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("gam-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('gam')
Loading required package: splines
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "anova.gam"
> 
> ### * anova.gam
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: anova.gam
> ### Title: Analysis of Deviance for a Generalized Additive Model
> ### Aliases: anova.gam summary.gam
> ### Keywords: models regression nonparametric
> 
> ### ** Examples
> 
> data(gam.data)
> gam.object <- gam(y~s(x,6)+z,data=gam.data)
> anova(gam.object)

DF for Terms and F-values for Nonparametric Effects

            Df Npar Df Npar F     Pr(F)    
(Intercept)  1                             
s(x, 6)      1       5 28.695 < 2.2e-16 ***
z            1                             
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> gam.object2 <- update(gam.object, ~.-z)
> anova(gam.object, gam.object2, test="Chisq")
Analysis of Deviance Table

Model 1: y ~ s(x, 6) + z
Model 2: y ~ s(x, 6)
  Resid. Df Resid. Dev Df Deviance P(>|Chi|)
1        92     7.5745                      
2        93     7.6444 -1  -0.0699    0.3568
> 
> 
> 
> cleanEx(); ..nameEx <- "gam"
> 
> ### * gam
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gam
> ### Title: Fitting Generalized Additive Models
> ### Aliases: gam gam.fit
> ### Keywords: models regression nonparametric
> 
> ### ** Examples
> 
> data(kyphosis)
> gam(Kyphosis ~ s(Age,4) + Number, family = binomial, data=kyphosis,
+ trace=TRUE)
GAM s.wam loop 1: deviance = 66.42083 
GAM s.wam loop 2: deviance = 63.77248 
GAM s.wam loop 3: deviance = 63.2519 
GAM s.wam loop 4: deviance = 63.13396 
GAM s.wam loop 5: deviance = 63.11019 
GAM s.wam loop 6: deviance = 63.10754 
GAM s.wam loop 7: deviance = 63.10733 
GAM s.wam loop 8: deviance = 63.10732 
GAM s.wam loop 9: deviance = 63.10732 
Call:
gam(formula = Kyphosis ~ s(Age, 4) + Number, family = binomial, 
    data = kyphosis, trace = TRUE)

Degrees of Freedom: 80 total; 75.00015 Residual
Residual Deviance: 63.10732 
> data(airquality)
> gam(Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp), data=airquality, na=na.gam.replace)
Warning in function (frame)  : 37 observations omitted due to missing values in the response
Call:
gam(formula = Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp), data = airquality, 
    na.action = na.gam.replace)

Degrees of Freedom: 115 total; 102.6761 Residual
Residual Deviance: 20.50298 
> gam(Kyphosis ~ poly(Age,2) + s(Start), data=kyphosis, family=binomial, subset=Number>2)
Call:
gam(formula = Kyphosis ~ poly(Age, 2) + s(Start), family = binomial, 
    data = kyphosis, subset = Number > 2)

Degrees of Freedom: 68 total; 61.9999 Residual
Residual Deviance: 44.56368 
> data(gam.data)
> gam.object <- gam(y ~ s(x,6) + z,data=gam.data)
> summary(gam.object)

Call: gam(formula = y ~ s(x, 6) + z, data = gam.data)
Deviance Residuals:
     Min       1Q   Median       3Q      Max 
-0.59177 -0.18050  0.01291  0.23940  0.51412 

(Dispersion Parameter for gaussian family taken to be 0.0823)

    Null Deviance: 57.7496 on 99 degrees of freedom
Residual Deviance: 7.5745 on 92 degrees of freedom
AIC: 43.7497 

Number of Local Scoring Iterations: 2 

DF for Terms and F-values for Nonparametric Effects

            Df Npar Df Npar F     Pr(F)    
(Intercept)  1                             
s(x, 6)      1       5 28.695 < 2.2e-16 ***
z            1                             
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> plot(gam.object,se=TRUE)
> data(gam.newdata)
> predict(gam.object,type="terms",newdata=gam.newdata)
       s(x, 6)            z
1   0.44378432  0.039427729
2   0.75359604  0.028567521
3   0.85469236  0.017707313
4   0.80512152  0.006847105
5   0.51279083 -0.004013103
6   0.07523078 -0.014873311
7  -0.42621357 -0.025733519
8  -0.80004353 -0.036593727
9  -0.96322154 -0.047453935
10 -1.01599595 -0.058314143
attr(,"constant")
[1] 0.634896
> 
> 
> 
> cleanEx(); ..nameEx <- "gam.control"
> 
> ### * gam.control
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gam.control
> ### Title: Auxilliary for controlling GAM fitting
> ### Aliases: gam.control
> ### Keywords: models regression nonparametric
> 
> ### ** Examples
> 
> ## Not run: gam(formula, family, control = gam.control(bf.maxit=15))
> ## Not run: gam(formula, family, bf.maxit = 15) # these are equivalent
> 
> 
> 
> cleanEx(); ..nameEx <- "gam.data"
> 
> ### * gam.data
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gam.data
> ### Title: Simulated dataset for gam
> ### Aliases: gam.data gam.newdata
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(gam.data)
> gam(y ~ s(x) + z, data=gam.data)
Call:
gam(formula = y ~ s(x) + z, data = gam.data)

