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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("VLMC-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('VLMC')
Loading required package: MASS
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "OZrain"
> 
> ### * OZrain
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: OZrain
> ### Title: Daily Rainfall in Melbourne, Australia, 1981-1990
> ### Aliases: OZrain
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(OZrain)
> (n <- length(OZrain)) ## should be 1 more than
[1] 3653
> ISOdate(1990,12,31) - ISOdate(1981, 1,1)## but it's 2 ..
Time difference of 3651 days
> 
> has.rain <- OZrain > 0
> 
> summary(OZrain[has.rain])# Median = 18,  Q3 = 50
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2.00    6.00   18.00   43.63   50.00  692.00 
> table(rain01 <- as.integer(has.rain))

   0    1 
2237 1416 
> table(rain4c <- cut(OZrain, c(-.1, 0.5, 18.5, 50.1, 1000)))

  (-0.1,0.5]   (0.5,18.5]  (18.5,50.1] (50.1,1e+03] 
        2237          713          356          347 
> 
> ## Don't show:
>   table(rain4c <- as.integer(rain4c) - 1:1)

   0    1    2    3 
2237  713  356  347 
>   draw(v4 <- vlmc(rain4c))
[x]-( 2236 713 356 347| 3652)-F
 +--[0]-( 1613 316 148 159| 2236) <59.03>
 |   `--[0]-( 1185 210 99 118| 1612) <1.26>
 |       +--[0]-( 876 149 71 88| 1184) <0.14>
 |       |   +--[0]-( 654 101 59 62| 876) <0.88>
 |       |   |   `--[0]-( 495 70 48 41| 654) <0.73>
 |       |   |       `--[2]-( 17 9 2 1| 29) <4.51>-T
 |       |   +--[1]-( 147 27 8 15| 197) <0.80>
 |       |   |   `--[0]-( 66 8 3 7| 84) <0.74>
 |       |   |       `--[0]-( 43 4 3 4| 54) <0.42>
 |       |   |           `--[3]-( 0 2 1 0| 3) <6.19>-T
 |       |   `--[2]-( 43 13 0 7| 63) <5.78>-T
 |       `--[3]-( 48 9 4 3| 64) <0.38>
 |           `--[0]-( 24 3 4 2| 33) <1.12>
 |               `--[1]-( 1 3 1 1| 6) <4.97>-T
 +--[1]-( 375 164 99 75| 713) <12.55>
 |   +--[0]-( 168 66 43 39| 316) <0.81>
 |   |   `--[0]-( 107 41 29 33| 210) <1.09>
 |   |       `--[0]-( 76 28 20 25| 149) <0.08>
 |   |           `--[0]-( 59 18 9 15| 101) <1.53>
 |   |               `--[0]-( 39 12 8 11| 70) <0.30>
 |   |                   `--[0]-( 28 10 4 7| 49) <0.44>
 |   |                       `--[0]-( 23 9 3 4| 39) <0.33>
 |   |                           `--[2]-( 0 0 0 2| 2) <4.55>-T
 |   `--[1]-( 87 42 22 13| 164) <0.82>
 |       `--[0]-( 34 16 8 8| 66) <0.71>
 |           `--[0]-( 18 9 7 7| 41) <1.02>
 |               `--[0]-( 13 6 3 6| 28) <0.55>
 |                   `--[2]-( 0 3 0 1| 4) <3.91>-T
 +--[2]-( 146 113 48 49| 356) <30.14>
 |   +--[0]-( 54 39 22 33| 148) <4.49>-T
 |   +--[1]-( 43 36 13 7| 99) <2.36>
 |   |   `--[1]-( 10 8 0 4| 22) <4.23>-T
 |   `--[3]-( 27 21 7 6| 61) <0.62>
 |       `--[3]-( 0 3 3 0| 6) <5.54>-T
 `--[3]-( 102 120 61 64| 347) <72.18>
     +--[0]-( 55 48 27 29| 159) <1.17>
     |   `--[0]-( 41 36 20 21| 118) <0.01>
     |       `--[1]-( 2 3 6 6| 17) <4.70>-T
     `--[2]-( 11 19 16 3| 49) <5.81>-T
>   v4 # cutoff 3.907; MC = 9, context = 39

`vlmc', a Variable Length Markov Chain;
	 alphabet '0123', |alphabet| = 4, n = 3653.
Call: vlmc(dts = rain4c);	default cutoff = 3.907
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        9        39        11       322 
AIC =  7485 
>   vlmc(rain4c, cut = 0  )$size # MC = 25; context = 2355, #{leaves}= 1062
   ord.MC   context nr.leaves     total 
       25      2355      1062     20018 
>   vlmc(rain4c, cut = 0.1)$size # MC = 25; context = 2344
   ord.MC   context nr.leaves     total 
       25      2344      1054     19930 
>   vlmc(rain4c, cut = 0.3)$size # MC = 25; context = 1990, #{leaves}=  967
   ord.MC   context nr.leaves     total 
       25      1990       967     17074 
>   vlmc(rain4c, cut = 6.0)$size # MC =  7; context =   10, #{leaves}=    4
   ord.MC   context nr.leaves     total 
        7        10         4        90 
>   lLv <- function(co) {
+     l <- logLik(vlmc(rain4c, cut = co)); c(Ll = l, df = attr(l,"df"))
+   }
>   cuts4 <- c(0,0.1,0.3,0.5,0.7, seq(1,7, by = .25), 9, 12,13)
>   ll4 <- sapply(cuts4, lLv)
>   par(mfcol = c(2,2))
>   plot(cuts4, AIC4 <- c(c(-2,2) %*% ll4), type = 'b')
>   plot(cuts4, BIC4 <- c(c(-2,log(n)) %*% ll4), type = 'b')
>   ii <- cuts4 > 2.
>   plot(cuts4[ii], AIC4[ii], type = 'b')
>   plot(cuts4[ii], BIC4[ii], type = 'b')
> ## End Don't show
> 
> AIC(v1  <- vlmc(rain01))# cutoff = 1.92
[1] 4547.764
> AIC(v00 <- vlmc(rain01, cut = 1.4))
[1] 4560.286
> AIC(v0  <- vlmc(rain01, cut = 1.5))
[1] 4554.815
> 
> ## Don't show:
>   lLv <- function(co) {
+     cat(co,"")
+     l <- logLik(vlmc(rain01, cut = co))
+     c(Ll = l, df = attr(l,"df"))
+   }
>   cc <- seq(1., 3, len = 401)
>   system.time(vll <- sapply(cc, lLv))# 21 & 49 sec. (on P III 900 MHz)
1 1.005 1.01 1.015 1.02 1.025 1.03 1.035 1.04 1.045 1.05 1.055 1.06 1.065 1.07 1.075 1.08 1.085 1.09 1.095 1.1 1.105 1.11 1.115 1.12 1.125 1.13 1.135 1.14 1.145 1.15 1.155 1.16 1.165 1.17 1.175 1.18 1.185 1.19 1.195 1.2 1.205 1.21 1.215 1.22 1.225 1.23 1.235 1.24 1.245 1.25 1.255 1.26 1.265 1.27 1.275 1.28 1.285 1.29 1.295 1.3 1.305 1.31 1.315 1.32 1.325 1.33 1.335 1.34 1.345 1.35 1.355 1.36 1.365 1.37 1.375 1.38 1.385 1.39 1.395 1.4 1.405 1.41 1.415 1.42 1.425 1.43 1.435 1.44 1.445 1.45 1.455 1.46 1.465 1.47 1.475 1.48 1.485 1.49 1.495 1.5 1.505 1.51 1.515 1.52 1.525 1.53 1.535 1.54 1.545 1.55 1.555 1.56 1.565 1.57 1.575 1.58 1.585 1.59 1.595 1.6 1.605 1.61 1.615 1.62 1.625 1.63 1.635 1.64 1.645 1.65 1.655 1.66 1.665 1.67 1.675 1.68 1.685 1.69 1.695 1.7 1.705 1.71 1.715 1.72 1.725 1.73 1.735 1.74 1.745 1.75 1.755 1.76 1.765 1.77 1.775 1.78 1.785 1.79 1.795 1.8 1.805 1.81 1.815 1.82 1.825 1.83 1.835 1.84 1.845 1.85 1.855 1.86 1.865 1.87 1.875 1.88 1.885 1.89 1.895 1.9 1.905 1.91 1.915 1.92 1.925 1.93 1.935 1.94 1.945 1.95 1.955 1.96 1.965 1.97 1.975 1.98 1.985 1.99 1.995 2 2.005 2.01 2.015 2.02 2.025 2.03 2.035 2.04 2.045 2.05 2.055 2.06 2.065 2.07 2.075 2.08 2.085 2.09 2.095 2.1 2.105 2.11 2.115 2.12 2.125 2.13 2.135 2.14 2.145 2.15 2.155 2.16 2.165 2.17 2.175 2.18 2.185 2.19 2.195 2.2 2.205 2.21 2.215 2.22 2.225 2.23 2.235 2.24 2.245 2.25 2.255 2.26 2.265 2.27 2.275 2.28 2.285 2.29 2.295 2.3 2.305 2.31 2.315 2.32 2.325 2.33 2.335 2.34 2.345 2.35 2.355 2.36 2.365 2.37 2.375 2.38 2.385 2.39 2.395 2.4 2.405 2.41 2.415 2.42 2.425 2.43 2.435 2.44 2.445 2.45 2.455 2.46 2.465 2.47 2.475 2.48 2.485 2.49 2.495 2.5 2.505 2.51 2.515 2.52 2.525 2.53 2.535 2.54 2.545 2.55 2.555 2.56 2.565 2.57 2.575 2.58 2.585 2.59 2.595 2.6 2.605 2.61 2.615 2.62 2.625 2.63 2.635 2.64 2.645 2.65 2.655 2.66 2.665 2.67 2.675 2.68 2.685 2.69 2.695 2.7 2.705 2.71 2.715 2.72 2.725 2.73 2.735 2.74 2.745 2.75 2.755 2.76 2.765 2.77 2.775 2.78 2.785 2.79 2.795 2.8 2.805 2.81 2.815 2.82 2.825 2.83 2.835 2.84 2.845 2.85 2.855 2.86 2.865 2.87 2.875 2.88 2.885 2.89 2.895 2.9 2.905 2.91 2.915 2.92 2.925 2.93 2.935 2.94 2.945 2.95 2.955 2.96 2.965 2.97 2.975 2.98 2.985 2.99 2.995 3 [1]  9.83  0.56 10.48  0.00  0.00
>   llv <- vll["Ll",] ; dfv <- vll["df",]
>   par(mfrow=c(2,2))
>   ## very interesting:  quite a few local minima!!
>   plot  (cc, -2*llv + 2*dfv, type='l', main="AIC(vlmc(rain01, cutoff = cc))")
>   plot  (cc, dfv, type='l', main="Complexity(vlmc(*, cutoff = cc))")
>   plot  (cc, llv, type='l', main="Log.Likelihood(vlmc(*, cutoff = cc))")
> 
>   ## BIC is all falling:
>   ##  par(new=T)
>   plot  (cc, -2*llv + log(3653)*dfv, type='l', main="BIC(vlmc(*, cutoff = cc))")
> ## End Don't show
> 
> hist(OZrain)
> hist(OZrain, breaks = c(0,1,5,10,50,1000), xlim = c(0,100))
> 
> plot(OZrain, main = "Rainfall 1981-1990 in Melbourne")
> ## work around bug in plot.ts() for R <= 1.3.1 :
> newer1.4 <- {v <- R.version; v$major > 1 || as.numeric(v$minor) >= 4.2}
> if(newer1.4) {
+   plot(OZrain, log="y", main = "Non-0 Rainfall [LOG scale]")
+ } else
+   plot.ts(time(OZrain)[has.rain],
+           OZrain[has.rain], log="y", main = "Non-0 Rainfall [LOG scale]")
Warning in xy.coords(x, NULL, log = log) : 2237 y values <= 0 omitted from logarithmic plot
> lOZ <- lowess(log10(OZrain[has.rain]), f= .05)
> lines(time(OZrain)[has.rain], 10^lOZ$y, col = 2, lwd = 2)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "RCplot"
> 
> ### * RCplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Encoding: latin1
> 
> ### Name: RCplot
> ### Title: Residuals vs Context plot
> ### Aliases: RCplot
> ### Keywords: hplot utilities
> 
> ### ** Examples
> 
> example(vlmc)

vlmc> f1 <- c(1, 0, 0, 0)

vlmc> f2 <- rep(1:0, 2)

vlmc> (dt1 <- c(f1, f1, f2, f1, f2, f2, f1))
 [1] 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0

vlmc> (vlmc.dt1 <- vlmc(dt1))

