2:21], 
    type = 2, select = 5, ntrees = c(1, 2), nleaves = c(1, 7), 
    oldfit = logregsavefit2, anneal.control = myanneal2)
> data(logreg.savefit6)
> print(logreg.savefit6$call)
logreg(select = 6, ntrees = 2, nleaves = c(1, 12), oldfit = logregsavefit1)
> data(logreg.savefit7)
> print(logreg.savefit7$call)
logreg(select = 7, oldfit = logregsavefit1, mc.control = logreg.mc.control(nburn = 1000, 
    niter = 1e+05, hyperpars = log(2)))
> 
> 
> 
> cleanEx(); ..nameEx <- "logreg.testdat"
> 
> ### * logreg.testdat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: logreg.testdat
> ### Title: Test data for Logic Regression
> ### Aliases: logreg.testdat
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(logreg.testdat)
> 
> 
> 
> cleanEx(); ..nameEx <- "logreg.tree.control"
> 
> ### * logreg.tree.control
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: logreg.tree.control
> ### Title: Control for logreg
> ### Aliases: logreg.tree.control
> ### Keywords: logic methods nonparametric tree
> 
> ### ** Examples
> 
> mytreecontrol <- logreg.tree.control(treesize = 16, minmass = 10)
> 
> 
> 
> cleanEx(); ..nameEx <- "logregmodel"
> 
> ### * logregmodel
> 
> flush(stderr()); flush(stdout())
logregmodel             package:LogicReg             R Documentation

_F_o_r_m_a_t _o_f _c_l_a_s_s _l_o_g_r_e_g_m_o_d_e_l

_D_e_s_c_r_i_p_t_i_o_n:

     This help file contains a description of the format of class
     logregmodel.

_U_s_a_g_e:

     logregmodel()

_V_a_l_u_e:

     An object of class logregtree has the following components: 

  ntrees: the number of trees in the current model.

 nleaves: the number of leaves for the fitted model.

    coef: the coefficients for this model.

   score: the score of the fitted model.

   trees: a list of 'ntrees' objects of class 'logregtree'.

_A_u_t_h_o_r(_s):

     Ingo Ruczinski ingo@jhu.edu and Charles Kooperberg clk@fhcrc.org.

_R_e_f_e_r_e_n_c_e_s:

     Ruczinski I, Kooperberg C, LeBlanc ML (2003).  Logic Regression,
     _Journal of Computational and Graphical Statistics_, *12*,
     475-511.

     Ruczinski I, Kooperberg C, LeBlanc ML (2002).  Logic Regression -
     methods and software.  _Proceedings of the MSRI workshop on
     Nonlinear Estimation and Classification_ (Eds: D. Denison, M.
     Hansen, C. Holmes, B. Mallick, B. Yu), Springer: New York,
     333-344.

     Kooperberg C, Ruczinski I, LeBlanc ML, Hsu L (2001). Sequence
     Analysis using Logic Regression, _Genetic Epidemiology_, *21*,
     S626-S631.

     Selected chapters from the dissertation of Ingo Ruczinski,
     available from <URL:
     http://bear.fhcrc.org/~ingor/logic/documents/myphd-logic.pdf>

_S_e_e _A_l_s_o:

     'logreg', 'plot.logregmodel', 'print.logregmodel', 'logregtree'

_E_x_a_m_p_l_e_s:

     logregmodel()       # displays this help file
     help(logregmodel)   # equivalent


logregmodel             package:LogicReg             R Documentation

_F_o_r_m_a_t _o_f _c_l_a_s_s _l_o_g_r_e_g_m_o_d_e_l

_D_e_s_c_r_i_p_t_i_o_n:

     This help file contains a description of the format of class
     logregmodel.

_U_s_a_g_e:

     logregmodel()

_V_a_l_u_e:

     An object of class logregtree has the following components: 

  ntrees: the number of trees in the current model.

 nleaves: the number of leaves for the fitted model.

    coef: the coefficients for this model.

   score: the score of the fitted model.

   trees: a list of 'ntrees' objects of class 'logregtree'.

