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("normalp-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('normalp')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "dnormp"
> 
> ### * dnormp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dnormp
> ### Title: Density function of an exponential power distribution
> ### Aliases: dnormp
> ### Keywords: distribution
> 
> ### ** Examples
> 
> ## Compute the density for a vector x with mu=0, sigmap=1 and p=1.5
> ## At the end we have the graph of the exponential power distribution 
> ## density function with p=1.5
> x <- c(-1, 1)
> f <- dnormp(x, p=1.5)
> print(f)
[1] 0.2170106 0.2170106
> plot(function(x) dnormp(x, p=1.5) , -4, 4,
+           main = "Exponential power distribution density function (p=1.5)", ylab="f(x)")
> 
> 
> 
> cleanEx(); ..nameEx <- "estimatep"
> 
> ### * estimatep
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: estimatep
> ### Title: Estimation of p
> ### Aliases: estimatep
> ### Keywords: univar
> 
> ### ** Examples
> 
> x<-rnormp(300,mu=1,sigmap=2,p=4)
> p<-estimatep(x,mu=1,p=2)
> p
[1] 5.531964
> 
> 
> 
> cleanEx(); ..nameEx <- "graphnp"
> 
> ### * graphnp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: graphnp
> ### Title: Plot of exponential power distributions
> ### Aliases: graphnp
> ### Keywords: aplot
> 
> ### ** Examples
> 
> ## Plot four different curves with p=1,2,3,4
> ## and 50 (it will plot an uniform distribution)
> graphnp(c(1:4,50))
> 
> 
> 
> cleanEx(); ..nameEx <- "kurtosis"
> 
> ### * kurtosis
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kurtosis
> ### Title: Indices of kurtosis
> ### Aliases: kurtosis
> ### Keywords: univar
> 
> ### ** Examples
> 
> kurtosis(p=2)
      VI       B2       Bp 
1.253314 3.000000 3.000000 
> x<-rnormp(50,mu=0,sigmap=2,p=1.5)
> kurtosis(x,p=2)
      VI       B2       Bp 
1.281927 3.956532 2.412952 
> 
> 
> 
> cleanEx(); ..nameEx <- "lmp"
> 
> ### * lmp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lmp
> ### Title: Fitted linear model with exponential power distribution errors
> ### Aliases: lmp lmp.default
> ### Keywords: regression
> 
> ### ** Examples
> 
> e<-rnormp(n=100,mu=0,sigmap=4,p=3,method="d")
> x<-runif(100)
> y<-0.5+2*x+e
> lmp(y~x)

Call:
lmp(formula = y ~ x)

Coefficients:
(Intercept)            x  
     0.6961       1.3703  

> 
> 
> 
> cleanEx(); ..nameEx <- "paramp"
> 
> ### * paramp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: paramp
> ### Title: Estimation of location and scale parameters
> ### Aliases: paramp paramp.default print.paramp
> ### Keywords: univar
> 
> ### ** Examples
> 
> x<-rnormp(1000,2,3,4.2)
> paramp(x)
    Mean       Mp       Sd       Sp        p 
1.975826 2.033102 2.478101 3.071740 4.364924 