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1);	default cutoff = 1.921
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        3         4         3        26 
AIC =  21.46 

vlmc> vlmc(dt1, dump = 1, ctl.dump = c(wid = 3, nmax = 20), 
    debug = TRUE)
vlmc: Alpha = '01' ; |X| = 2
vlmc: ctl.dump =  3 20 
vlmc: n = |data| = 28, cutoff{prune} = 1.92073, threshold{gen} = 2
vlmc: |alphabet| = 2, alphabet = 01
generating... pruning... Dump{Tree} __after__ pruning: 
Lev Ch|   0   1 |  tot |  num size : ..{set->list}..
------+--------------------------------------------------
  0 x |  18  10 |   28 |   28   32 : 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ..
  1 0 |   8   9 |   17 |   17   32 : 2 3 4 6 7 8 10 12 14 15 16 18 20 22 24 26 27
  2 0 |   4   3 |    7 |    7   16 : 3 4 7 8 15 16 27
  3 0 |   0   3 |    3 |    3   16 : 4 8 16
  3 1 |   4   0 |    4 |    4   16 : 3 7 15 27
  1 1 |  10   0 |   10 |   10   16 : 1 5 9 11 13 17 19 21 23 25
computing differences[`completing'] ... writing tree...
[0]01
0 0 0
[1]1 4 6
[2]2 0 0
[3]3 0 3
- -1
- -1

[3]3 4 0
- -1
- -1


- -1

[1]1 10 0
- -1
- -1



`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, debug = TRUE, dump = 1, ctl.dump = c(wid = 3,     nmax = 20));	default cutoff = 1.921
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        3         4         3        26 
AIC =  21.46 

vlmc> (vlmc.dt1c01 <- vlmc(dts = dt1, cutoff.prune = 0.1, 
    dump = 1))
Lev Ch|  0  1 | tot | num size : ..{set->list}..
------+---------------------------------------------
  0 x | 18 10 |  28 |  28  32 : 0 1 2 3 4 5 6 7 8 9 10 11 12 ..
  1 0 |  8  9 |  17 |  17  32 : 2 3 4 6 7 8 10 12 14 15 16 18 20 ..
  2 0 |  4  3 |   7 |   7  16 : 3 4 7 8 15 16 27
  3 0 |  0  3 |   3 |   3  16 : 4 8 16
  3 1 |  4  0 |   4 |   4  16 : 3 7 15 27
  2 1 |  4  6 |  10 |  10  16 : 2 6 10 12 14 18 20 22 24 26
  3 0 |  3  6 |   9 |   9  16 : 6 10 12 14 18 20 22 24 26
  4 0 |  1  2 |   3 |   3  16 : 6 10 18
  5 0 |  1  2 |   3 |   3  16 : 6 10 18
  6 1 |  1  2 |   3 |   3  16 : 6 10 18
  7 0 |  0  2 |   2 |   2  16 : 10 18
  4 1 |  2  4 |   6 |   6  16 : 12 14 20 22 24 26
  5 0 |  2  4 |   6 |   6  16 : 12 14 20 22 24 26
  6 0 |  0  2 |   2 |   2  16 : 12 20
  6 1 |  2  2 |   4 |   4  16 : 14 22 24 26
  1 1 | 10  0 |  10 |  10  16 : 1 5 9 11 13 17 19 21 23 25

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, cutoff.prune = 0.1, dump = 1)
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        7        11         6        66 
AIC =  27.55 

vlmc> data(presidents)

vlmc> dpres <- cut(presidents, c(0, 45, 70, 100))

vlmc> table(dpres <- factor(dpres, exclude = NULL))

  (0,45]  (45,70] (70,100]     <NA> 
      28       60       26        6 

vlmc> vlmc.pres <- vlmc(dpres, debug = TRUE)
Warning in vlmc(dpres, debug = TRUE) : alphabet with >1-letter strings; trying to abbreviate
vlmc: Alpha = '0123' ; |X| = 4
vlmc: ctl.dump =  3.079181 -1 
vlmc: n = |data| = 120, cutoff{prune} = 3.90736, threshold{gen} = 2
vlmc: |alphabet| = 4, alphabet = 0123
generating... pruning... computing differences[`completing'] ... writing tree...
[0]0123
0 1 2 1 2
[1]1 18 7 0 2
- -1
- -1
- -1
- -1

[1]1 9 33 5 1
- -1
- -1
[2]2 0 6 6 0
- -1
- -1
- -1
- -1

- -1

[1]1 0 12 14 0
- -1
- -1
- -1
- -1

- -1


vlmc> vlmc.pres

`vlmc', a Variable Length Markov Chain;
	 alphabet '0123', |alphabet| = 4, n = 120.
Call: vlmc(dts = dpres, debug = TRUE);	default cutoff = 3.907
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        2         5         3        42 
AIC =  227.6 

vlmc> vlmc.pres$alpha
[1] "0123"

vlmc> stopifnot(length(print(strsplit(vlmc.pres$alpha, NULL)[[1]])) == 
    vlmc.pres$alpha.len)
[1] "0" "1" "2" "3"
> RCplot(vlmc.pres)
> RCplot(vlmc.dt1c01)## << almost perfect fit (0 resid.)
> 
> 
> 
> cleanEx(); ..nameEx <- "alpha2int"
> 
> ### * alpha2int
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: alpha2int
> ### Title: `Single Character' <-> Integer Conversion for Discrete Data
> ### Aliases: alpha2int int2alpha
> ### Keywords: character utilities
> 
> ### ** Examples
> 
> alphabet <- "abcdefghijk"
> (ch <- sample(letters[1:10], 30, replace = TRUE))
 [1] "c" "d" "f" "j" "c" "i" "j" "g" "g" "a" "c" "b" "g" "d" "h" "e" "h" "j" "d"
[20] "h" "j" "c" "g" "b" "c" "d" "a" "d" "i" "d"
> (ic <- alpha2int(ch, alphabet))
 [1] 2 3 5 9 2 8 9 6 6 0 2 1 6 3 7 4 7 9 3 7 9 2 6 1 2 3 0 3 8 3
> stopifnot(int2alpha(ic, alphabet) == ch)
> 
> 
> 
> cleanEx(); ..nameEx <- "alphabet"
> 
> ### * alphabet
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: alphabet
> ### Title: The Alphabet in Use
> ### Aliases: alphabet alphabet.vlmc
> ### Keywords: character utilities
> 
> ### ** Examples
> 
> data(bnrf1)
> vb <- vlmc(bnrf1EB, cutoff = 5)
> alphabet(vb) # |--> "a" "c" "g" "t"
[1] "a" "c" "g" "t"
> 
> 
> 
> cleanEx(); ..nameEx <- "as.dendrogram.vlmc"
> 
> ### * as.dendrogram.vlmc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: as.dendrogram.vlmc
> ### Title: Dendrogram Construction from VLMCs
> ### Aliases: as.dendrogram.vlmc
> ### Keywords: graphs iplot
> 
> ### ** Examples
> 
> data(presidents)
> dpr <- factor(cut(presidents, c(0,45,70,100)), exclude=NULL)# NA = 4th level
> (vlmc.pres <- vlmc(dpr))
Warning in vlmc(dpr) : alphabet with >1-letter strings; trying to abbreviate

`vlmc', a Variable Length Markov Chain;
	 alphabet '0123', |alphabet| = 4, n = 120.
Call: vlmc(dts = dpr);	default cutoff = 3.907
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        2         5         3        42 
AIC =  227.6 
> draw(vlmc.pres)
[x]-( 28 60 26 5| 119)
 +--[0]-( 18 7 0 2| 27) <15.22>-T
 +--[1]-( 9 39 11 1| 60) <3.00>
 |   `--[2]-( 0 6 6 0| 12) <4.45>-T
 `--[2]-( 0 12 14 0| 26) <11.57>-T
> (dv.dpr <- as.dendrogram(vlmc.pres))
'dendrogram' with 3 branches and 3 members total, at height 2 
> str(dv.dpr)
--[dendrogram w/ 3 branches and 3 members at h = 2]
  |--leaf 27 (h= 1 count = c("18", " 7", " 0", " 2"), edgetext = 0 )
  |--[dendrogram w/ 1 branches and 1 members at h = 1]
  |  `--leaf 12 ( count = c("0", "6", "6", "0"), edgetext = 2 )
  `--leaf 26 (h= 1 count = c(" 0", "12", "14", " 0"), edgetext = 2 )
> str(unclass(dv.dpr))
List of 3
 $ 0: atomic [1:1] 27
  ..- attr(*, "members")= int 1
  ..- attr(*, "leaf")= logi TRUE
  ..- attr(*, "height")= int 1
  ..- attr(*, "count")= int [1:4] 18 7 0 2
  ..- attr(*, "edgetext")= chr "0"
 $ 1:List of 1
  ..$ 2: atomic [1:1] 12
  .. ..- attr(*, "members")= int 1
  .. ..- attr(*, "leaf")= logi TRUE
  .. ..- attr(*, "height")= int 0
  .. ..- attr(*, "count")= int [1:4] 0 6 6 0
  .. ..- attr(*, "edgetext")= chr "2"
  ..- attr(*, "0-kids")= int [1:3] 0 1 3
  ..- attr(*, "members")= int 1
  ..- attr(*, "height")= int 1
  ..- attr(*, "count")= int [1:4] 9 33 5 1
  ..- attr(*, "edgetext")= chr "1"
 $ 2: atomic [1:1] 26
  ..- attr(*, "members")= int 1
  ..- attr(*, "leaf")= logi TRUE
  ..- attr(*, "height")= int 1
  ..- attr(*, "count")= int [1:4] 0 12 14 0
  ..- attr(*, "edgetext")= chr "2"
 - attr(*, "0-kids")= int 3
 - attr(*, "members")= int 3
 - attr(*, "height")= int 2
 - attr(*, "count")= int [1:4] 1 2 1 2
> 
> plot(dv.dpr, type ="tr", nodePar = list(pch=c(1,16), cex = 1.5))
> 
> ## Artificial example
> f1 <- c(1,0,0,0) ; f2 <- rep(1:0, 2)
> (dt1 <- c(f1,f1,f2,f1,f2,f2,f1))
 [1] 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0
> (vlmc.dt1c01 <- vlmc(dts = dt1, cutoff.prune = 0.1))