_A_u_t_h_o_r(_s):

     Ingo Ruczinski ingo@jhu.edu and Charles Kooperberg clk@fhcrc.org.

_R_e_f_e_r_e_n_c_e_s:

     Ruczinski I, Kooperberg C, LeBlanc ML (2003).  Logic Regression,
     _Journal of Computational and Graphical Statistics_, *12*,
     475-511.

     Ruczinski I, Kooperberg C, LeBlanc ML (2002).  Logic Regression -
     methods and software.  _Proceedings of the MSRI workshop on
     Nonlinear Estimation and Classification_ (Eds: D. Denison, M.
     Hansen, C. Holmes, B. Mallick, B. Yu), Springer: New York,
     333-344.

     Kooperberg C, Ruczinski I, LeBlanc ML, Hsu L (2001). Sequence
     Analysis using Logic Regression, _Genetic Epidemiology_, *21*,
     S626-S631.

     Selected chapters from the dissertation of Ingo Ruczinski,
     available from <URL:
     http://bear.fhcrc.org/~ingor/logic/documents/myphd-logic.pdf>

_S_e_e _A_l_s_o:

     'logreg', 'plot.logregmodel', 'print.logregmodel', 'logregtree'

_E_x_a_m_p_l_e_s:

     logregmodel()       # displays this help file
     help(logregmodel)   # equivalent


> 
> ### Name: logregmodel
> ### Title: Format of class logregmodel
> ### Aliases: logregmodel
> ### Keywords: logic methods nonparametric tree
> 
> ### ** Examples
> 
> logregmodel()       # displays this help file
> help(logregmodel)   # equivalent
> 
> 
> 
> cleanEx(); ..nameEx <- "logregtree"
> 
> ### * logregtree
> 
> flush(stderr()); flush(stdout())
logregtree             package:LogicReg             R Documentation

_F_o_r_m_a_t _o_f _c_l_a_s_s _l_o_g_r_e_g_t_r_e_e

_D_e_s_c_r_i_p_t_i_o_n:

     This help file contains a description of the format of class
     logregtree.

_U_s_a_g_e:

     logregtree()

_D_e_t_a_i_l_s:

     When storing trees, we number the location of the nodes using the
     following scheme (this is an example for a tree with at most 8
     _terminal_ nodes, but the generalization should be obvious):

                              1                        
                2                           3          
          4            5             6             7   
       8     9     10     11     12     13     14     15

     Each node may or may not be present in the current tree. If it is
     present, it can contain an operator (``and'' or ``or''), in which
     case it has to child nodes, or it can contain a variable, in which
     case the node is a terminal node. It is also possible that the
     node does not exist (as the user only specifies the maximum tree
     size, not the tree size that is actually fitted).

     Output files have one line for each node. Each line contains 5
     numbers:

        1.  the node number.

        2.  does this node contain an ``and'' (1), an ``or'' (2), a
           variable (3), or is the node empty (0).

        3.  if the node contains a variable, which one is it; e.g. if
           this number is 3 the node contains X3.

        4.  if the node contains a variable, does it contain the
           regular variable (0) or its complement (1)

        5.  is the node empty (0) or not (1) (this information is
           redundant with the second number) .inen -3

           *Example*

                                          AND                        
                                                                     
                                                                     
                        OR                            OR             
                                                                     
                                                                     
               OR               OR               X20            OR   
                                                                     
          X17      X12      X3      X13c                    X2      X1

           is represented as

           1  1   0  0  1
           2  2   0  0  1
           3  2   0  0  1
           4  2   0  0  1
           5  2   0  0  1
           6  3  20  0  1
           7  2   0  0  1
           8  3  17  0  1
           9  3  12  0  1
          10  3   3  0  1
          11  3  13  1  1
          12  0   0  0  0
          13  0   0  0  0
          14  3   2  0  1
          15  3   1  0  1

_V_a_l_u_e:

     An object of class logregtree is typically a substructure of an
     object of the class 'logregmodel'.  It will typically be the
     result of using the fitting function 'logreg'.  An object of class
     logictree has the following components: 

whichtree: the sequence number of the current tree within the model.

    coef: the coefficients of this tree.

   trees: a matrix (data.frame) with five columns; see below for the
          format.

