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("labstatR-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('labstatR')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "COV"
> 
> ### * COV
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: COV
> ### Title: Calcola la covarianza non corretta
> ### Aliases: COV
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> x <- c(1,3,2,4,6,7)
> y <- c(7,3,2,1,-1,-3)
> cov(x,y)
[1] -7.5
> COV(x,y)
[1] -6.25
> 
> 
> 
> cleanEx(); ..nameEx <- "E"
> 
> ### * E
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: E
> ### Title: Calcola l'indice di eterogeneita'
> ### Aliases: E
> ### Keywords: univar
> 
> ### ** Examples
> 
> x <- c("A", "A", "B", "A", "C", "A")
> E(x)
[1] 0.75
> 
> 
> 
> cleanEx(); ..nameEx <- "Markov"
> 
> ### * Markov
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Markov
> ### Title: Simulatore di catene di Markov
> ### Aliases: Markov Markov2
> ### Keywords: distribution
> 
> ### ** Examples
> 
> x <- c("P","S","N")
> P <- matrix(c(0.5,0.5,0.25,0.25,0,0.25,0.25,0.5,0.5),3,3)
> Markov("S",15,x,P)  -> traj
> traj
$X
 [1] "S" "P" "P" "S" "N" "P" "N" "N" "N" "N" "P" "P" "P" "S" "P" "N"

$t
 [1]  0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15

> plot(traj$t,unclass(factor(traj$X)),type="s",axes=FALSE,
+  xlab="t",ylab="Che tempo fa'")
> axis(1)
> axis(2,c(1,2,3),levels(factor(traj$X)))
> box()
> 
> 
> 
> cleanEx(); ..nameEx <- "Me"
> 
> ### * Me
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Me
> ### Title: Calcola la mediana anche per fenomeni qualitativi
> ### Aliases: Me
> ### Keywords: univar
> 
> ### ** Examples
> 
> x <- factor(c("A", "B", "A", "C", "A"))
> Me(x)
Warning in Me(x) : La mediana non si puo' calcolare!!!
[1] NA
> 
> 
> 
> cleanEx(); ..nameEx <- "Rp"
> 
> ### * Rp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Rp
> ### Title: Calcola l'allocazione ottimale di un portafoglio
> ### Aliases: Rp
> ### Keywords: distribution
> 
> ### ** Examples
> 
> x <- c(11,9,25,7,-2)/100
> y <- c(-3,15,2,20,6)/100
> pxy <- matrix(rep(0,25),5,5)
> pxy[1,1] <- 0.2
> pxy[2,2] <- 0.2
> pxy[3,3] <- 0.2
> pxy[4,4] <- 0.2
> pxy[5,5] <- 0.2
> Rp(x,y,pxy)
$a
[1] 0.486653

$Rm
[1] 0.08973306

$VR
[1] 0.00246653

> 
> 
> 
> cleanEx(); ..nameEx <- "Rpa"
> 
> ### * Rpa
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Rpa
> ### Title: Calcola il rendimento di un portafoglio
> ### Aliases: Rpa
> ### Keywords: distribution
> 
> ### ** Examples
> 
> x <- c(11,9,25,7,-2)/100
> y <- c(-3,15,2,20,6)/100
> pxy <- matrix(rep(0,25),5,5)
> pxy[1,1] <- 0.2
> pxy[2,2] <- 0.2
> pxy[3,3] <- 0.2
> pxy[4,4] <- 0.2
> pxy[5,5] <- 0.2
> Rpa(0.1,x,y,pxy)
$Rm
[1] 0.082