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, cutoff.prune = 0.1)
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        7        11         6        66 
AIC =  27.55 
> (dvlmc <- as.dendrogram(vlmc.dt1c01))
'dendrogram' with 2 branches and 6 members total, at height 7 
> str(dvlmc)
--[dendrogram w/ 2 branches and 6 members at h = 7]
  |--[dendrogram w/ 2 branches and 5 members at h = 6]
  |  |--[dendrogram w/ 2 branches and 2 members at h = 5]
  |  |  |--leaf 3 (h= 4 count = c("0", "3"), edgetext = 0 )
  |  |  `--leaf 4 (h= 4 count = c("4", "0"), edgetext = 1 )
  |  `--[dendrogram w/ 1 branches and 3 members at h = 5]
  |     `--[dendrogram w/ 2 branches and 3 members at h = 4]
  |        |--[dendrogram w/ 1 branches and 1 members at h = 3]
  |        |  `--[dendrogram w/ 1 branches and 1 members at h = 2]
  |        |     `--[dendrogram w/ 1 branches and 1 members at h = 1]
  |        |        `--leaf 2 ( count = c("0", "2"), edgetext = 0 )
  |        `--[dendrogram w/ 1 branches and 2 members at h = 3]
  |           `--[dendrogram w/ 2 branches and 2 members at h = 2]
  |              |--leaf 2 (h= 1 count = c("0", "2"), edgetext = 0 )
  |              `--leaf 4 (h= 1 count = c("2", "2"), edgetext = 1 )
  `--leaf 10 (h= 6 count = c("10", " 0"), edgetext = 1 )
> ## not so useful:
> plot(dvlmc, nodePar= list(pch=c(1,16)))
> ## complete disaster:
> plot(dvlmc, type ="tr", nodePar= list(pch=c(1,16)))
> 
> ## but this is not (yet) so much better (want the same angles to left
> ## and right!!
> plot(dvlmc, type ="tr", nodePar = list(pch=c(1,16)), center=TRUE,
+      main = format(vlmc.dt1c01$call))
> mtext(paste("dt1 =", gsub(" ","",deparse(dt1,width=100))))
> 
> 
> 
> cleanEx(); ..nameEx <- "bnrf1"
> 
> ### * bnrf1
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: bnrf1
> ### Title: BNRF1 Gene DNA sequences: Epstein-Barr and Herpes
> ### Aliases: bnrf1EB bnrf1HV
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(bnrf1)
> bnrf1EB[1:500]
  [1] a t g g a a g a g a g g g g c a g g g a a a c g c a a a t g c c g g t t g
 [38] c c c g g t a t g g g g g c c c g t t t a t t a t g g t a a g g c t c t t
 [75] c g g g c a a g a t g g a g a g g c a a a c a t a c a g g a g g a a a g g
[112] c t a t a t g a g c t a c t c t c t g a c c c a c g c t c c g c g c t c g
[149] g c c t a g a c c c g g g g c c c c t g a t t g c t g a g a a c c t g c t
[186] g c t a g t g g c g c t g c g t g g c a c c a a c a a c g a t c c c a g g
[223] c c t c a g c g t c a g g a g a g g g c c a g a g a a c t g g c c c t c g
[260] t t g g c a t t c t a c t a g g a a a c g g c g a g c a g g g t g a a c a
[297] c t t g g g c a c g g a g a g t g c c c t g g a g g c c t c a g g c a a c
[334] a a c t a t g t g t a t g c c t a c g g a c c a g a c t g g a t g g c a a
[371] g g c c t t c c a c a t g g t c c g c g g a a a t c c a g c a a t t c c t
[408] g c g a c t c c t g g g c g c c a c g t a c g t g c t t c g c g t g g a g
[445] a t g g g c a g g c a g t t t g g c t t c g a g g t g c a t a g a a g c c
[482] g g c c c t c c t t c c g t c a g t t
Levels: a c g t
> table(bnrf1EB)
bnrf1EB
   a    c    g    t 
 744 1195 1232  783 
> table(bnrf1HV)
bnrf1HV
   a    c    g    t 
1172  717  680 1172 
> n <- length(bnrf1HV)
> table(t = bnrf1HV[-1], "t-1" = bnrf1HV[-n])
   t-1
t     a   c   g   t
  a 389 280 200 302
  c 227 134 150 206
  g 256  39 120 265
  t 299 264 210 399
> 
> plot(as.integer(bnrf1EB[1:500]), type = "b")
> ## Don't show:
>  ftable(table( t = bnrf1HV[-(1:2)],
+               "t-1" = bnrf1HV[-c(1,n)],
+               "t-2" = bnrf1HV[-c(n-1,n)]))
      t-2   a   c   g   t
t t-1                    
a a       146  99  71  73
  c        97  47  56  80
  g        88  11  43  58
  t        76  73  49 104
c a        73  49  47  58
  c        37  34  24  39
  g        53   7  34  56
  t        47  48  38  73
g a        94  57  36  69
  c        15   6  10   8
  g        33   4  19  64
  t        93  38  46  88
t a        75  75  46 102
  c        78  47  60  79
  g        82  17  24  87
  t        83 105  77 134
>  lag.plot(jitter(as.ts(bnrf1HV)),lag = 4, pch = ".")
> ## End Don't show
> 
> ## Simplistic gene matching:
> percent.eq <- sapply(0:200,
+            function(i) 100 * sum(bnrf1EB[(1+i):(n+i)] ==  bnrf1HV))/n
> plot.ts(percent.eq)
> 
> 
> 
> cleanEx(); ..nameEx <- "deviance.vlmc"
> 
> ### * deviance.vlmc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: deviance.vlmc
> ### Title: Compute the Deviance of a Fitted VLMC Object
> ### Aliases: deviance.vlmc
> ### Keywords: ts models
> 
> ### ** Examples
> 
> example(vlmc)

vlmc> f1 <- c(1, 0, 0, 0)

vlmc> f2 <- rep(1:0, 2)

vlmc> (dt1 <- c(f1, f1, f2, f1, f2, f2, f1))
 [1] 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0

vlmc> (vlmc.dt1 <- vlmc(dt1))

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1);	default cutoff = 1.921
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        3         4         3        26 
AIC =  21.46 

vlmc> vlmc(dt1, dump = 1, ctl.dump = c(wid = 3, nmax = 20), 
    debug = TRUE)
vlmc: Alpha = '01' ; |X| = 2
vlmc: ctl.dump =  3 20 
vlmc: n = |data| = 28, cutoff{prune} = 1.92073, threshold{gen} = 2
vlmc: |alphabet| = 2, alphabet = 01
generating... pruning... Dump{Tree} __after__ pruning: 
Lev Ch|   0   1 |  tot |  num size : ..{set->list}..
------+--------------------------------------------------
  0 x |  18  10 |   28 |   28   32 : 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ..
  1 0 |   8   9 |   17 |   17   32 : 2 3 4 6 7 8 10 12 14 15 16 18 20 22 24 26 27
  2 0 |   4   3 |    7 |    7   16 : 3 4 7 8 15 16 27
  3 0 |   0   3 |    3 |    3   16 : 4 8 16
  3 1 |   4   0 |    4 |    4   16 : 3 7 15 27
  1 1 |  10   0 |   10 |   10   16 : 1 5 9 11 13 17 19 21 23 25
computing differences[`completing'] ... writing tree...
[0]01
0 0 0
[1]1 4 6
[2]2 0 0
[3]3 0 3
- -1
- -1

[3]3 4 0
- -1
- -1


- -1

[1]1 10 0
- -1
- -1



`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, debug = TRUE, dump = 1, ctl.dump = c(wid = 3,     nmax = 20));	default cutoff = 1.921
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        3         4         3        26 
AIC =  21.46 

vlmc> (vlmc.dt1c01 <- vlmc(dts = dt1, cutoff.prune = 0.1, 
    dump = 1))
Lev Ch|  0  1 | tot | num size : ..{set->list}..
------+---------------------------------------------
  0 x | 18 10 |  28 |  28  32 : 0 1 2 3 4 5 6 7 8 9 10 11 12 ..
  1 0 |  8  9 |  17 |  17  32 : 2 3 4 6 7 8 10 12 14 15 16 18 20 ..
  2 0 |  4  3 |   7 |   7  16 : 3 4 7 8 15 16 27
  3 0 |  0  3 |   3 |   3  16 : 4 8 16
  3 1 |  4  0 |   4 |   4  16 : 3 7 15 27
  2 1 |  4  6 |  10 |  10  16 : 2 6 10 12 14 18 20 22 24 26
  3 0 |  3  6 |   9 |   9  16 : 6 10 12 14 18 20 22 24 26
  4 0 |  1  2 |   3 |   3  16 : 6 10 18
  5 0 |  1  2 |   3 |   3  16 : 6 10 18
  6 1 |  1  2 |   3 |   3  16 : 6 10 18
  7 0 |  0  2 |   2 |   2  16 : 10 18
  4 1 |  2  4 |   6 |   6  16 : 12 14 20 22 24 26
  5 0 |  2  4 |   6 |   6  16 : 12 14 20 22 24 26
  6 0 |  0  2 |   2 |   2  16 : 12 20
  6 1 |  2  2 |   4 |   4  16 : 14 22 24 26
  1 1 | 10  0 |  10 |  10  16 : 1 5 9 11 13 17 19 21 23 25

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, cutoff.prune = 0.1, dump = 1)
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        7        11         6        66 
AIC =  27.55 

vlmc> data(presidents)

vlmc> dpres <- cut(presidents, c(0, 45, 70, 100))

vlmc> table(dpres <- factor(dpres, exclude = NULL))

  (0,45]  (45,70] (70,100]     <NA> 
      28       60       26        6 

vlmc> vlmc.pres <- vlmc(dpres, debug = TRUE)
Warning in vlmc(dpres, debug = TRUE) : alphabet with >1-letter strings; trying to abbreviate
vlmc: Alpha = '0123' ; |X| = 4
vlmc: ctl.dump =  3.079181 -1 
vlmc: n = |data| = 120, cutoff{prune} = 3.90736, threshold{gen} = 2
vlmc: |alphabet| = 4, alphabet = 0123
generating... pruning... computing differences[`completing'] ... writing tree...
[0]0123
0 1 2 1 2
[1]1 18 7 0 2
- -1
- -1
- -1
- -1

[1]1 9 33 5 1
- -1
- -1
[2]2 0 6 6 0
- -1
- -1
- -1
- -1

- -1

[1]1 0 12 14 0
- -1
- -1
- -1
- -1

- -1


vlmc> vlmc.pres

`vlmc', a Variable Length Markov Chain;
	 alphabet '0123', |alphabet| = 4, n = 120.
Call: vlmc(dts = dpres, debug = TRUE);	default cutoff = 3.907
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        2         5         3        42 
AIC =  227.6 

vlmc> vlmc.pres$alpha
[1] "0123"

vlmc> stopifnot(length(print(strsplit(vlmc.pres$alpha, NULL)[[1]])) == 
    vlmc.pres$alpha.len)
[1] "0" "1" "2" "3"
> deviance(vlmc.pres)
[1] 197.6090
> 
> devianceR <- function(object)
+ {
+     dn <- dimnames(pr <- predict(object))
+     -2 * sum(log(pr[cbind(2:nrow(pr), match(dn[[1]][-1], dn[[2]]))]))
+ }
> all.equal(deviance(vlmc.pres), devianceR(vlmc.pres), tol = 1e-14)
[1] TRUE
> 
> 
> 
> cleanEx(); ..nameEx <- "draw.vlmc"
> 
> ### * draw.vlmc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: draw.vlmc
> ### Title: Draw a "VLMC" Object (in ASCII) as tree.
> ### Aliases: draw.vlmc draw
> ### Keywords: ts models
> 
> ### ** Examples
> 
>   example(vlmc)

vlmc> f1 <- c(1, 0, 0, 0)

vlmc> f2 <- rep(1:0, 2)

vlmc> (dt1 <- c(f1, f1, f2, f1, f2, f2, f1))
 [1] 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0

vlmc> (vlmc.dt1 <- vlmc(dt1))

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1);	default cutoff = 1.921
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        3         4         3        26 
AIC =  21.46 

vlmc> vlmc(dt1, dump = 1, ctl.dump = c(wid = 3, nmax = 20), 
    debug = TRUE)
vlmc: Alpha = '01' ; |X| = 2
vlmc: ctl.dump =  3 20 
vlmc: n = |data| = 28, cutoff{prune} = 1.92073, threshold{gen} = 2
vlmc: |alphabet| = 2, alphabet = 01
generating... pruning... Dump{Tree} __after__ pruning: 
Lev Ch|   0   1 |  tot |  num size : ..{set->list}..
------+--------------------------------------------------
  0 x |  18  10 |   28 |   28   32 : 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ..
  1 0 |   8   9 |   17 |   17   32 : 2 3 4 6 7 8 10 12 14 15 16 18 20 22 24 26 27
  2 0 |   4   3 |    7 |    7   16 : 3 4 7 8 15 16 27
  3 0 |   0   3 |    3 |    3   16 : 4 8 16
  3 1 |   4   0 |    4 |    4   16 : 3 7 15 27
  1 1 |  10   0 |   10 |   10   16 : 1 5 9 11 13 17 19 21 23 25
computing differences[`completing'] ... writing tree...
[0]01
0 0 0
[1]1 4 6
[2]2 0 0
[3]3 0 3
- -1
- -1

[3]3 4 0
- -1
- -1


- -1

[1]1 10 0
- -1
- -1



`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, debug = TRUE, dump = 1, ctl.dump = c(wid = 3,     nmax = 20));	default cutoff = 1.921
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        3         4         3        26 
AIC =  21.46 

vlmc> (vlmc.dt1c01 <- vlmc(dts = dt1, cutoff.prune = 0.1, 
    dump = 1))
Lev Ch|  0  1 | tot | num size : ..{set->list}..
------+---------------------------------------------
  0 x | 18 10 |  28 |  28  32 : 0 1 2 3 4 5 6 7 8 9 10 11 12 ..
  1 0 |  8  9 |  17 |  17  32 : 2 3 4 6 7 8 10 12 14 15 16 18 20 ..
  2 0 |  4  3 |   7 |   7  16 : 3 4 7 8 15 16 27
  3 0 |  0  3 |   3 |   3  16 : 4 8 16
  3 1 |  4  0 |   4 |   4  16 : 3 7 15 27
  2 1 |  4  6 |  10 |  10  16 : 2 6 10 12 14 18 20 22 24 26
  3 0 |  3  6 |   9 |   9  16 : 6 10 12 14 18 20 22 24 26
  4 0 |  1  2 |   3 |   3  16 : 6 10 18
  5 0 |  1  2 |   3 |   3  16 : 6 10 18
  6 1 |  1  2 |   3 |   3  16 : 6 10 18
  7 0 |  0  2 |   2 |   2  16 : 10 18
  4 1 |  2  4 |   6 |   6  16 : 12 14 20 22 24 26
  5 0 |  2  4 |   6 |   6  16 : 12 14 20 22 24 26
  6 0 |  0  2 |   2 |   2  16 : 12 20
  6 1 |  2  2 |   4 |   4  16 : 14 22 24 26
  1 1 | 10  0 |  10 |  10  16 : 1 5 9 11 13 17 19 21 23 25

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, cutoff.prune = 0.1, dump = 1)
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        7        11         6        66 
AIC =  27.55 

vlmc> data(presidents)

vlmc> dpres <- cut(presidents, c(0, 45, 70, 100))

vlmc> table(dpres <- factor(dpres, exclude = NULL))