_A_u_t_h_o_r(_s):

     Ingo Ruczinski ingo@jhu.edu and Charles Kooperberg clk@fhcrc.org.

_R_e_f_e_r_e_n_c_e_s:

     Ruczinski I, Kooperberg C, LeBlanc ML (2003).  Logic Regression,
     _Journal of Computational and Graphical Statistics_, *12*,
     475-511.

     Ruczinski I, Kooperberg C, LeBlanc ML (2002).  Logic Regression -
     methods and software.  _Proceedings of the MSRI workshop on
     Nonlinear Estimation and Classification_ (Eds: D. Denison, M.
     Hansen, C. Holmes, B. Mallick, B. Yu), Springer: New York,
     333-344.

     Selected chapters from the dissertation of Ingo Ruczinski,
     available from <URL:
     http://bear.fhcrc.org/~ingor/logic/documents/myphd-logic.pdf>

_S_e_e _A_l_s_o:

     'logreg', 'plot.logregtree', 'print.logregtree', 'logregmodel'

_E_x_a_m_p_l_e_s:

     logregtree()       # displays this help file
     help(logregtree)   # equivalent


logregtree             package:LogicReg             R Documentation

_F_o_r_m_a_t _o_f _c_l_a_s_s _l_o_g_r_e_g_t_r_e_e

_D_e_s_c_r_i_p_t_i_o_n:

     This help file contains a description of the format of class
     logregtree.

_U_s_a_g_e:

     logregtree()

_D_e_t_a_i_l_s:

     When storing trees, we number the location of the nodes using the
     following scheme (this is an example for a tree with at most 8
     _terminal_ nodes, but the generalization should be obvious):

                              1                        
                2                           3          
          4            5             6             7   
       8     9     10     11     12     13     14     15

     Each node may or may not be present in the current tree. If it is
     present, it can contain an operator (``and'' or ``or''), in which
     case it has to child nodes, or it can contain a variable, in which
     case the node is a terminal node. It is also possible that the
     node does not exist (as the user only specifies the maximum tree
     size, not the tree size that is actually fitted).

     Output files have one line for each node. Each line contains 5
     numbers:

        1.  the node number.

        2.  does this node contain an ``and'' (1), an ``or'' (2), a
           variable (3), or is the node empty (0).

        3.  if the node contains a variable, which one is it; e.g. if
           this number is 3 the node contains X3.

        4.  if the node contains a variable, does it contain the
           regular variable (0) or its complement (1)

        5.  is the node empty (0) or not (1) (this information is
           redundant with the second number) .inen -3

           *Example*

                                          AND                        
                                                                     
                                                                     
                        OR                            OR             
                                                                     
                                                                     
               OR               OR               X20            OR   
                                                                     
          X17      X12      X3      X13c                    X2      X1

           is represented as

           1  1   0  0  1
           2  2   0  0  1
           3  2   0  0  1
           4  2   0  0  1
           5  2   0  0  1
           6  3  20  0  1
           7  2   0  0  1
           8  3  17  0  1
           9  3  12  0  1
          10  3   3  0  1
          11  3  13  1  1
          12  0   0  0  0
          13  0   0  0  0
          14  3   2  0  1
          15  3   1  0  1

_V_a_l_u_e:

     An object of class logregtree is typically a substructure of an
     object of the class 'logregmodel'.  It will typically be the
     result of using the fitting function 'logreg'.  An object of class
     logictree has the following components: 

whichtree: the sequence number of the current tree within the model.

    coef: the coefficients of this tree.

   trees: a matrix (data.frame) with five columns; see below for the
          format.

_A_u_t_h_o_r(_s):

     Ingo Ruczinski ingo@jhu.edu and Charles Kooperberg clk@fhcrc.org.

_R_e_f_e_r_e_n_c_e_s:

     Ruczinski I, Kooperberg C, LeBlanc ML (2003).  Logic Regression,
     _Journal of Computational and Graphical Statistics_, *12*,
     475-511.