$VR
[1] 0.0053788

> Rpa(0.5,x,y,pxy)
$Rm
[1] 0.09

$VR
[1] 0.00247

> 
> 
> 
> cleanEx(); ..nameEx <- "birthday"
> 
> ### * birthday
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: birthday
> ### Title: Calcola la probabilita' di compleanni coincidenti
> ### Aliases: birthday
> ### Keywords: distribution
> 
> ### ** Examples
> 
> n <- c(5,10,15,20,21,22,23,24,25,30,50,60,
+        70,80,90,100,200,300,365)
> for(i in n)
+  cat("\n n=",i,"P(A)=",birthday(i))

 n= 5 P(A)= 0.02713557
 n= 10 P(A)= 0.1169482
 n= 15 P(A)= 0.2529013
 n= 20 P(A)= 0.4114384
 n= 21 P(A)= 0.4436883
 n= 22 P(A)= 0.4756953
 n= 23 P(A)= 0.5072972
 n= 24 P(A)= 0.5383443
 n= 25 P(A)= 0.5686997
 n= 30 P(A)= 0.7063162
 n= 50 P(A)= 0.9703736
 n= 60 P(A)= 0.9941227
 n= 70 P(A)= 0.9991596
 n= 80 P(A)= 0.9999143
 n= 90 P(A)= 0.9999938
 n= 100 P(A)= 0.9999997
 n= 200 P(A)= 1
 n= 300 P(A)= 1
 n= 365 P(A)= 1> 
> 
> 
> cleanEx(); ..nameEx <- "bubbleplot"
> 
> ### * bubbleplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: bubbleplot
> ### Title: Disegna un grafico a bolle
> ### Aliases: bubbleplot
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> x <- c("O","O","S","B","S","O","B","B","S",
+    "B","O","B","B","O","S")
> y <- c("O","B","B","B","S","S","O","O","B",
+    "B","O","S","B","S","B")
> x <- ordered(x, levels=c("S","B","O")) 
> y <- ordered(y, levels=c("S","B","O")) 
> table(x,y)
   y
x   S B O
  S 1 3 0
  B 1 3 2
  O 2 1 2
> bubbleplot(table(x,y),main="Musica versus Pittura")
> 
> 
> 
> cleanEx(); ..nameEx <- "chi2"
> 
> ### * chi2
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: chi2
> ### Title: Calcola l'indice di connessione
> ### Aliases: chi2
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> x <- rbinom(8,5,0.5)
> y <- c("A", "A", "B", "A", "B", "B", "C", "B")
> chi2(x,y)
[1] 0.2777778
> 
> 
> 
> cleanEx(); ..nameEx <- "cv"
> 
> ### * cv
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cv
> ### Title: Calcola il coefficiente di variazione
> ### Aliases: cv
> ### Keywords: univar
> 
> ### ** Examples
> 
> x <- c(1,3,2,4,6,7)
> cv(x)
[1] 0.5516773
> 
> 
> 
> cleanEx(); ..nameEx <- "eta"
> 
> ### * eta
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: eta
> ### Title: Calcola l'indice di dipendenza in media
> ### Aliases: eta
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> x <- c(rep(1,10),rep(0,23), rep(2,15))
> y <- c(rnorm(10,mean=7),rnorm(23,mean=19),rnorm(15,mean=17))
> eta(x,y)
[1] 0.9687662
> y <- c(rnorm(10,mean=8),rnorm(23,mean=7),rnorm(15,mean=6.5))
> eta(x,y)
[1] 0.2093498
> 
> 
> 
> cleanEx(); ..nameEx <- "gen.vc"
> 
> ### * gen.vc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gen.vc
> ### Title: Simula una variabile casuale discreta
> ### Aliases: gen.vc
> ### Keywords: distribution
> 
> ### ** Examples
> 
> x <- c(-2,3,7,10,12)
> p <- c(0.2, 0.1, 0.4, 0.2, 0.1) 
> y <- NULL
> for(i in 1:1000) y <- c(y,gen.vc(x,p))
> table(y)/length(y)
y
   -2     3     7    10    12 
0.200 0.093 0.403 0.194 0.110 
> 
> 
> 
> cleanEx(); ..nameEx <- "gini"
> 
> ### * gini
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gini
> ### Title: Calcola l'indice di concetrazione
> ### Aliases: gini
> ### Keywords: univar
> 
> ### ** Examples
> 
> x <- c(1,3,4,30,100)
> gini(x)
$G
[1] 0.8152174