  (0,45]  (45,70] (70,100]     <NA> 
      28       60       26        6 

vlmc> vlmc.pres <- vlmc(dpres, debug = TRUE)
Warning in vlmc(dpres, debug = TRUE) : alphabet with >1-letter strings; trying to abbreviate
vlmc: Alpha = '0123' ; |X| = 4
vlmc: ctl.dump =  3.079181 -1 
vlmc: n = |data| = 120, cutoff{prune} = 3.90736, threshold{gen} = 2
vlmc: |alphabet| = 4, alphabet = 0123
generating... pruning... computing differences[`completing'] ... writing tree...
[0]0123
0 1 2 1 2
[1]1 18 7 0 2
- -1
- -1
- -1
- -1

[1]1 9 33 5 1
- -1
- -1
[2]2 0 6 6 0
- -1
- -1
- -1
- -1

- -1

[1]1 0 12 14 0
- -1
- -1
- -1
- -1

- -1


vlmc> vlmc.pres

`vlmc', a Variable Length Markov Chain;
	 alphabet '0123', |alphabet| = 4, n = 120.
Call: vlmc(dts = dpres, debug = TRUE);	default cutoff = 3.907
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        2         5         3        42 
AIC =  227.6 

vlmc> vlmc.pres$alpha
[1] "0123"

vlmc> stopifnot(length(print(strsplit(vlmc.pres$alpha, NULL)[[1]])) == 
    vlmc.pres$alpha.len)
[1] "0" "1" "2" "3"
>   draw(vlmc.dt1c01)
[x]-( 18 9| 27)-F
 +--[0]-( 8 9| 17) <1.38>-F
 |   +--[0]-( 4 3| 7) <0.14>-F
 |   |   +--[0]-( 0 3| 3) <2.54>-T
 |   |   `--[1]-( 4 0| 4) <2.24>-T
 |   `--[1]-( 4 6| 10) <0.10>
 |       `--[0]-( 3 6| 9) <0.09>-F
 |           +--[0]-( 1 2| 3) <0.00>
 |           |   `--[0]-( 1 2| 3) <0.00>
 |           |       `--[1]-( 1 2| 3) <0.00>
 |           |           `--[0]-( 0 2| 2) <0.81>-T
 |           `--[1]-( 2 4| 6) <0.00>
 |               `--[0]-( 2 4| 6) <0.00>-F
 |                   +--[0]-( 0 2| 2) <0.81>-T
 |                   `--[1]-( 2 2| 4) <0.24>-T
 `--[1]-( 10 0| 10) <4.05>-T
>   draw(vlmc.dt1c01, flag = FALSE)
-4.000000
>   draw(vlmc.dt1c01, kind = 1)
[x]-( 18 9| 27)
 +--[0]-( 8 9| 17) <1.38>
 +---+--[0]-( 4 3| 7) <0.14>
 +---+---+--[0]-( 0 3| 3) <2.54>
 +---+---+--[1]-( 4 0| 4) <2.24>
 +---+--[1]-( 4 6| 10) <0.10>
 +---+---+--[0]-( 3 6| 9) <0.09>
 +---+---+---+--[0]-( 1 2| 3) <0.00>
 +---+---+---+---+--[0]-( 1 2| 3) <0.00>
 +---+---+---+---+---+--[1]-( 1 2| 3) <0.00>
 +---+---+---+---+---+---+--[0]-( 0 2| 2) <0.81>
 +---+---+---+--[1]-( 2 4| 6) <0.00>
 +---+---+---+---+--[0]-( 2 4| 6) <0.00>
 +---+---+---+---+---+--[0]-( 0 2| 2) <0.81>
 +---+---+---+---+---+--[1]-( 2 2| 4) <0.24>
 +--[1]-( 10 0| 10) <4.05>
>   draw(vlmc.dt1)
[x]-( 18 9| 27)-F
 +--[0]-( 8 9| 17) <1.38>
 |   `--[0]-( 4 3| 7) <0.14>-F
 |       +--[0]-( 0 3| 3) <2.54>-T
 |       `--[1]-( 4 0| 4) <2.24>-T
 `--[1]-( 10 0| 10) <4.05>-T
>   draw(vlmc.dt1, show = 3)
[x]-( 18 9| 27)-F
 +--[0]-( 8 9| 17) <1.38>-.
 |   +--[0]-( 4 3| 7) <0.14>-F
 |   |   +--[0]-( 0 3| 3) <2.54>-T
 |   |   `--[1]-( 4 0| 4) <2.24>-T
 |   `--[1]-(___)
 `--[1]-( 10 0| 10) <4.05>-T
>   draw(vlmc.dt1, cumulative = FALSE)
[x]-( 0 0| 0)-F
 +--[0]-( 4 6| 10)
 |   `--[0]-( 0 0| 0)-F
 |       +--[0]-( 0 3| 3)-T
 |       `--[1]-( 4 0| 4)-T
 `--[1]-( 10 0| 10)-T
> 
> 
> 
> cleanEx(); ..nameEx <- "id2ctxt"
> 
> ### * id2ctxt
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: id2ctxt
> ### Title: VLMC Context ID Conversion
> ### Aliases: id2ctxt
> ### Keywords: utilities
> 
> ### ** Examples
> 
>     id2ctxt(c(2,3,5,9), alpha = "Ab")
[1] "A"   "b"   "Ab"  "AAb"
> str(id2ctxt(c(2,3,5,9), 2))
List of 4
 $ : int 0
 $ : int 1
 $ : int [1:2] 0 1
 $ : int [1:3] 0 0 1
> 
> 
> 
> cleanEx(); ..nameEx <- "int2char"
> 
> ### * int2char
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: int2char
> ### Title: Character - Integer Conversion
> ### Aliases: int2char char2int
> ### Keywords: character utilities
> 
> ### ** Examples
> 
> char2int("vlmc", paste(letters, collapse=""))
[1] 21 11 12  2
> 
> int2char(c(0:3, 3:1), "abcd")
[1] "abcddcb"
> int2char(c(1:0,3,3), "abc") # to eat ;-)
[1] "baNANA"
> 
> 
> 
> cleanEx(); ..nameEx <- "logLik"
> 
> ### * logLik
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: logLik
> ### Title: Log Likelihood of and between VLMC objects
> ### Aliases: logLik.vlmc entropy entropy2
> ### Keywords: ts models
> 
> ### ** Examples
> 
> dd <- cumsum(rpois(999, 1.5)) %% 10
> (vd <- vlmc(dd))

`vlmc', a Variable Length Markov Chain;
	 alphabet '0123456789', |alphabet| = 10, n = 999.
Call: vlmc(dts = dd);	default cutoff = 8.46
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        1        10        10       222 
AIC =  3202 
> logLik(vd)
'log Lik.' -1511.208 (df=90)
> 
> ## AIC model selection:
> f1 <- c(1,0,0,0)  # as in example(vlmc)
> f2 <- rep(1:0,2)
> (dt1 <- c(f1,f1,f2,f1,f2,f2,f1))
 [1] 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0
> AIC(print(vlmc(dt1)))

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1);	default cutoff = 1.921
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        3         4         3        26 
AIC =  21.46 
[1] 21.46023
> AIC(print(vlmc(dt1, cutoff = 2.6)))

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, cutoff.prune = 2.6)
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        1         2         1        10 
AIC =  27.51 
[1] 27.50815
> AIC(print(vlmc(dt1, cutoff = 0.4)))# these two differ ``not really''

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, cutoff.prune = 0.4)
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        7        11         5        62 
AIC =  27.55 
[1] 27.54518
> AIC(print(vlmc(dt1, cutoff = 0.1)))

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, cutoff.prune = 0.1)
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        7        11         6        66 
AIC =  27.55 
[1] 27.54518
> 
> ## Show how to compute it from the fitted conditional probabilities :
> logLikR <- function(x) {
+     dn <- dimnames(pr <- predict(x))
+     sum(log(pr[cbind(2:nrow(pr), match(dn[[1]][-1], dn[[2]]))]))
+ }
> 
> all.equal(  logLikR(vd),
+           c(logLik (vd)), tol=1e-10) # TRUE, they do the same
[1] TRUE
> 
> ## Compare different ones:  [cheap example]:
> example(draw)

draw> example(vlmc)

vlmc> f1 <- c(1, 0, 0, 0)

vlmc> f2 <- rep(1:0, 2)

vlmc> (dt1 <- c(f1, f1, f2, f1, f2, f2, f1))
 [1] 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0

vlmc> (vlmc.dt1 <- vlmc(dt1))

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1);	default cutoff = 1.921
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        3         4         3        26 
AIC =  21.46 

vlmc> vlmc(dt1, dump = 1, ctl.dump = c(wid = 3, nmax = 20), 
    debug = TRUE)
vlmc: Alpha = '01' ; |X| = 2
vlmc: ctl.dump =  3 20 
vlmc: n = |data| = 28, cutoff{prune} = 1.92073, threshold{gen} = 2
vlmc: |alphabet| = 2, alphabet = 01
generating... pruning... Dump{Tree} __after__ pruning: 
Lev Ch|   0   1 |  tot |  num size : ..{set->list}..
------+--------------------------------------------------
  0 x |  18  10 |   28 |   28   32 : 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ..
  1 0 |   8   9 |   17 |   17   32 : 2 3 4 6 7 8 10 12 14 15 16 18 20 22 24 26 27
  2 0 |   4   3 |    7 |    7   16 : 3 4 7 8 15 16 27
  3 0 |   0   3 |    3 |    3   16 : 4 8 16
  3 1 |   4   0 |    4 |    4   16 : 3 7 15 27
  1 1 |  10   0 |   10 |   10   16 : 1 5 9 11 13 17 19 21 23 25
computing differences[`completing'] ... writing tree...
[0]01
0 0 0
[1]1 4 6
[2]2 0 0
[3]3 0 3
- -1
- -1

[3]3 4 0
- -1
- -1


- -1

[1]1 10 0
- -1
- -1



`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, debug = TRUE, dump = 1, ctl.dump = c(wid = 3,     nmax = 20));	default cutoff = 1.921
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        3         4         3        26 
AIC =  21.46 

vlmc> (vlmc.dt1c01 <- vlmc(dts = dt1, cutoff.prune = 0.1, 
    dump = 1))
Lev Ch|  0  1 | tot | num size : ..{set->list}..
------+---------------------------------------------
  0 x | 18 10 |  28 |  28  32 : 0 1 2 3 4 5 6 7 8 9 10 11 12 ..
  1 0 |  8  9 |  17 |  17  32 : 2 3 4 6 7 8 10 12 14 15 16 18 20 ..
  2 0 |  4  3 |   7 |   7  16 : 3 4 7 8 15 16 27
  3 0 |  0  3 |   3 |   3  16 : 4 8 16
  3 1 |  4  0 |   4 |   4  16 : 3 7 15 27
  2 1 |  4  6 |  10 |  10  16 : 2 6 10 12 14 18 20 22 24 26
  3 0 |  3  6 |   9 |   9  16 : 6 10 12 14 18 20 22 24 26
  4 0 |  1  2 |   3 |   3  16 : 6 10 18
  5 0 |  1  2 |   3 |   3  16 : 6 10 18
  6 1 |  1  2 |   3 |   3  16 : 6 10 18
  7 0 |  0  2 |   2 |   2  16 : 10 18
  4 1 |  2  4 |   6 |   6  16 : 12 14 20 22 24 26
  5 0 |  2  4 |   6 |   6  16 : 12 14 20 22 24 26
  6 0 |  0  2 |   2 |   2  16 : 12 20
  6 1 |  2  2 |   4 |   4  16 : 14 22 24 26
  1 1 | 10  0 |  10 |  10  16 : 1 5 9 11 13 17 19 21 23 25

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, cutoff.prune = 0.1, dump = 1)
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        7        11         6        66 
AIC =  27.55 

vlmc> data(presidents)

vlmc> dpres <- cut(presidents, c(0, 45, 70, 100))

vlmc> table(dpres <- factor(dpres, exclude = NULL))

  (0,45]  (45,70] (70,100]     <NA> 
      28       60       26        6 

vlmc> vlmc.pres <- vlmc(dpres, debug = TRUE)
Warning in vlmc(dpres, debug = TRUE) : alphabet with >1-letter strings; trying to abbreviate
vlmc: Alpha = '0123' ; |X| = 4
vlmc: ctl.dump =  3.079181 -1 
vlmc: n = |data| = 120, cutoff{prune} = 3.90736, threshold{gen} = 2
vlmc: |alphabet| = 4, alphabet = 0123
generating... pruning... computing differences[`completing'] ... writing tree...
[0]0123
0 1 2 1 2
[1]1 18 7 0 2
- -1
- -1
- -1
- -1