     Ruczinski I, Kooperberg C, LeBlanc ML (2002).  Logic Regression -
     methods and software.  _Proceedings of the MSRI workshop on
     Nonlinear Estimation and Classification_ (Eds: D. Denison, M.
     Hansen, C. Holmes, B. Mallick, B. Yu), Springer: New York,
     333-344.

     Selected chapters from the dissertation of Ingo Ruczinski,
     available from <URL:
     http://bear.fhcrc.org/~ingor/logic/documents/myphd-logic.pdf>

_S_e_e _A_l_s_o:

     'logreg', 'plot.logregtree', 'print.logregtree', 'logregmodel'

_E_x_a_m_p_l_e_s:

     logregtree()       # displays this help file
     help(logregtree)   # equivalent


> 
> ### Name: logregtree
> ### Title: Format of class logregtree
> ### Aliases: logregtree
> ### Keywords: logic methods nonparametric tree
> 
> ### ** Examples
> 
> logregtree()       # displays this help file
> help(logregtree)   # equivalent
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.logreg"
> 
> ### * plot.logreg
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.logreg
> ### Title: Plots for Logic Regression
> ### Aliases: plot.logreg
> ### Keywords: logic methods nonparametric tree
> 
> ### ** Examples
> 
> data(logreg.savefit1,logreg.savefit2,logreg.savefit3,logreg.savefit4,
+      logreg.savefit5,logreg.savefit6,logreg.savefit7)
> #
> # fit a single model
> # myanneal <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 1000)
> # logreg.savefit1 <- logreg(resp = logreg.testdat[,1], bin=logreg.testdat[, 2:21],
> #                type = 2, select = 1, ntrees = 2, anneal.control = myanneal)
> # the best score should be in the 0.96-0.98 range
> plot(logreg.savefit1)
> #
> # fit multiple models
> # myanneal2 <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 0)
> # logreg.savefit2 <- logreg(select = 2, ntrees = c(1,2), nleaves =c(1,7), 
> #                oldfit = logreg.savefit1, anneal.control = myanneal2)
> plot(logreg.savefit2)
> # After an initial steep decline, the scores only get slightly better
> # for models with more than four leaves and two trees. 
> #
> # cross validation
> # logreg.savefit3 <- logreg(select = 3, oldfit = logreg.savefit2)
> plot(logreg.savefit3)
> # 4 leaves, 2 trees should give the best test set score
> #
> # null model test
> # logreg.savefit4 <- logreg(select = 4, anneal.control = myanneal2, oldfit = logreg.savefit1)
> plot(logreg.savefit4)
> # A histogram of the 25 scores obtained from the permutation test. Also shown
> # are the scores for the best scoring model with one logic tree, and the null
> # model (no tree). As the permutation scores are not even close to the score
> # of the best model with one tree (fit on the original data), there is strong
> # evidence against the null hypothesis that there was no signal in the data. 
> #
> # Permutation tests
> # logreg.savefit5 <- logreg(select = 5, oldfit = logreg.savefit2)
> plot(logreg.savefit5)
> # The permutation scores improve until we condition on a model with two
> # trees and four leaves, and then do not change very much anymore. This 
> # indicates that the best model has indeed four leaves.
> #
> # a greedy sequence
> # logreg.savefit6 <- logreg(select = 6, ntrees = 2, nleaves =c(1,12), oldfit = logreg.savefit1)
> plot(logreg.savefit6)
empty tree 
empty tree 
empty tree 
empty tree 
> # Monte Carlo Logic Regression
> # logreg.savefit7 <- logreg(select = 7, oldfit = logreg.savefit1, mc.control=
> #                logreg.mc.control(nburn=1000, niter=100000, hyperpars=log(2)))
> plot(logreg.savefit7)