$R
[1] 0.6521739

$P
[1] 0.0 0.2 0.4 0.6 0.8 1.0

$Q
[1] 0.000000000 0.007246377 0.028985507 0.057971014 0.275362319 1.000000000

> y <- c(10,10,10,10)
> gini(y, add=TRUE,col="red")
$G
[1] 0

$R
[1] 0

$P
[1] 0.00 0.25 0.50 0.75 1.00

$Q
[1] 0.00 0.25 0.50 0.75 1.00

> 
> 
> 
> cleanEx(); ..nameEx <- "gioco1"
> 
> ### * gioco1
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gioco1
> ### Title: Simula la scommessa di De Mere
> ### Aliases: gioco1 gioco1a
> ### Keywords: distribution
> 
> ### ** Examples
> 
> ptm <- proc.time()
> gioco1a(10000)
[1] 0.5195
> proc.time() - ptm
[1] 0.19 0.01 0.19 0.00 0.00
> ptm <- proc.time()
> gioco1(10000)
[1] 0.5196
> proc.time() - ptm
[1] 1.00 0.02 1.16 0.00 0.00
> 
> 
> 
> cleanEx(); ..nameEx <- "gioco2"
> 
> ### * gioco2
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gioco2
> ### Title: Simula la scommessa di De Mere
> ### Aliases: gioco2 gioco2a
> ### Keywords: distribution
> 
> ### ** Examples
> 
> ptm <- proc.time()
> gioco2a(10000)
[1] 0.4986
> proc.time() - ptm
[1] 0.33 0.03 0.36 0.00 0.00
> ptm <- proc.time()
> gioco2(10000)
[1] 0.4907
> proc.time() - ptm
[1] 5.57 0.01 6.12 0.00 0.00
> 
> 
> 
> cleanEx(); ..nameEx <- "hist.pf"
> 
> ### * hist.pf
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: hist.pf
> ### Title: Disegna il poligono di frequenza
> ### Aliases: hist.pf
> ### Keywords: univar
> 
> ### ** Examples
> 
> x <- rnorm(50)
> hist.pf(x,br=5)
> 
> 
> 
> cleanEx(); ..nameEx <- "ic.var"
> 
> ### * ic.var
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ic.var
> ### Title: Calcola intervallo di confidenza per la varianza
> ### Aliases: ic.var
> ### Keywords: univar
> 
> ### ** Examples
> 
> x <- c(0.39, 0.68, 0.82, 1.35, 1.38, 1.62, 1.70,
+        1.71, 1.85, 2.14, 2.89, 3.69)
> ic.var(x)
[1] 0.4266368 2.4508692
> ic.var(x,FALSE)
[1] 0.000000 2.044215
> 
> 
> 
> cleanEx(); ..nameEx <- "interinale"
> 
> ### * interinale
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: interinale
> ### Title: Dati sul lavoro interinale
> ### Aliases: interinale
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(interinale)
> glm(avviato~., binomial, data=interinale) -> model
> model

Call:  glm(formula = avviato ~ ., family = binomial, data = interinale) 

Coefficients:
       (Intercept)              sessoM                 eta        esperienzaSI  
          -2.08711             0.10766            -0.03943             0.23706  
       corsispecSI       informaticaSI            lingueSI        mezzitraspSI  
           1.63760             0.19965            -0.32134             0.32126  
   istruzionemedia      istruzioneuniv     arearesNord-Est   arearesNord-Ovest  
          -0.12745            -0.17650             0.46722             0.58942  
arearesSud e Isole  
          -1.02883  