[1]1 9 33 5 1
- -1
- -1
[2]2 0 6 6 0
- -1
- -1
- -1
- -1

- -1

[1]1 0 12 14 0
- -1
- -1
- -1
- -1

- -1


vlmc> vlmc.pres

`vlmc', a Variable Length Markov Chain;
	 alphabet '0123', |alphabet| = 4, n = 120.
Call: vlmc(dts = dpres, debug = TRUE);	default cutoff = 3.907
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        2         5         3        42 
AIC =  227.6 

vlmc> vlmc.pres$alpha
[1] "0123"

vlmc> stopifnot(length(print(strsplit(vlmc.pres$alpha, NULL)[[1]])) == 
    vlmc.pres$alpha.len)
[1] "0" "1" "2" "3"

draw> draw(vlmc.dt1c01)
[x]-( 18 9| 27)-F
 +--[0]-( 8 9| 17) <1.38>-F
 |   +--[0]-( 4 3| 7) <0.14>-F
 |   |   +--[0]-( 0 3| 3) <2.54>-T
 |   |   `--[1]-( 4 0| 4) <2.24>-T
 |   `--[1]-( 4 6| 10) <0.10>
 |       `--[0]-( 3 6| 9) <0.09>-F
 |           +--[0]-( 1 2| 3) <0.00>
 |           |   `--[0]-( 1 2| 3) <0.00>
 |           |       `--[1]-( 1 2| 3) <0.00>
 |           |           `--[0]-( 0 2| 2) <0.81>-T
 |           `--[1]-( 2 4| 6) <0.00>
 |               `--[0]-( 2 4| 6) <0.00>-F
 |                   +--[0]-( 0 2| 2) <0.81>-T
 |                   `--[1]-( 2 2| 4) <0.24>-T
 `--[1]-( 10 0| 10) <4.05>-T

draw> draw(vlmc.dt1c01, flag = FALSE)
-4.000000

draw> draw(vlmc.dt1c01, kind = 1)
[x]-( 18 9| 27)
 +--[0]-( 8 9| 17) <1.38>
 +---+--[0]-( 4 3| 7) <0.14>
 +---+---+--[0]-( 0 3| 3) <2.54>
 +---+---+--[1]-( 4 0| 4) <2.24>
 +---+--[1]-( 4 6| 10) <0.10>
 +---+---+--[0]-( 3 6| 9) <0.09>
 +---+---+---+--[0]-( 1 2| 3) <0.00>
 +---+---+---+---+--[0]-( 1 2| 3) <0.00>
 +---+---+---+---+---+--[1]-( 1 2| 3) <0.00>
 +---+---+---+---+---+---+--[0]-( 0 2| 2) <0.81>
 +---+---+---+--[1]-( 2 4| 6) <0.00>
 +---+---+---+---+--[0]-( 2 4| 6) <0.00>
 +---+---+---+---+---+--[0]-( 0 2| 2) <0.81>
 +---+---+---+---+---+--[1]-( 2 2| 4) <0.24>
 +--[1]-( 10 0| 10) <4.05>

draw> draw(vlmc.dt1)
[x]-( 18 9| 27)-F
 +--[0]-( 8 9| 17) <1.38>
 |   `--[0]-( 4 3| 7) <0.14>-F
 |       +--[0]-( 0 3| 3) <2.54>-T
 |       `--[1]-( 4 0| 4) <2.24>-T
 `--[1]-( 10 0| 10) <4.05>-T

draw> draw(vlmc.dt1, show = 3)
[x]-( 18 9| 27)-F
 +--[0]-( 8 9| 17) <1.38>-.
 |   +--[0]-( 4 3| 7) <0.14>-F
 |   |   +--[0]-( 0 3| 3) <2.54>-T
 |   |   `--[1]-( 4 0| 4) <2.24>-T
 |   `--[1]-(___)
 `--[1]-( 10 0| 10) <4.05>-T

draw> draw(vlmc.dt1, cumulative = FALSE)
[x]-( 0 0| 0)-F
 +--[0]-( 4 6| 10)
 |   `--[0]-( 0 0| 0)-F
 |       +--[0]-( 0 3| 3)-T
 |       `--[1]-( 4 0| 4)-T
 `--[1]-( 10 0| 10)-T
> for(n in ls())
+   if(is.vlmc(get(n))) {
+        vv <- get(n)
+        cat(n,":",formatC(logLik(vv) / log(vv$alpha.len),
+                          format= "f", wid=10),"\n")
+   }
vd :  -656.3095 
vlmc.dt1 :    -9.7095 
vlmc.dt1c01 :    -4.0000 
vlmc.pres :   -71.2724 
> 
> 
> 
> cleanEx(); ..nameEx <- "predict.vlmc"
> 
> ### * predict.vlmc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: predict.vlmc
> ### Title: Prediction of VLMC for (new) Series
> ### Aliases: print.predict.vlmc predict.vlmc fitted.vlmc
> ### Keywords: ts models
> 
> ### ** Examples
> 
> f1 <- c(1,0,0,0)
> f2 <- rep(1:0,2)
> (dt2 <- rep(c(f1,f1,f2,f1,f2,f2,f1),2))
 [1] 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0
[39] 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0
> 
> (vlmc.dt2c15  <- vlmc(dt2, cutoff = 1.5))

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 56.
Call: vlmc(dts = dt2, cutoff.prune = 1.5)
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        6         8         4        46 
AIC =  36.65 
> draw(vlmc.dt2c15)
[x]-( 36 19| 55)-F
 +--[0]-( 16 19| 35) <2.84>-F
 |   +--[0]-( 8 7| 15) <0.17>-F
 |   |   +--[0]-( 0 7| 7) <5.33>-T
 |   |   `--[1]-( 8 0| 8) <5.03>-T
 |   `--[1]-( 8 12| 20) <0.13>
 |       `--[0]-( 7 12| 19) <0.04>
 |           `--[1]-( 4 8| 12) <0.03>
 |               `--[0]-( 4 8| 12) <0.00>
 |                   `--[0]-( 0 4| 4) <1.62>-T
 `--[1]-( 20 0| 20) <8.48>-T
> 
> ## Fitted Values:
> all.equal(predict(vlmc.dt2c15, dt2), predict(vlmc.dt2c15))
[1] TRUE
> (pa2c15 <- predict(vlmc.dt2c15, type = "ALL"))
  fit  Pr[X= 0 ] Pr[X= 1 ] id   flags ctxt  
1 <NA> <NA>      <NA>      <NA> 0     NA    
0 0    1         0         3    55    1     
0 0    1         0         5    55    01    
0 0    1         0         9    55    001   
1 1    0         1         8    0     000   
0 0    1         0         3    0     1     
0 1    3/7       4/7       10   0     010   
0 0    1         0         9    0     001   
1 1    0         1         8    0     000   
0 0    1         0         3    0     1     
1 1    3/7       4/7       10   0     010   
0 0    1         0         3    0     1     
1 1    0         1         84   0     010100
0 0    1         0         3    0     1     
0 1    1/2       1/2       42   0     01010 
0 0    1         0         9    0     001   
1 1    0         1         8    0     000   
0 0    1         0         3    0     1     
1 1    3/7       4/7       10   0     010   
0 0    1         0         3    0     1     
1 1    0         1         84   0     010100
0 0    1         0         3    0     1     
1 1    1/2       1/2       42   0     01010 
0 0    1         0         3    0     1     
1 0    1/2       1/2       42   0     01010 
0 0    1         0         3    0     1     
0 0    1/2       1/2       42   0     01010 
0 0    1         0         9    0     001   
1 1    0         1         8    0     000   
0 0    1         0         3    0     1     
0 1    3/7       4/7       10   0     010   
0 0    1         0         9    0     001   
1 1    0         1         8    0     000   
0 0    1         0         3    0     1     
0 1    3/7       4/7       10   0     010   
0 0    1         0         9    0     001   
1 1    0         1         8    0     000   
0 0    1         0         3    0     1     
1 1    3/7       4/7       10   0     010   
0 0    1         0         3    0     1     
1 1    0         1         84   0     010100
0 0    1         0         3    0     1     
0 1    1/2       1/2       42   0     01010 
0 0    1         0         9    0     001   
1 1    0         1         8    0     000   
0 0    1         0         3    0     1     
1 1    3/7       4/7       10   0     010   
0 0    1         0         3    0     1     
1 1    0         1         84   0     010100
0 0    1         0         3    0     1     
1 0    1/2       1/2       42   0     01010 
0 0    1         0         3    0     1     
1 0    1/2       1/2       42   0     01010 
0 0    1         0         3    0     1     
0 0    1/2       1/2       42   0     01010 
0 0    1         0         9    0     001   
> 
> ## Depth = context length  ([1] : NA) :
> stopifnot(nchar(pa2c15 $ ctxt)[-1] ==
+           predict(vlmc.dt2c15, type = "depth")[-1])
> 
> same <- (ff1 <- pa2c15 $ fitted) ==
+         (ff2 <- int2alpha(predict(vlmc.dt2c15, type ="response"), alpha="01"))
> which(!same) #-> some are different, since max.col() breaks ties at random!
[1] 15 23 43
> 
> ndt2 <- c(rep(0,6),f1,f1,f2)
> predict(vlmc.dt2c15, ndt2, "ALL")
  fit  Pr[X= 0 ] Pr[X= 1 ] id   flags ctxt
0 <NA> <NA>      <NA>      <NA> 0     NA  
0 1    16/35     19/35     2    65    0   
0 0    8/15      7/15      4    65    00  
0 1    0         1         8    55    000 
0 1    0         1         8    0     000 
0 1    0         1         8    0     000 
1 1    0         1         8    0     000 
0 0    1         0         3    0     1   
0 1    3/7       4/7       10   0     010 
0 0    1         0         9    0     001 
1 1    0         1         8    0     000 
0 0    1         0         3    0     1   
0 1    3/7       4/7       10   0     010 
0 0    1         0         9    0     001 
1 1    0         1         8    0     000 
0 0    1         0         3    0     1   
1 1    3/7       4/7       10   0     010 
0 0    1         0         3    0     1   
> 
> (newdt2 <- sample(dt2, 17))
 [1] 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 0 1
> pm <- predict(vlmc.dt2c15, newdt2, allow.subset = TRUE)
> summary(apply(pm, 1, sum))# all 1
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
      1       1       1       1       1       1       1 
> 
> predict(vlmc.dt2c15, newdt2, type = "ALL")
  fit  Pr[X= 0 ] Pr[X= 1 ] id   flags ctxt
0 <NA> <NA>      <NA>      <NA> 0     NA  
0 1    16/35     19/35     2    65    0   
0 0    8/15      7/15      4    65    00  
0 1    0         1         8    55    000 
0 1    0         1         8    0     000 
0 1    0         1         8    0     000 
1 1    0         1         8    0     000 
1 0    1         0         3    0     1   
0 0    1         0         3    0     1   
1 0    1         0         5    0     01  
0 0    1         0         3    0     1   
0 1    1/3       2/3       21   10    0101
0 0    1         0         9    0     001 
1 1    0         1         8    0     000 
0 0    1         0         3    0     1   
0 1    3/7       4/7       10   0     010 
1 0    1         0         9    0     001 
> 
> data(bnrf1)
> (vbnrf <- vlmc(bnrf1EB))

`vlmc', a Variable Length Markov Chain;
	 alphabet 'acgt', |alphabet| = 4, n = 3954.
Call: vlmc(dts = bnrf1EB);	default cutoff = 3.907
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        6        73        28       618 
AIC =  10555 
> (pA <- predict(vbnrf, bnrf1EB[1:24], type = "ALL"))
  fit  Pr[X= a ] Pr[X= c ] Pr[X= g ] Pr[X= t ] id   flags ctxt 
a <NA> <NA>      <NA>      <NA>      <NA>      <NA> 0     NA   
t t    0         0         0         1         4    55    a    
g g    4/51      14/51     26/51     7/51      28   55    ta   
g g    3/14      13/56     19/56     3/14      108  55    gta  
a c    17/72     3/8       19/72     1/8       107  0     ggt  
a g    32/189    7/27      73/189    5/27      18   0     ag   
g g    13/76     5/19      6/19      1/4       16   0     aa   
a c    3/10      3/10      1/5       1/5       96   0     gaa  
g g    32/189    7/27      73/189    5/27      18   0     ag   
a g    52/203    54/203    10/29     27/203    24   0     ga   
g g    32/189    7/27      73/189    5/27      18   0     ag   
g g    52/203    54/203    10/29     27/203    24   0     ga   
g c    19/100    53/150    3/10      47/300    26   0     gg   
g c    19/100    53/150    3/10      47/300    26   0     gg   
c c    19/100    53/150    3/10      47/300    26   0     gg   
a c    1/12      3/4       0         1/6       1450 0     cgggg
g g    43/197    47/197    70/197    37/197    17   0     ac   
g g    52/203    54/203    10/29     27/203    24   0     ga   
g c    19/100    53/150    3/10      47/300    26   0     gg   
a c    19/100    53/150    3/10      47/300    26   0     gg   
a g    32/189    7/27      73/189    5/27      18   0     ag   
a g    13/76     5/19      6/19      1/4       16   0     aa   
c g    13/76     5/19      6/19      1/4       16   0     aa   
g g    49/219    65/219    67/219    38/219    20   0     ca   
>  pc <- predict(vbnrf, bnrf1EB[1:24], type = "class")
>  pr <- predict(vbnrf, bnrf1EB[1:24], type = "resp")
> stopifnot(as.integer  (pc[-1])   == 1 + pr[-1],
+           as.character(pc[-1]) == strsplit(vbnrf$alpha,NULL)[[1]][1 + pr[-1]])
> 
> ##-- Example of a "perfect" fit -- just for illustration:
> ##                          the default, thresh = 2 doesn't fit perfectly(i=38)
> (vlmc.dt2c0th1 <- vlmc(dt2, cutoff = 0, thresh = 1))