Degrees of Freedom: 99 total; 93.99996 Residual
Residual Deviance: 7.90771 
> 
> 
> 
> cleanEx(); ..nameEx <- "lo"
> 
> ### * lo
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lo
> ### Title: Specify a loess fit in a GAM formula
> ### Aliases: lo gam.lo
> ### Keywords: models regression nonparametric
> 
> ### ** Examples
> 
> y ~ Age + lo(Start, span=.5)
y ~ Age + lo(Start, span = 0.5)
>      # fit Start using a loess smooth with a span of 0.5.
> y ~ lo(Age) + lo(Start, Number) 
y ~ lo(Age) + lo(Start, Number)
> y ~ lo(Age, 0.5) # the argument name for span is not needed.
y ~ lo(Age, 0.5)
> 
> 
> 
> cleanEx(); ..nameEx <- "na.gam.replace"
> 
> ### * na.gam.replace
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: na.gam.replace
> ### Title: Missing Data Filter for GAMs
> ### Aliases: na.gam.replace
> ### Keywords: models regression nonparametric
> 
> ### ** Examples
> 
> data(airquality)
> gam(Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp), data=airquality, na=na.gam.replace)
Warning in function (frame)  : 37 observations omitted due to missing values in the response
Call:
gam(formula = Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp), data = airquality, 
    na.action = na.gam.replace)

Degrees of Freedom: 115 total; 102.6761 Residual
Residual Deviance: 20.50298 
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.gam"
> 
> ### * plot.gam
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.gam
> ### Title: Plot Components of a GAM Object
> ### Aliases: plot.gam preplot.gam plot.preplot.gam
> ### Keywords: models regression nonparametric
> 
> ### ** Examples
> 
> data(gam.data)
> gam.object <- gam(y ~ s(x,6) + z,data=gam.data)
> plot(gam.object,se=TRUE)
> data(gam.newdata)
> preplot(gam.object,newdata=gam.newdata)
Warning in newdata.predict.gam(object, newdata, type, dispersion, se.fit,  : 
	 No standard errors (currently) for gam predictions with newdata
$"s(x, 6)"
$x
 [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

$y
          1           2           3           4           5           6 
 0.44378432  0.75359604  0.85469236  0.80512152  0.51279083  0.07523078 
          7           8           9          10 
-0.42621357 -0.80004353 -0.96322154 -1.01599595 

$se.y
NULL

$xlab
[1] "x"

$ylab
[1] "s(x, 6)"

attr(,"class")
[1] "preplot.gam"

$z
$x
 [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

$y
           1            2            3            4            5            6 
 0.039427729  0.028567521  0.017707313  0.006847105 -0.004013103 -0.014873311 
           7            8            9           10 
-0.025733519 -0.036593727 -0.047453935 -0.058314143 

$se.y
NULL

$xlab
[1] "z"

$ylab
[1] "partial for z"

attr(,"class")
[1] "preplot.gam"