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.logregmodel"
> 
> ### * plot.logregmodel
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.logregmodel
> ### Title: Plots for Logic Regression
> ### Aliases: plot.logregmodel
> ### Keywords: logic methods nonparametric tree
> 
> ### ** Examples
> 
> data(logreg.savefit1)
> # myanneal <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 1000)
> # logreg.savefit1 <- logreg(resp = logreg.testdat[,1], bin=logreg.testdat[, 2:21],
> #                type = 2, select = 1, ntrees = 2, anneal.control = myanneal)
> # plot(logreg.savefit1)
> plot(logreg.savefit1$model) # does the same
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.logregtree"
> 
> ### * plot.logregtree
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.logregtree
> ### Title: A plot of one Logic Regression tree.
> ### Aliases: plot.logregtree
> ### Keywords: logic methods nonparametric tree
> 
> ### ** Examples
> 
> data(logreg.savefit2)
> # 
> # myanneal2 <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 0)
> # logreg.savefit2 <- logreg(resp = logreg.testdat[,1], bin=logreg.testdat[, 2:21],
> #                type = 2, select = 2, ntrees = c(1,2), nleaves =c(1,7),
> #                anneal.control = myanneal2)
> for(i in 1:logreg.savefit2$nmodels) for(j in 1:logreg.savefit2$alltrees[[i]]$ntrees[1]){
+    plot.logregtree(logreg.savefit2$alltrees[[i]]$trees[[j]])
+    title(main=paste("model",i,"tree",j))
+ }
> 
> 
> 
> cleanEx(); ..nameEx <- "predict.logreg"
> 
> ### * predict.logreg
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: predict.logreg
> ### Title: Predicted values Logic Regression
> ### Aliases: predict.logreg
> ### Keywords: logic methods nonparametric tree
> 
> ### ** Examples
> 
> data(logreg.savefit1,logreg.savefit2,logreg.savefit6,logreg.testdat)
> #
> # myanneal <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 1000)
> # logreg.savefit1 <- logreg(resp = logreg.testdat[,1], bin=logreg.testdat[, 2:21], type = 2,
> #                select = 1, ntrees = 2, anneal.control = myanneal)
> z1 <- predict(logreg.savefit1)
> plot(z1, logreg.testdat[,1]-z1, xlab="fitted values", ylab="residuals")
> # myanneal2 <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 0)
> # logreg.savefit2 <- logreg(select = 2, nleaves =c(1,7), oldfit = logreg.savefit1,
> #                anneal.control = myanneal2)
> z2  <- predict(logreg.savefit2)
> # logreg.savefit6 <- logreg(select = 6, ntrees = 2, nleaves =c(1,12), oldfit = logreg.savefit1)
> z6 <- predict(logreg.savefit6, msz = 3:5)
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "print.logreg"
> 
> ### * print.logreg
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.logreg
> ### Title: Prints Logic Regression Output
> ### Aliases: print.logreg
> ### Keywords: logic methods nonparametric tree
> 
> ### ** Examples
> 
> data(logreg.savefit1,logreg.savefit2,logreg.savefit3,logreg.savefit4,
+      logreg.savefit5,logreg.savefit6)
> #
> # fit a single model
> # myanneal <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 1000)
> # logreg.savefit1 <- logreg(resp = logreg.testdat[,1], bin=logreg.testdat[, 2:21],
> #                type = 2, select = 1, ntrees = 2, anneal.control = myanneal)
> # the best score should be in the 0.96-0.98 range
> print(logreg.savefit1)
score 0.966 
 -1.3 * (((X14 or (not X5)) and ((not X1) and (not X2))) and (((not X3) or X1) or ((not X20) and (not X2)))) +2.15 * (((not X4) or ((not X13) and (not X11))) and (not X3))
> #