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 56.
Call: vlmc(dts = dt2, cutoff.prune = 0, threshold.gen = 1)
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
       12        28        12       158 
AIC =  56 
> 
> ## "Fitted" = "Data" (but the first which can't be predicted):
> stopifnot(dt2[-1] == predict(vlmc.dt2c0th1,type = "response")[-1])
> 
> 
> 
> cleanEx(); ..nameEx <- "prt.vvec"
> 
> ### * prt.vvec
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: prt.vvec
> ### Title: Recursively Print the VLMC Result Vector
> ### Aliases: prt.vvec
> ### Keywords: utilities
> 
> ### ** Examples
> 
>   example(vlmc)

vlmc> f1 <- c(1, 0, 0, 0)

vlmc> f2 <- rep(1:0, 2)

vlmc> (dt1 <- c(f1, f1, f2, f1, f2, f2, f1))
 [1] 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0

vlmc> (vlmc.dt1 <- vlmc(dt1))

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1);	default cutoff = 1.921
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        3         4         3        26 
AIC =  21.46 

vlmc> vlmc(dt1, dump = 1, ctl.dump = c(wid = 3, nmax = 20), 
    debug = TRUE)
vlmc: Alpha = '01' ; |X| = 2
vlmc: ctl.dump =  3 20 
vlmc: n = |data| = 28, cutoff{prune} = 1.92073, threshold{gen} = 2
vlmc: |alphabet| = 2, alphabet = 01
generating... pruning... Dump{Tree} __after__ pruning: 
Lev Ch|   0   1 |  tot |  num size : ..{set->list}..
------+--------------------------------------------------
  0 x |  18  10 |   28 |   28   32 : 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ..
  1 0 |   8   9 |   17 |   17   32 : 2 3 4 6 7 8 10 12 14 15 16 18 20 22 24 26 27
  2 0 |   4   3 |    7 |    7   16 : 3 4 7 8 15 16 27
  3 0 |   0   3 |    3 |    3   16 : 4 8 16
  3 1 |   4   0 |    4 |    4   16 : 3 7 15 27
  1 1 |  10   0 |   10 |   10   16 : 1 5 9 11 13 17 19 21 23 25
computing differences[`completing'] ... writing tree...
[0]01
0 0 0
[1]1 4 6
[2]2 0 0
[3]3 0 3
- -1
- -1

[3]3 4 0
- -1
- -1


- -1

[1]1 10 0
- -1
- -1



`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, debug = TRUE, dump = 1, ctl.dump = c(wid = 3,     nmax = 20));	default cutoff = 1.921
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        3         4         3        26 
AIC =  21.46 

vlmc> (vlmc.dt1c01 <- vlmc(dts = dt1, cutoff.prune = 0.1, 
    dump = 1))
Lev Ch|  0  1 | tot | num size : ..{set->list}..
------+---------------------------------------------
  0 x | 18 10 |  28 |  28  32 : 0 1 2 3 4 5 6 7 8 9 10 11 12 ..
  1 0 |  8  9 |  17 |  17  32 : 2 3 4 6 7 8 10 12 14 15 16 18 20 ..
  2 0 |  4  3 |   7 |   7  16 : 3 4 7 8 15 16 27
  3 0 |  0  3 |   3 |   3  16 : 4 8 16
  3 1 |  4  0 |   4 |   4  16 : 3 7 15 27
  2 1 |  4  6 |  10 |  10  16 : 2 6 10 12 14 18 20 22 24 26
  3 0 |  3  6 |   9 |   9  16 : 6 10 12 14 18 20 22 24 26
  4 0 |  1  2 |   3 |   3  16 : 6 10 18
  5 0 |  1  2 |   3 |   3  16 : 6 10 18
  6 1 |  1  2 |   3 |   3  16 : 6 10 18
  7 0 |  0  2 |   2 |   2  16 : 10 18
  4 1 |  2  4 |   6 |   6  16 : 12 14 20 22 24 26
  5 0 |  2  4 |   6 |   6  16 : 12 14 20 22 24 26
  6 0 |  0  2 |   2 |   2  16 : 12 20
  6 1 |  2  2 |   4 |   4  16 : 14 22 24 26
  1 1 | 10  0 |  10 |  10  16 : 1 5 9 11 13 17 19 21 23 25

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, cutoff.prune = 0.1, dump = 1)
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        7        11         6        66 
AIC =  27.55 

vlmc> data(presidents)

vlmc> dpres <- cut(presidents, c(0, 45, 70, 100))

vlmc> table(dpres <- factor(dpres, exclude = NULL))

  (0,45]  (45,70] (70,100]     <NA> 
      28       60       26        6 

vlmc> vlmc.pres <- vlmc(dpres, debug = TRUE)
Warning in vlmc(dpres, debug = TRUE) : alphabet with >1-letter strings; trying to abbreviate
vlmc: Alpha = '0123' ; |X| = 4
vlmc: ctl.dump =  3.079181 -1 
vlmc: n = |data| = 120, cutoff{prune} = 3.90736, threshold{gen} = 2
vlmc: |alphabet| = 4, alphabet = 0123
generating... pruning... computing differences[`completing'] ... writing tree...
[0]0123
0 1 2 1 2
[1]1 18 7 0 2
- -1
- -1
- -1
- -1

[1]1 9 33 5 1
- -1
- -1
[2]2 0 6 6 0
- -1
- -1
- -1
- -1

- -1

[1]1 0 12 14 0
- -1
- -1
- -1
- -1

- -1


vlmc> vlmc.pres

`vlmc', a Variable Length Markov Chain;
	 alphabet '0123', |alphabet| = 4, n = 120.
Call: vlmc(dts = dpres, debug = TRUE);	default cutoff = 3.907
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        2         5         3        42 
AIC =  227.6 

vlmc> vlmc.pres$alpha
[1] "0123"

vlmc> stopifnot(length(print(strsplit(vlmc.pres$alpha, NULL)[[1]])) == 
    vlmc.pres$alpha.len)
[1] "0" "1" "2" "3"
>   str(vv <- vlmc.dt1$vlmc)
 int [1:26] 2 0 0 0 1 4 6 2 0 0 ...
>   prt.vvec(vv[-1], n = 2)
{0} [0, 0]
  {1} [4, 6]
     {2} [0, 0]
        {3} [0, 3] - -
        {3} [4, 0] - - -
  {1} [10, 0] - -
NULL
>   prt.vvec(vv[-1], n = 2, pad = " | ")
{0} [0, 0]
 |  | {1} [4, 6]
 |  |  |  |  | {2} [0, 0]
 |  |  |  |  |  |  |  | {3} [0, 3] - -
 |  |  |  |  |  |  |  | {3} [4, 0] - - -
 |  | {1} [10, 0] - -
NULL
> 
> 
> 
> cleanEx(); ..nameEx <- "residuals.vlmc"
> 
> ### * residuals.vlmc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: residuals.vlmc
> ### Title: Compute Residuals of a Fitted VLMC Object
> ### Aliases: residuals.vlmc
> ### Keywords: ts models
> 
> ### ** Examples
> 
> example(vlmc)

vlmc> f1 <- c(1, 0, 0, 0)

vlmc> f2 <- rep(1:0, 2)

vlmc> (dt1 <- c(f1, f1, f2, f1, f2, f2, f1))
 [1] 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0

vlmc> (vlmc.dt1 <- vlmc(dt1))

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1);	default cutoff = 1.921
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        3         4         3        26 
AIC =  21.46 

vlmc> vlmc(dt1, dump = 1, ctl.dump = c(wid = 3, nmax = 20), 
    debug = TRUE)
vlmc: Alpha = '01' ; |X| = 2
vlmc: ctl.dump =  3 20 
vlmc: n = |data| = 28, cutoff{prune} = 1.92073, threshold{gen} = 2
vlmc: |alphabet| = 2, alphabet = 01
generating... pruning... Dump{Tree} __after__ pruning: 
Lev Ch|   0   1 |  tot |  num size : ..{set->list}..
------+--------------------------------------------------
  0 x |  18  10 |   28 |   28   32 : 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ..
  1 0 |   8   9 |   17 |   17   32 : 2 3 4 6 7 8 10 12 14 15 16 18 20 22 24 26 27
  2 0 |   4   3 |    7 |    7   16 : 3 4 7 8 15 16 27
  3 0 |   0   3 |    3 |    3   16 : 4 8 16
  3 1 |   4   0 |    4 |    4   16 : 3 7 15 27
  1 1 |  10   0 |   10 |   10   16 : 1 5 9 11 13 17 19 21 23 25
computing differences[`completing'] ... writing tree...
[0]01
0 0 0
[1]1 4 6
[2]2 0 0
[3]3 0 3
- -1
- -1

[3]3 4 0
- -1
- -1


- -1

[1]1 10 0
- -1
- -1



`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, debug = TRUE, dump = 1, ctl.dump = c(wid = 3,     nmax = 20));	default cutoff = 1.921
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        3         4         3        26 
AIC =  21.46 

vlmc> (vlmc.dt1c01 <- vlmc(dts = dt1, cutoff.prune = 0.1, 
    dump = 1))
Lev Ch|  0  1 | tot | num size : ..{set->list}..
------+---------------------------------------------
  0 x | 18 10 |  28 |  28  32 : 0 1 2 3 4 5 6 7 8 9 10 11 12 ..
  1 0 |  8  9 |  17 |  17  32 : 2 3 4 6 7 8 10 12 14 15 16 18 20 ..
  2 0 |  4  3 |   7 |   7  16 : 3 4 7 8 15 16 27
  3 0 |  0  3 |   3 |   3  16 : 4 8 16
  3 1 |  4  0 |   4 |   4  16 : 3 7 15 27
  2 1 |  4  6 |  10 |  10  16 : 2 6 10 12 14 18 20 22 24 26
  3 0 |  3  6 |   9 |   9  16 : 6 10 12 14 18 20 22 24 26
  4 0 |  1  2 |   3 |   3  16 : 6 10 18
  5 0 |  1  2 |   3 |   3  16 : 6 10 18
  6 1 |  1  2 |   3 |   3  16 : 6 10 18
  7 0 |  0  2 |   2 |   2  16 : 10 18
  4 1 |  2  4 |   6 |   6  16 : 12 14 20 22 24 26
  5 0 |  2  4 |   6 |   6  16 : 12 14 20 22 24 26
  6 0 |  0  2 |   2 |   2  16 : 12 20
  6 1 |  2  2 |   4 |   4  16 : 14 22 24 26
  1 1 | 10  0 |  10 |  10  16 : 1 5 9 11 13 17 19 21 23 25

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, cutoff.prune = 0.1, dump = 1)
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        7        11         6        66 
AIC =  27.55 

vlmc> data(presidents)

vlmc> dpres <- cut(presidents, c(0, 45, 70, 100))

vlmc> table(dpres <- factor(dpres, exclude = NULL))

  (0,45]  (45,70] (70,100]     <NA> 
      28       60       26        6 

vlmc> vlmc.pres <- vlmc(dpres, debug = TRUE)
Warning in vlmc(dpres, debug = TRUE) : alphabet with >1-letter strings; trying to abbreviate
vlmc: Alpha = '0123' ; |X| = 4
vlmc: ctl.dump =  3.079181 -1 
vlmc: n = |data| = 120, cutoff{prune} = 3.90736, threshold{gen} = 2
vlmc: |alphabet| = 4, alphabet = 0123
generating... pruning... computing differences[`completing'] ... writing tree...
[0]0123
0 1 2 1 2
[1]1 18 7 0 2
- -1
- -1
- -1
- -1