Degrees of Freedom: 17275 Total (i.e. Null);  17263 Residual
Null Deviance:	    7528 
Residual Deviance: 6937 	AIC: 6963 
> pr <- predict(model, type="response")
> plot(density(pr),xlim=c(0,0.2),main="")
> 
> 
> 
> cleanEx(); ..nameEx <- "kurt"
> 
> ### * kurt
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kurt
> ### Title: Calcola l'indice di curtosi
> ### Aliases: kurt
> ### Keywords: univar
> 
> ### ** Examples
> 
> x <- rnorm(50)
> kurt(x)
[1] 3.406608
> y <- rt(50,df=1)
> kurt(y)
[1] 12.45768
> 
> 
> 
> cleanEx(); ..nameEx <- "lewis"
> 
> ### * lewis
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lewis
> ### Title: Simulatore di processi di Poisson
> ### Aliases: lewis
> ### Keywords: distribution
> 
> ### ** Examples
> 
> lewis(20,sin)
> 
> 
> 
> cleanEx(); ..nameEx <- "mean.a"
> 
> ### * mean.a
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mean.a
> ### Title: Calcola la media armonica
> ### Aliases: mean.a
> ### Keywords: univar
> 
> ### ** Examples
> 
> x <- c(1,3,2,4,6,7)
> mean.a(x)
[1] 2.507463
> 
> 
> 
> cleanEx(); ..nameEx <- "mean.g"
> 
> ### * mean.g
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mean.g
> ### Title: Calcola la media geometrica
> ### Aliases: mean.g
> ### Keywords: univar
> 
> ### ** Examples
> 
> x <- c(1,3,2,4,6,7)
> mean.g(x)
[1] 3.16648
> 
> 
> 
> cleanEx(); ..nameEx <- "sigma2"
> 
> ### * sigma2
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: sigma2
> ### Title: Calcola la varianza non corretta
> ### Aliases: sigma2
> ### Keywords: univar
> 
> ### ** Examples
> 
> x <- rnorm(10)
> var(x)
[1] 0.6093144
> sigma2(x)
[1] 0.548383
> 
> 
> 
> cleanEx(); ..nameEx <- "skew"
> 
> ### * skew
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: skew
> ### Title: Calcola l'indice di asimmetria
> ### Aliases: skew
> ### Keywords: univar
> 
> ### ** Examples
> 
> x <- rnorm(50)
> skew(x)
[1] -0.6610509
> y <- rchisq(50,df=1)
> skew(y)
[1] 2.274165
> 
> 
> 
> cleanEx(); ..nameEx <- "test.var"
> 
> ### * test.var
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: test.var
> ### Title: Calcola intervallo di confidenza per la varianza
> ### Aliases: test.var
> ### Keywords: univar
> 
> ### ** Examples
> 
> x <- rnorm(100, sd=5)
> var(x)
[1] 20.16905
> test.var(x,20)

 Ipotesi nulla => H0 : sigma2 = 20
 Varianza campionaria: 20.16905 , statistica test: 99.8368Warning in if (alternative == "greater") { : 
	 the condition has length > 1 and only the first element will be used

 p-value: 0.4575253 , livello del test: 0.05
 Quantile Chi-quadrato: 123.2252  con  99 gdl
 Ipotesi alternativa => H1 : sigma2 >  20
 Decisione: non si rifuta H0
> 
> 
> 
> cleanEx(); ..nameEx <- "trajectory"
> 
> ### * trajectory
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: trajectory
> ### Title: Simulatore di processi di diffusione
> ### Aliases: trajectory
> ### Keywords: distribution
> 
> ### ** Examples
> 
> n <- 100
> T <- 1
> x0 <- 1
> mu <- function(x,t) {-x*t}
> sigma <- function(x,t) {x*t}
> diff <- trajectory(1,0,1,mu,sigma,100)
> plot(diff$t,diff$y,type="l")
> acf(diff$y, main="Processo di diffusione")
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  10.44 1.33 12.72 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')