> # fit multiple models
> # myanneal2 <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 0)
> # logreg.savefit2 <- logreg(select = 2, ntrees = c(1,2), nleaves =c(1,7),
> #                oldfit = logreg.savefit1, anneal.control = myanneal2)
> print(logreg.savefit2)
1 trees with 1 leaves: score is 1.15 
 -1.91 * X3
1 trees with 2 leaves: score is 1.048 
 +2.13 * ((not X3) and (not X4))
1 trees with 3 leaves: score is 1.046 
 +2.14 * ((not X3) and ((not X13) or (not X4)))
1 trees with 4 leaves: score is 1.042 
 +2.14 * ((((not X13) and (not X11)) or (not X4)) and (not X3))
1 trees with 5 leaves: score is 1.042 
 +2.14 * ((not X3) and ((not X4) or ((not X13) and (not X11))))
1 trees with 6 leaves: score is 1.042 
 -2.14 * (X3 or (X4 and (X13 or (not X19))))
1 trees with 7 leaves: score is 1.04 
 +2.15 * ((((not X4) or (not X13)) and (not X3)) or (((not X6) and X14) and ((not X1) and (not X12))))
2 trees with 2 leaves: score is 1.117 
 +1.89 * (not X3) -0.904 * X4
2 trees with 3 leaves: score is 1.033 
 +0.401 * X1 +2.13 * ((not X3) and (not X4))
2 trees with 4 leaves: score is 0.988 
 -1.11 * ((not X1) and (not X2)) -2.12 * (X3 or X4)
2 trees with 5 leaves: score is 0.982 
 +1.22 * ((X2 or X1) or X20) +2.12 * ((not X4) and (not X3))
2 trees with 6 leaves: score is 0.979 
 +2.13 * ((not X3) and (not X4)) +1.23 * ((X2 or X1) or (X20 and X3))
2 trees with 7 leaves: score is 0.978 
 +2.13 * ((not X3) and (not X4)) -1.29 * (((not X7) or ((not X5) or (not X15))) and ((not X1) and (not X2)))
> # After an initial steep decline, the scores only get slightly better
> # for models with more than four leaves and two trees.
> #
> # cross validation
> # logreg.savefit3 <- logreg(select = 3, oldfit = logreg.savefit2)
> print(logreg.savefit3)
    ntree nleaf train.ave    train.sd  cv/test cv/test.sd
10      1     1 1.1498841 0.016778885 1.167571 0.15626639
20      1     2 1.0478746 0.011226685 1.069748 0.10832190
30      1     3 1.0449851 0.010941456 1.086064 0.08148609
40      1     4 1.0415285 0.011277524 1.073501 0.11002902
50      1     5 1.0407865 0.010747466 1.103262 0.10778810
60      1     6 1.0375888 0.016061698 1.081281 0.08087984
70      1     7 1.0389121 0.012111442 1.083661 0.08286943
80      2     2 1.0742266 0.029866724 1.113556 0.14902853
90      2     3 1.0331042 0.009432286 1.069458 0.09146249
100     2     4 0.9880183 0.009248304 1.028298 0.08375898
110     2     5 0.9819231 0.008823874 1.034018 0.09038718
120     2     6 0.9780617 0.009782010 1.045832 0.09437465
130     2     7 0.9752644 0.008960632 1.059286 0.10264426
> # 4 leaves, 2 trees should give the best test set score
> #
> # null model test
> # logreg.savefit4 <- logreg(select = 4, anneal.control = myanneal2, oldfit = logreg.savefit1)
> print(logreg.savefit4)
Null Score 1.494 ; best score 0.969 
Summary 25 Randomized scores
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.398   1.411   1.423   1.421   1.428   1.444 
0  randomized scores ( 0 %) are better than the best score
> # A summary of the permutation test
> #
> # Permutation tests
> # logreg.savefit5 <- logreg(select = 5, oldfit = logreg.savefit2)
> print(logreg.savefit5)
25 randomizations
   trees leaves     null     start      best rand: min 1st Qu median   mean
1      1      1 1.494099 1.1500238 0.9707544    1.1080 1.1140 1.1190 1.1180
2      1      2 1.494099 1.0480632 0.9707544    0.9896 1.0140 1.0190 1.0170
3      1      3 1.494099 1.0456557 0.9707544    0.9987 1.0120 1.0160 1.0150
4      1      4 1.494099 1.0420860 0.9707544    1.0040 1.0080 1.0120 1.0120