[1]1 9 33 5 1
- -1
- -1
[2]2 0 6 6 0
- -1
- -1
- -1
- -1

- -1

[1]1 0 12 14 0
- -1
- -1
- -1
- -1

- -1


vlmc> vlmc.pres

`vlmc', a Variable Length Markov Chain;
	 alphabet '0123', |alphabet| = 4, n = 120.
Call: vlmc(dts = dpres, debug = TRUE);	default cutoff = 3.907
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        2         5         3        42 
AIC =  227.6 

vlmc> vlmc.pres$alpha
[1] "0123"

vlmc> stopifnot(length(print(strsplit(vlmc.pres$alpha, NULL)[[1]])) == 
    vlmc.pres$alpha.len)
[1] "0" "1" "2" "3"
> rp <- residuals(vlmc.pres)
> stopifnot(all(abs(apply(rp[-1,],1,sum)) < 1e-15))
> matplot(seq(presidents), rp, ylab = "residuals", type="l")
> ## ``Tukey-Anscombe'' (the following is first stab at plot method):
> matplot(fitted(vlmc.pres), rp, ylab = "residuals", xaxt = "n",
+         type="b", pch=vlmc.pres$alpha)
> axis(1, at = 0:(vlmc.pres$alpha.len-1),
+      labels = strsplit(vlmc.pres$alpha,"")[[1]])
> 
> summary(rd <- residuals(vlmc.pres, type = "dev"))
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
-2.2820 -1.2440 -1.1130 -1.1220 -0.8657  2.7830  1.0000 
> rd[1:7]
[1]        NA -1.893018 -1.112690 -1.112690 -1.243535 -1.177410 -1.829741
> ## sum of squared dev.residuals === deviance :
> all.equal(sum(rd[-1] ^ 2),
+           deviance(vlmc.pres))
[1] TRUE
> ## Don't show:
> stopifnot(all.equal(sum(rd[-1] ^ 2),
+                     deviance(vlmc.pres),tol=1e-12))
> ## End Don't show
> 
> 
> 
> cleanEx(); ..nameEx <- "simulate.vlmc"
> 
> ### * simulate.vlmc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: simulate.vlmc
> ### Title: Simulate a Discrete Time Series from fitted VLMC model
> ### Aliases: simulate.vlmc
> ### Keywords: ts models
> 
> ### ** Examples
> 
> example(vlmc)

vlmc> f1 <- c(1, 0, 0, 0)

vlmc> f2 <- rep(1:0, 2)

vlmc> (dt1 <- c(f1, f1, f2, f1, f2, f2, f1))
 [1] 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0

vlmc> (vlmc.dt1 <- vlmc(dt1))

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1);	default cutoff = 1.921
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        3         4         3        26 
AIC =  21.46 

vlmc> vlmc(dt1, dump = 1, ctl.dump = c(wid = 3, nmax = 20), 
    debug = TRUE)
vlmc: Alpha = '01' ; |X| = 2
vlmc: ctl.dump =  3 20 
vlmc: n = |data| = 28, cutoff{prune} = 1.92073, threshold{gen} = 2
vlmc: |alphabet| = 2, alphabet = 01
generating... pruning... Dump{Tree} __after__ pruning: 
Lev Ch|   0   1 |  tot |  num size : ..{set->list}..
------+--------------------------------------------------
  0 x |  18  10 |   28 |   28   32 : 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ..
  1 0 |   8   9 |   17 |   17   32 : 2 3 4 6 7 8 10 12 14 15 16 18 20 22 24 26 27
  2 0 |   4   3 |    7 |    7   16 : 3 4 7 8 15 16 27
  3 0 |   0   3 |    3 |    3   16 : 4 8 16
  3 1 |   4   0 |    4 |    4   16 : 3 7 15 27
  1 1 |  10   0 |   10 |   10   16 : 1 5 9 11 13 17 19 21 23 25
computing differences[`completing'] ... writing tree...
[0]01
0 0 0
[1]1 4 6
[2]2 0 0
[3]3 0 3
- -1
- -1

[3]3 4 0
- -1
- -1


- -1

[1]1 10 0
- -1
- -1



`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, debug = TRUE, dump = 1, ctl.dump = c(wid = 3,     nmax = 20));	default cutoff = 1.921
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        3         4         3        26 
AIC =  21.46 

vlmc> (vlmc.dt1c01 <- vlmc(dts = dt1, cutoff.prune = 0.1, 
    dump = 1))
Lev Ch|  0  1 | tot | num size : ..{set->list}..
------+---------------------------------------------
  0 x | 18 10 |  28 |  28  32 : 0 1 2 3 4 5 6 7 8 9 10 11 12 ..
  1 0 |  8  9 |  17 |  17  32 : 2 3 4 6 7 8 10 12 14 15 16 18 20 ..
  2 0 |  4  3 |   7 |   7  16 : 3 4 7 8 15 16 27
  3 0 |  0  3 |   3 |   3  16 : 4 8 16
  3 1 |  4  0 |   4 |   4  16 : 3 7 15 27
  2 1 |  4  6 |  10 |  10  16 : 2 6 10 12 14 18 20 22 24 26
  3 0 |  3  6 |   9 |   9  16 : 6 10 12 14 18 20 22 24 26
  4 0 |  1  2 |   3 |   3  16 : 6 10 18
  5 0 |  1  2 |   3 |   3  16 : 6 10 18
  6 1 |  1  2 |   3 |   3  16 : 6 10 18
  7 0 |  0  2 |   2 |   2  16 : 10 18
  4 1 |  2  4 |   6 |   6  16 : 12 14 20 22 24 26
  5 0 |  2  4 |   6 |   6  16 : 12 14 20 22 24 26
  6 0 |  0  2 |   2 |   2  16 : 12 20
  6 1 |  2  2 |   4 |   4  16 : 14 22 24 26
  1 1 | 10  0 |  10 |  10  16 : 1 5 9 11 13 17 19 21 23 25

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, cutoff.prune = 0.1, dump = 1)
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        7        11         6        66 
AIC =  27.55 

vlmc> data(presidents)

vlmc> dpres <- cut(presidents, c(0, 45, 70, 100))

vlmc> table(dpres <- factor(dpres, exclude = NULL))

  (0,45]  (45,70] (70,100]     <NA> 
      28       60       26        6 

vlmc> vlmc.pres <- vlmc(dpres, debug = TRUE)
Warning in vlmc(dpres, debug = TRUE) : alphabet with >1-letter strings; trying to abbreviate
vlmc: Alpha = '0123' ; |X| = 4
vlmc: ctl.dump =  3.079181 -1 
vlmc: n = |data| = 120, cutoff{prune} = 3.90736, threshold{gen} = 2
vlmc: |alphabet| = 4, alphabet = 0123
generating... pruning... computing differences[`completing'] ... writing tree...
[0]0123
0 1 2 1 2
[1]1 18 7 0 2
- -1
- -1
- -1
- -1

[1]1 9 33 5 1
- -1
- -1
[2]2 0 6 6 0
- -1
- -1
- -1
- -1

- -1

[1]1 0 12 14 0
- -1
- -1
- -1
- -1

- -1


vlmc> vlmc.pres

`vlmc', a Variable Length Markov Chain;
	 alphabet '0123', |alphabet| = 4, n = 120.
Call: vlmc(dts = dpres, debug = TRUE);	default cutoff = 3.907
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        2         5         3        42 
AIC =  227.6 

vlmc> vlmc.pres$alpha
[1] "0123"

vlmc> stopifnot(length(print(strsplit(vlmc.pres$alpha, NULL)[[1]])) == 
    vlmc.pres$alpha.len)
[1] "0" "1" "2" "3"
> 
> simulate.vlmc(vlmc.dt1, 100)
  [1] "0" "1" "0" "1" "0" "0" "0" "1" "0" "0" "0" "1" "0" "0" "0" "1" "0" "0"
 [19] "0" "1" "0" "0" "0" "1" "0" "1" "0" "0" "0" "1" "0" "0" "0" "1" "0" "0"
 [37] "0" "1" "0" "1" "0" "0" "0" "1" "0" "0" "0" "1" "0" "1" "0" "1" "0" "1"
 [55] "0" "0" "0" "1" "0" "1" "0" "0" "0" "1" "0" "0" "0" "1" "0" "1" "0" "1"
 [73] "0" "1" "0" "0" "0" "1" "0" "0" "0" "1" "0" "1" "0" "0" "0" "1" "0" "0"
 [91] "0" "1" "0" "1" "0" "0" "0" "1" "0" "0"
> simulate.vlmc(vlmc.dt1c01, 100, int = TRUE)
  [1] 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0
 [38] 1 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 0 1 0 1 0 1
 [75] 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 0
> # n.start = 0: 1st few observations will resemble the data
> simulate.vlmc(vlmc.dt1c01, 20, n.start=0, int = TRUE)
 [1] 0 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "summary.vlmc"
> 
> ### * summary.vlmc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: summary.vlmc
> ### Title: Summary of Fitted Variable Length Markov Chain (VLMC)
> ### Aliases: summary.vlmc print.summary.vlmc
> ### Keywords: ts models
> 
> ### ** Examples
> 
> data(bnrf1)
> vb <- vlmc(bnrf1EB)
> svb <- summary(vb)
> svb

`vlmc', a Variable Length Markov Chain;
	 alphabet 'acgt', |alphabet| = 4, n = 3954.
Call: vlmc(dts = bnrf1EB);	default cutoff = 3.907364
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        6        73        28       618 
AIC =  10554.84 
R^2 = %{correctly predicted} = 39.2%
Confusion matrix:
    predicted
data   a   c   g   t
   a  78 316 338  11
   c  34 613 524  24
   g  34 375 811  12
   t  44 339 352  48
Markov chain depths along the data:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
       1        2        2     2.45        3        6 
> ## Don't show:
> try(## since it currently fails (".. nested too deeply") -- FIXME -- !
+   print(svb, vvec.print = TRUE)
+ )

`vlmc', a Variable Length Markov Chain;
	 alphabet 'acgt', |alphabet| = 4, n = 3954.
Call: vlmc(dts = bnrf1EB);	default cutoff = 3.907364
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        6        73        28       618 
AIC =  10554.84 
R^2 = %{correctly predicted} = 39.2%
Confusion matrix:
    predicted
data   a   c   g   t
   a  78 316 338  11
   c  34 613 524  24
   g  34 375 811  12
   t  44 339 352  48
Markov chain depths along the data:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
       1        2        2     2.45        3        6 

context tree encoding `vvec'tor:
{0} [0, 0, 0, 0]
  {1} [0, 0, 0, 1]
     {2} [13, 20, 24, 19] -
        {3} [4, 18, 9, 2] - -
           {4} [3, 2, 10, 6] - - - - - - -
     {2} [43, 47, 70, 37] -
        {3} [9, 15, 31, 13] -
           {4} [2, 12, 4, 0] - - - - - - - -
     {2} [32, 49, 73, 35] - - -
        {3} [7, 11, 6, 8]
           {4} [3, 5, 3, 0] - -
              {5} [0, 0, 0, 3] - - - - - - -
           {4} [0, 4, 5, 0] - - - -
     {2} [14, 36, 12, 24] - - - -
  {1} [58, 75, 43, 53]
     {2} [49, 65, 67, 38] - - - -
     {2} [53, 54, 43, 54]
        {3} [2, 6, 5, 5] -
           {4} [2, 3, 5, 5] - -
              {5} [3, 4, 0, 0] - - - - -
           {4} [4, 7, 4, 3] - -
              {5} [4, 0, 0, 3] - - - - - -
        {3} [11, 23, 22, 17] - -
           {4} [4, 14, 4, 12]
              {5} [3, 0, 1, 0] - - - - - - - - - -
     {2} [23, 59, 29, 39]
        {3} [20, 3, 6, 8] -
           {4} [7, 4, 4, 3] - -
              {5} [3, 0, 3, 0]
                 {6} [0, 1, 0, 4] - - - - - - - - - - -
        {3} [7, 10, 3, 6] -
           {4} [2, 19, 9, 4] - - - -
           {4} [13, 8, 6, 7] - -
              {5} [1, 9, 0, 2] - - - - -
           {4} [12, 16, 8, 4] - - -
              {5} [2, 0, 0, 3] - - - - - -
  {1} [0, 0, 0, 0]
     {2} [52, 54, 70, 27]
        {3} [12, 12, 8, 8] - - -
           {4} [0, 0, 3, 0] - - - - - - -
     {2} [24, 39, 57, 32]
        {3} [10, 14, 23, 7] - - -
           {4} [3, 0, 0, 3] -
              {5} [0, 4, 3, 0] - - - - - - - -
        {3} [4, 8, 3, 9] -
           {4} [2, 1, 13, 3] - - - - - -
     {2} [57, 106, 90, 47] - - -
        {3} [17, 27, 19, 9] -
           {4} [3, 11, 6, 4] -
              {5} [2, 3, 5, 4] -
                 {6} [3, 4, 0, 0] - - - - - - - - - -
     {2} [27, 42, 63, 25]
        {3} [12, 13, 19, 12]
           {4} [2, 6, 1, 3] -
              {5} [0, 0, 3, 0] - - - - - - - - - -
        {3} [10, 15, 19, 5] - -
           {4} [2, 4, 12, 3] - -
              {5} [2, 4, 0, 2] - - - - - - -
  {1} [0, 0, 0, 0]
     {2} [8, 28, 52, 14] - -
        {3} [1, 7, 12, 10]
           {4} [2, 7, 7, 0] - - - - - - - -
     {2} [11, 21, 41, 7]
        {3} [13, 7, 6, 12] - - - -
        {3} [5, 22, 31, 12] -
           {4} [2, 7, 12, 4] - - -
              {5} [1, 0, 0, 3] - - - - - - -
        {3} [4, 15, 15, 7] - -
           {4} [0, 6, 0, 0] -
              {5} [0, 0, 0, 3] - - - -
              {5} [0, 0, 3, 0] - - - - - -
     {2} [6, 18, 40, 25] - -
        {3} [8, 21, 29, 6] - - - -
        {3} [4, 15, 9, 22] - - - -
     {2} [9, 34, 26, 32]
        {3} [7, 11, 4, 2] - - - - - -
        {3} [5, 10, 19, 3] - - - -
> ## End Don't show
> 
> 
> 
> cleanEx(); ..nameEx <- "vlmc"
> 
> ### * vlmc
> 
> flush(stderr()); flush(stdout())
> 
> ### Encoding: latin1
> 
> ### Name: vlmc
> ### Title: Fit a Variable Length Markov Chain (VLMC)
> ### Aliases: vlmc print.vlmc is.vlmc
> ### Keywords: ts models
> 
> ### ** Examples
> 
> f1 <- c(1,0,0,0)
> f2 <- rep(1:0,2)
> (dt1 <- c(f1,f1,f2,f1,f2,f2,f1))
 [1] 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0
> 
> (vlmc.dt1  <- vlmc(dt1))