no.conv = FALSE 

> 
> 
> 
> cleanEx(); ..nameEx <- "plot.lmp"
> 
> ### * plot.lmp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.lmp
> ### Title: Diagnostic plots for a lmp object
> ### Aliases: plot.lmp
> ### Keywords: hplot
> 
> ### ** Examples
> 
> x<-1:20
> z<-runif(20)
> e<-rnormp(20,mu=0,sigmap=1,p=3)
> y<-0.5+x+z+e
> lmp.res<-lmp(y~x+z)
> plot(lmp.res)
Hit <Return> to see next plot: 
Hit <Return> to see next plot: 
Hit <Return> to see next plot: 
> cleanEx(); ..nameEx <- "plot.simul.lmp"
> 
> ### * plot.simul.lmp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.simul.lmp
> ### Title: Plots of the results of a simulation plan on a linear regression
> ###   model
> ### Aliases: plot.simul.lmp
> ### Keywords: hplot
> 
> ### ** Examples
> 
> sim<-simul.lmp(n=10,m=50,q=1,data=1.5,int=0,sigmap=1,p=3.5)
> plot(sim)
Hit <Return> to see next plot: 
Hit <Return> to see next plot: 
Hit <Return> to see next plot: 
> cleanEx(); ..nameEx <- "plot.simul.mp"
> 
> ### * plot.simul.mp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.simul.mp
> ### Title: Plots of the results of a simulation plan on the parameters of
> ###   an exponential power distribution
> ### Aliases: plot.simul.mp
> ### Keywords: hplot
> 
> ### ** Examples
> 
> ## The histograms of all the computed estimates
> a<-simul.mp(100,50,mu=0,sigmap=1,p=3)
> plot(a)
Hit <Return> to see next plot: 
Hit <Return> to see next plot: 
Hit <Return> to see next plot: 
Hit <Return> to see next plot: cleanEx(); ..na> meEx <- "pnormp"
> 
> ### * pnormp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pnormp
> ### Title: Probability function of an exponential power distribution
> ### Aliases: pnormp
> ### Keywords: distribution
> 
> ### ** Examples
> 
> ## Compute the distribution function for a vector x with mu=0, sigmap=1 and p=1.5
> ## At the end we have the graph of the exponential power distribution function with p=1.5.
> x <- c(-1, 1)
> pr <- pnormp(x, p=1.5)
> print(pr)
[1] 0.1699012 0.8300988
> plot(function(x) pnormp(x, p=1.5), -4, 4,
+           main = "Exponential Power Distribution Function (p=1.5)", ylab="F(x)")
> 
> 
> 
> cleanEx(); ..nameEx <- "qnormp"
> 
> ### * qnormp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: qnormp
> ### Title: Quantiles of an exponential power distribution
> ### Aliases: qnormp
> ### Keywords: distribution
> 
> ### ** Examples
> 
> ## Compute the quantiles for a vector of probabilities x
> ## with mu=1, sigmap=2 and p=1.5
> x <- 0.3
> q <- qnormp(x, 1, 2, 1.5)
> q
[1] -0.0434479
> 
> 
> 
> cleanEx(); ..nameEx <- "qqnormp"
> 
> ### * qqnormp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: qqnormp
> ### Title: Quantile-Quantile plot for an exponential power distribution
> ### Aliases: qqnormp qqnormp.default qqlinep
> ### Keywords: hplot
> 
> ### ** Examples
> 
> ## Exponential power distribution Q-Q plot for a sample of 100 observations.
> e<-rnormp(100,mu=0,sigmap=1,p=3)
> qqnormp(e,p=3)
> qqlinep(e,p=3)
> 
> 
> 
> cleanEx(); ..nameEx <- "rnormp"
> 
> ### * rnormp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rnormp
> ### Title: Pseudo-random numbers from an exponential power distribution
> ### Aliases: rnormp
> ### Keywords: distribution
> 
> ### ** Examples
> 
> ## Generate a random sample x from an exponential power distribution
> ## At the end we have the histogram of x
> x <- rnormp(1000, 1, 2, 1.5)
> hist(x, main="Histogram of the random sample")
> 
> 
> 
> cleanEx(); ..nameEx <- "simul.lmp"
> 
> ### * simul.lmp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: simul.lmp
> ### Title: Simulation planning for a linear regression model with errors
> ###   distributed as an exponential power distribution
> ### Aliases: simul.lmp
> ### Keywords: regression
> 
> ### ** Examples
> 
> ## Simulation of 50 samples of size 10 for a linear regression model with 1 regressor.
> simul.lmp(10,50,1,data=1.5,int=1,sigmap=1,p=3,lp=FALSE)
         (intercept)       x1         Sp         p
Mean       0.8869702 1.664769 0.93954806  3.545009
Variance   0.4414876 1.367305 0.07136779 14.023322

Number of samples with a difficult convergence: 1 
> 
> 
> 
> cleanEx(); ..nameEx <- "simul.mp"
> 
> ### * simul.mp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: simul.mp
> ### Title: Simulation planning for the parameters of an exponential power
> ###   distribution
> ### Aliases: simul.mp print.simul.mp simul.mp.default
> ### Keywords: univar
> 
> ### ** Examples
> 
> ## Simulation plan for 100 samples of size 20, with mu=0, sigmap=1, p=3.
> simul.mp(20,100,mu=0,sigmap=1,p=3)
                 Mean           Mp         Sd         Sp        p
Mean     -0.001455832 -0.002005936 0.85957678 0.91897954 2.621267
Variance  0.043359067  0.056024484 0.01065886 0.04360268 5.159214

Number of samples with a difficult convergence: 2 
> 
> 
> 
> cleanEx(); ..nameEx <- "summary.lmp"
> 
> ### * summary.lmp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: summary.lmp
> ### Title: Summarize linear model fits with exponential power distribution
> ###   errors
> ### Aliases: summary.lmp print.summary.lmp
> ### Keywords: regression
> 
> ### ** Examples
> 
> x<-runif(30)
> e<-rnormp(30,0,3,1.25)
> y<-0.5+x+e
> L<-lmp(y~x)
> summary(L)

Call:
lmp(formula = y ~ x)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.0304 -2.3054 -0.2569  2.7494  5.7489 

Coefficients:
(Intercept)           x 
      0.893       1.001 

Estimate of p
`2.386026`

Power deviation of order p: 3.391
> 
> 
> 
> cleanEx(); ..nameEx <- "summary.simul.lmp"
> 
> ### * summary.simul.lmp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: summary.simul.lmp
> ### Title: Summarize simulation results on linear regression model
> ### Aliases: summary.simul.lmp print.summary.simul.lmp
> ### Keywords: regression
> 
> ### ** Examples
> 
> ris<-simul.lmp(100,20,2,data=c(3,2),int=0,sigmap=1,p=3)
> summary(ris)
Results:
         (intercept)         x1       x2         Sp        p
Mean      0.01693667 2.94269215 2.044959 0.98760052 3.245725
Variance  0.08861216 0.07691343 0.131576 0.01651496 2.023159

Coefficients:
(intercept)          x1          x2 
          0           3           2 

Formula:
y ~ +x1 + x2

Number of samples:
`20`

Value of p:
`3`

Number of samples with problems on convergence
`0`

> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  5.8 0.07 5.91 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')