5      1      5 1.494099 1.0420860 0.9707544    0.9996 1.0080 1.0110 1.0110
6      1      6 1.494099 1.0420861 0.9707544    0.9867 1.0140 1.0180 1.0160
7      1      7 1.494099 1.0400850 0.9707544    1.0040 1.0120 1.0160 1.0170
8      2      2 1.494099 1.1168472 0.9707544    1.0040 1.0090 1.0180 1.0170
9      2      3 1.494099 1.0333284 0.9707544    0.9783 0.9998 1.0030 1.0030
10     2      4 1.494099 0.9884828 0.9707544    0.9542 0.9667 0.9720 0.9709
11     2      5 1.494099 0.9823242 0.9707544    0.9489 0.9643 0.9677 0.9679
12     2      6 1.494099 0.9791870 0.9707544    0.9524 0.9630 0.9674 0.9650
13     2      7 1.494099 0.9778322 0.9707544    0.9539 0.9670 0.9702 0.9713
   3rd Qu    max % < best
1  1.1210 1.1310        0
2  1.0230 1.0260        0
3  1.0200 1.0250        0
4  1.0160 1.0230        0
5  1.0150 1.0280        0
6  1.0200 1.0290        0
7  1.0220 1.0390        0
8  1.0220 1.0320        0
9  1.0090 1.0150        0
10 0.9755 0.9845       40
11 0.9714 0.9795       68
12 0.9686 0.9757       84
13 0.9779 0.9894       56
> # A table summarizing the permutation tests
> #
> # a greedy sequence
> # logreg.savefit6 <- logreg(select = 6, ntrees = 2, nleaves =c(1,12), oldfit = logreg.savefit1)
> print(logreg.savefit6)
2 trees with 0 leaves: score is 1.494 
 +0 * 1
2 trees with 1 leaves: score is 1.15 
 -1.91 * X3
2 trees with 2 leaves: score is 1.048 
 -2.13 * (X3 or X4)
2 trees with 3 leaves: score is 1.033 
 -2.13 * (X3 or X4) +0.401 * X1
2 trees with 4 leaves: score is 0.988 
 -2.12 * (X3 or X4) +1.11 * (X1 or X2)
2 trees with 5 leaves: score is 0.982 
 -2.12 * (X3 or X4) +1.22 * ((X1 or X20) or X2)
2 trees with 6 leaves: score is 0.981 
 -2.14 * (X3 or (X4 and X13)) +0 * ((X1 or X20) or X2)
2 trees with 7 leaves: score is 0.98 
 +0 * (X3 or (X4 and X13)) +0 * ((X1 or X20) or (X2 and (not X4)))
> 
> 
> 
> cleanEx(); ..nameEx <- "print.logregmodel"
> 
> ### * print.logregmodel
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.logregmodel
> ### Title: Prints Logic Regression Formula
> ### Aliases: print.logregmodel
> ### Keywords: logic methods nonparametric tree
> 
> ### ** Examples
> 
> data(logreg.savefit1)
> #
> # myanneal <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 1000)
> # logreg.savefit1 <- logreg(resp = logreg.testdat[,1], bin=logreg.testdat[, 2:21],
> #                type = 2, select = 1, ntrees = 2, anneal.control = myanneal)
> print(logreg.savefit1$model) 
 -1.3 * (((X14 or (not X5)) and ((not X1) and (not X2))) and (((not X3) or X1) or ((not X20) and (not X2)))) +2.15 * (((not X4) or ((not X13) and (not X11))) and (not X3))
> 
> 
> 
> cleanEx(); ..nameEx <- "print.logregtree"
> 
> ### * print.logregtree
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: print.logregtree
> ### Title: Prints Logic Regression Formula
> ### Aliases: print.logregtree
> ### Keywords: logic methods nonparametric tree
> 
> ### ** Examples
> 
> data(logreg.savefit1)
> #
> # myanneal <- logreg.anneal.control(start = -1, end = -4, iter = 25000, update = 1000)
> # logreg.savefit1 <- logreg(resp = logreg.testdat[,1], bin=logreg.testdat[, 2:21],
> #                type = 2, select = 1, ntrees = 2, anneal.control = myanneal)
> print(logreg.savefit1$model$trees[[1]]) 
(((X14 or (not X5)) and ((not X1) and (not X2))) and (((not X3) or X1) or ((not X20) and (not X2))))> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  45.17 1.06 46.77 0.02 0.09 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')