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1);	default cutoff = 1.921
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        3         4         3        26 
AIC =  21.46 
>  vlmc(dt1, dump = 1,
+       ctl.dump = c(wid = 3, nmax = 20), debug = TRUE)
vlmc: Alpha = '01' ; |X| = 2
vlmc: ctl.dump =  3 20 
vlmc: n = |data| = 28, cutoff{prune} = 1.92073, threshold{gen} = 2
vlmc: |alphabet| = 2, alphabet = 01
generating... pruning... Dump{Tree} __after__ pruning: 
Lev Ch|   0   1 |  tot |  num size : ..{set->list}..
------+--------------------------------------------------
  0 x |  18  10 |   28 |   28   32 : 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ..
  1 0 |   8   9 |   17 |   17   32 : 2 3 4 6 7 8 10 12 14 15 16 18 20 22 24 26 27
  2 0 |   4   3 |    7 |    7   16 : 3 4 7 8 15 16 27
  3 0 |   0   3 |    3 |    3   16 : 4 8 16
  3 1 |   4   0 |    4 |    4   16 : 3 7 15 27
  1 1 |  10   0 |   10 |   10   16 : 1 5 9 11 13 17 19 21 23 25
computing differences[`completing'] ... writing tree...
[0]01
0 0 0
[1]1 4 6
[2]2 0 0
[3]3 0 3
- -1
- -1

[3]3 4 0
- -1
- -1


- -1

[1]1 10 0
- -1
- -1



`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, debug = TRUE, dump = 1, ctl.dump = c(wid = 3,     nmax = 20));	default cutoff = 1.921
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        3         4         3        26 
AIC =  21.46 
> (vlmc.dt1c01 <- vlmc(dts = dt1, cutoff.prune = .1, dump=1))
Lev Ch|  0  1 | tot | num size : ..{set->list}..
------+---------------------------------------------
  0 x | 18 10 |  28 |  28  32 : 0 1 2 3 4 5 6 7 8 9 10 11 12 ..
  1 0 |  8  9 |  17 |  17  32 : 2 3 4 6 7 8 10 12 14 15 16 18 20 ..
  2 0 |  4  3 |   7 |   7  16 : 3 4 7 8 15 16 27
  3 0 |  0  3 |   3 |   3  16 : 4 8 16
  3 1 |  4  0 |   4 |   4  16 : 3 7 15 27
  2 1 |  4  6 |  10 |  10  16 : 2 6 10 12 14 18 20 22 24 26
  3 0 |  3  6 |   9 |   9  16 : 6 10 12 14 18 20 22 24 26
  4 0 |  1  2 |   3 |   3  16 : 6 10 18
  5 0 |  1  2 |   3 |   3  16 : 6 10 18
  6 1 |  1  2 |   3 |   3  16 : 6 10 18
  7 0 |  0  2 |   2 |   2  16 : 10 18
  4 1 |  2  4 |   6 |   6  16 : 12 14 20 22 24 26
  5 0 |  2  4 |   6 |   6  16 : 12 14 20 22 24 26
  6 0 |  0  2 |   2 |   2  16 : 12 20
  6 1 |  2  2 |   4 |   4  16 : 14 22 24 26
  1 1 | 10  0 |  10 |  10  16 : 1 5 9 11 13 17 19 21 23 25

`vlmc', a Variable Length Markov Chain;
	 alphabet '01', |alphabet| = 2, n = 28.
Call: vlmc(dts = dt1, cutoff.prune = 0.1, dump = 1)
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        7        11         6        66 
AIC =  27.55 
> 
> data(presidents)
> dpres <- cut(presidents, c(0,45,70, 100)) # three values + NA
> table(dpres <- factor(dpres, exclude = NULL)) # NA as 4th level

  (0,45]  (45,70] (70,100]     <NA> 
      28       60       26        6 
> vlmc.pres <- vlmc(dpres, debug = TRUE)
Warning in vlmc(dpres, debug = TRUE) : alphabet with >1-letter strings; trying to abbreviate
vlmc: Alpha = '0123' ; |X| = 4
vlmc: ctl.dump =  3.079181 -1 
vlmc: n = |data| = 120, cutoff{prune} = 3.90736, threshold{gen} = 2
vlmc: |alphabet| = 4, alphabet = 0123
generating... pruning... computing differences[`completing'] ... writing tree...
[0]0123
0 1 2 1 2
[1]1 18 7 0 2
- -1
- -1
- -1
- -1

[1]1 9 33 5 1
- -1
- -1
[2]2 0 6 6 0
- -1
- -1
- -1
- -1

- -1

[1]1 0 12 14 0
- -1
- -1
- -1
- -1

- -1

> vlmc.pres

`vlmc', a Variable Length Markov Chain;
	 alphabet '0123', |alphabet| = 4, n = 120.
Call: vlmc(dts = dpres, debug = TRUE);	default cutoff = 3.907
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        2         5         3        42 
AIC =  227.6 
> 
> ## alphabet & and its length:
> vlmc.pres$alpha
[1] "0123"
> stopifnot(
+   length(print(strsplit(vlmc.pres$alpha,NULL)[[1]])) == vlmc.pres$ alpha.len
+ )
[1] "0" "1" "2" "3"
> 
> 
> 
> cleanEx(); ..nameEx <- "vlmc.version"
> 
> ### * vlmc.version
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: vlmc.version
> ### Title: Version of VLMC Package
> ### Aliases: vlmc.version
> ### Keywords: data
> 
> ### ** Examples
> 
> vlmc.version
[1] "VLMC 1.3-8;  after $Date: 2005/03/25 13:48:33 $ UTC"
> ## Not run: 
> ##D [1] "VLMC 1.3-7;  after $Date: 2004/05/05 16:41:47 $ UTC"
> ## End(Not run)
> 
> 
> 
> cleanEx(); ..nameEx <- "vlmctree"
> 
> ### * vlmctree
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: vlmctree
> ### Title: Compute the tree structure of a "vlmc" object
> ### Aliases: vlmctree .vvec2tree str.vtree
> ### Keywords: ts models
> 
> ### ** Examples
> 
> data(presidents)
> dpres <- cut(presidents, c(0,45,70, 100)) # three values + NA
> table(dpres <- factor(dpres, exclude = NULL)) # NA as 4th level

  (0,45]  (45,70] (70,100]     <NA> 
      28       60       26        6 
> 
> (vlmc.prc1 <- vlmc(dpres, cut = 1, debug = TRUE))
Warning in vlmc(dpres, cut = 1, debug = TRUE) : 
	 alphabet with >1-letter strings; trying to abbreviate
vlmc: Alpha = '0123' ; |X| = 4
vlmc: ctl.dump =  3.079181 -1 
vlmc: n = |data| = 120, cutoff{prune} = 1, threshold{gen} = 2
vlmc: |alphabet| = 4, alphabet = 0123
generating... pruning... computing differences[`completing'] ... writing tree...
[0]0123
0 0 0 0 0
[1]1 11 5 0 2
- -1
[2]2 3 2 0 0
- -1
[3]3 4 0 0 0
- -1
- -1
- -1
- -1

- -1
- -1

- -1
- -1

[1]1 1 1 0 0
[2]2 2 2 1 0
- -1
[3]3 2 0 0 0
- -1
- -1
- -1
- -1

- -1
- -1

[2]2 1 7 1 0
- -1
[3]3 0 1 0 0
[4]4 1 1 0 0
- -1
- -1
- -1
- -1

[4]4 1 3 0 0
- -1
[5]5 0 3 0 0
- -1
[6]6 0 12 1 1
- -1
- -1
[7]7 1 1 0 0
- -1
- -1
- -1
- -1

- -1

- -1
- -1

- -1
- -1

[4]4 0 2 2 0
- -1
- -1
- -1
- -1

- -1

- -1
- -1

[2]2 0 4 3 0
- -1
[3]3 0 0 0 0
- -1
[4]4 0 2 0 0
- -1
- -1
- -1
- -1

[4]4 0 0 3 0
- -1
- -1
- -1
- -1

- -1

- -1
- -1

- -1

[1]1 0 5 7 0
- -1
- -1
[2]2 0 4 3 0
- -1
- -1
[3]3 0 3 2 0
- -1
[4]4 0 0 2 0
- -1
- -1
- -1
- -1

- -1
- -1

- -1

- -1

[1]1 0 0 1 1
[2]2 1 0 0 1
- -1
- -1
- -1
- -1

- -1
- -1
[2]2 0 2 0 0
- -1
- -1
- -1
- -1




`vlmc', a Variable Length Markov Chain;
	 alphabet '0123', |alphabet| = 4, n = 120.
Call: vlmc(dts = dpres, cutoff.prune = 1, debug = TRUE)
 -> extensions (= $size ) :
   ord.MC   context nr.leaves     total 
        7        25        10       210 
AIC =  292.3 
> str(vv.prc1 <- vlmctree(vlmc.prc1))
`vtree':
 {7} 0 0 0 0 | 0 ;  4 children 
 ..  {2} 11  5  0  2 | 18 ;  4 children 
 .. ..  {1} 3 2 0 0 | 5 ;  4 children 
 .. .. ..  {0} 4 0 0 0 | 4 ;  _leaf_ 
 ..  {6} 1 1 0 0 | 2 ;  4 children 
 .. ..  {1} 2 2 1 0 | 5 ;  4 children 
 .. .. ..  {0} 2 0 0 0 | 2 ;  _leaf_ 
 .. ..  {5} 1 7 1 0 | 9 ;  4 children 
 .. .. ..  {4} 0 1 0 0 | 1 ;  4 children 
 .. .. .. ..  {0} 1 1 0 0 | 2 ;  _leaf_ 
 .. .. .. ..  {3} 1 3 0 0 | 4 ;  4 children 
 .. .. .. .. ..  {2} 0 3 0 0 | 3 ;  4 children 
 .. .. .. .. .. ..  {1}  0 12  1  1 | 14 ;  4 children 
 .. .. .. .. .. .. ..  {0} 1 1 0 0 | 2 ;  _leaf_ 
 .. .. .. ..  {0} 0 2 2 0 | 4 ;  _leaf_ 
 .. ..  {2} 0 4 3 0 | 7 ;  4 children 
 .. .. ..  {1} 0 0 0 0 | 0 ;  4 children 
 .. .. .. ..  {0} 0 2 0 0 | 2 ;  _leaf_ 
 .. .. .. ..  {0} 0 0 3 0 | 3 ;  _leaf_ 
 ..  {3} 0 5 7 0 | 12 ;  4 children 
 .. ..  {2} 0 4 3 0 | 7 ;  4 children 
 .. .. ..  {1} 0 3 2 0 | 5 ;  4 children 
 .. .. .. ..  {0} 0 0 2 0 | 2 ;  _leaf_ 
 ..  {1} 0 0 1 1 | 2 ;  4 children 
 .. ..  {0} 1 0 0 1 | 2 ;  _leaf_ 
 .. ..  {0} 0 2 0 0 | 2 ;  _leaf_ 
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  13.51 0.97 14.79 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')