attr(,"class")
[1] "preplot.gam"
> 
> 
> 
> cleanEx(); ..nameEx <- "predict.gam"
> 
> ### * predict.gam
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: predict.gam
> ### Title: Predict method for GAM fits
> ### Aliases: predict.gam
> ### Keywords: models regression nonparametric
> 
> ### ** Examples
> 
> data(gam.data)
> gam.object <- gam(y ~ s(x,6) + z, data=gam.data)
> predict(gam.object) # extract the additive predictors
          1           2           3           4           5           6 
 0.80174236  1.08594720  1.31185931 -0.06023088  1.03175402  1.18776824 
          7           8           9          10          11          12 
-0.34196089  0.91816180  1.14185041 -0.29130792 -0.39471934  1.44862019 
         13          14          15          16          17          18 
-0.10965211  0.47271279  0.59043489  1.42774901  1.35225538  1.28011864 
         19          20          21          22          23          24 
-0.37702822  1.47743529  1.38816587  1.14343124 -0.15894245 -0.25786206 
         25          26          27          28          29          30 
 1.41672592 -0.26040124  0.16411710 -0.27613061  1.48808923 -0.01668390 
         31          32          33          34          35          36 
-0.08602763  1.51644195 -0.32403266 -0.18498080  1.30911786 -0.13643300 
         37          38          39          40          41          42 
-0.26455800  1.46929420  0.86226736  0.15782227  0.13535744  0.01135680 
         43          44          45          46          47          48 
 0.51406338  1.06271839 -0.29758233  1.45362893  1.03180497  1.49352792 
         49          50          51          52          53          54 
 0.77868272  1.46033254 -0.30039613 -0.33160621  0.73381631  1.47103493 
         55          56          57          58          59          60 
-0.32700989  1.43849166 -0.31925805  1.50499770 -0.24600726  0.43842092 
         61          62          63          64          65          66 
 1.44918383 -0.16657598  0.74918250  0.60880911  1.34655176 -0.33211608 
         67          68          69          70          71          72 
-0.34074073  0.12581265  0.38596596  0.56651408 -0.32542086  1.34193342 
         73          74          75          76          77          78 
 0.87463872  0.89007891  1.39620365  1.49045042 -0.27479459  1.38673785 
         79          80          81          82          83          84 
 1.41351374  1.45097308 -0.29389320  0.96967205  1.44159356  1.38500100 
         85          86          87          88          89          90 
 0.67737547  0.92695518 -0.33725594  1.52324210 -0.32514876  0.56679832 
         91          92          93          94          95          96 
 1.32211422  0.85794848  0.78036577  1.20135834  1.37958726  1.02704183 
         97          98          99         100 
 0.55290083  1.45672096 -0.32176147  0.05277387 
> data(gam.newdata)
> predict(gam.object, gam.newdata, type="terms") 
       s(x, 6)            z
1   0.44378432  0.039427729
2   0.75359604  0.028567521
3   0.85469236  0.017707313
4   0.80512152  0.006847105
5   0.51279083 -0.004013103
6   0.07523078 -0.014873311
7  -0.42621357 -0.025733519
8  -0.80004353 -0.036593727
9  -0.96322154 -0.047453935
10 -1.01599595 -0.058314143
attr(,"constant")
[1] 0.634896
> 
> 
> 
> cleanEx(); ..nameEx <- "s"
> 
> ### * s
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: s
> ### Title: Specify a Smoothing Spline Fit in a GAM Formula
> ### Aliases: s gam.s
> ### Keywords: models regression nonparametric
> 
> ### ** Examples
> 
> # fit Start using a smoothing spline with 4 df.
> y ~ Age + s(Start, 4)
y ~ Age + s(Start, 4)
> # fit log(Start) using a smoothing spline with 5 df.
> y ~ Age + s(log(Start), df=5)
y ~ Age + s(log(Start), df = 5)
> 
> 
> 
> cleanEx(); ..nameEx <- "step.gam"
> 
> ### * step.gam
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: step.gam
> ### Title: Stepwise model builder for GAM
> ### Aliases: step.gam
> ### Keywords: models regression nonparametric
> 
> ### ** Examples
> 
> data(gam.data)
> gam.object <- gam(y~x+z, data=gam.data)
> step.object <-step.gam(gam.object, scope=list("x"=~1+x+s(x,4)+s(x,6)+s(x,12),"z"=~1+z+s(z,4)))
Start:  y ~ x + z; AIC= 127.7316 
Trial:  y ~  1 + z; AIC= 234.8821 
Trial:  y ~  s(x, 4) + z; AIC= 44.0546 
Trial:  y ~  x + 1; AIC= 126.5148 
Trial:  y ~  x + s(z, 4); AIC= 131.0277 
Step :  y ~ s(x, 4) + z ; AIC= 44.0546 

Trial:  y ~  s(x, 6) + z; AIC= 43.7497 
Trial:  y ~  s(x, 4) + 1; AIC= 43.1801 
Trial:  y ~  s(x, 4) + s(z, 4); AIC= 47.1022 
Step :  y ~ s(x, 4) ; AIC= 43.1801 

Trial:  y ~  s(x, 6) + 1; AIC= 42.6683 
Step :  y ~ s(x, 6) ; AIC= 42.6683 

Trial:  y ~  s(x, 12) + 1; AIC= 45.5601 
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  1.17 0.1 1.28 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')