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("CircStats-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('CircStats')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "A1inv"
> 
> ### * A1inv
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: A1inv
> ### Title: Inverse of A1
> ### Aliases: A1inv
> ### Keywords: math
> 
> ### ** Examples
> 
> #Generate data from a von Mises distribution
> data <- rvm(50, pi, 4)
> #Estimate the concentration parameter
> s <- sum(sin(data))
> c <- sum(cos(data))
> mean.dir <- atan(s, c)
> kappa <- A1inv(mean(cos(data - mean.dir)))
> 
> 
> 
> cleanEx(); ..nameEx <- "circ.cor"
> 
> ### * circ.cor
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: circ.cor
> ### Title: Correlation Coefficient for Angular Variables
> ### Aliases: circ.cor
> ### Keywords: univar
> 
> ### ** Examples
> 
> # Generate two circular data sets, and compute their correlation.
> data1 <- rvm(50, 0, 3)
> data2 <- data1 + pi + rvm(50, 0, 10)
> circ.cor(data1, data2, test=TRUE)
          r test.stat      p.value
1 0.9197672  4.764692 1.891424e-06
> 
> 
> 
> cleanEx(); ..nameEx <- "circ.mean"
> 
> ### * circ.mean
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: circ.mean
> ### Title: Mean Direction
> ### Aliases: circ.mean
> ### Keywords: univar
> 
> ### ** Examples
> 
> # Compute the mean direction of a random sample of observations.
> data <- runif(50, 0, pi)
> mean.dir <- circ.mean(data)
> 
> 
> 
> cleanEx(); ..nameEx <- "circ.plot"
> 
> ### * circ.plot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: circ.plot
> ### Title: Circular Data Plot
> ### Aliases: circ.plot
> ### Keywords: hplot
> 
> ### ** Examples
> 
> # Generate 100 observations from a von Mises distribution.
> # with mean direction 0 and concentration 3.
> data.vm <- rvm(100, 0, 3) 
> # Plot data set. All points do not fit on plot.
> circ.plot(data.vm, stack=TRUE, bins=150) 
Loading required package: MASS
> # Shrink the plot so that all points fit.
> circ.plot(data.vm, stack=TRUE, bins=150, shrink=1.5) 
> 
> 
> 
> cleanEx(); ..nameEx <- "circ.range"
> 
> ### * circ.range
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: circ.range
> ### Title: Circular Range
> ### Aliases: circ.range nCk
> ### Keywords: univar htest
> 
> ### ** Examples
> 
> data <- rvm(50, 0, 2)
> circ.range(data, test=TRUE)
     range      p.value
1 4.231505 1.933770e-07
> data <- runif(50, 0, 2*pi)
> circ.range(data, test=TRUE)
     range    p.value
1 5.411513 0.03302356
> 
> 
> 
> cleanEx(); ..nameEx <- "circ.reg"
> 
> ### * circ.reg
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: circ.reg
> ### Title: Circular-Circular Regression
> ### Aliases: circ.reg
> ### Keywords: models
> 
> ### ** Examples
> 
> # Generate a data set of dependent circular variables.
> data1 <- runif(50, 0, 2*pi)
> data2 <- atan(0.15*cos(data1) + 0.25*sin(data1), 0.35*sin(data1)) + rvm(50, 0, 5)
> 
> # Fit a circular regression model.
> circ.lm <- circ.reg(data1, data2, order=1)
> # Obtain a crude plot a data and fitted regression line.
> plot(data1, data2)
> circ.lm$fitted[circ.lm$fitted>pi] <- circ.lm$fitted[circ.lm$fitted>pi] - 2*pi 
> 
> points(data1[order(data1)], circ.lm$fitted[order(data1)], type='l')
> 
> 
> 
> cleanEx(); ..nameEx <- "circ.summary"
> 
> ### * circ.summary
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: circ.summary
> ### Title: Circular Summary Statistics
> ### Aliases: circ.summary
> ### Keywords: misc
> 
> ### ** Examples
> 
> # Compute summary statistics of a random sample of observations. 
> data <- runif(50, 0, pi)
> circ.summary(data)
   n mean.dir       rho
1 50 1.703981 0.6815599
> 
> 
> 
> cleanEx(); ..nameEx <- "dwrpnorm"
> 
> ### * dwrpnorm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dwrpnorm
> ### Title: Wrapped Normal Density Function
> ### Aliases: dwrpnorm
> ### Keywords: distribution
> 
> ### ** Examples
> 
> # Values for which to evaluate density
> theta <- c(1:500)*2*pi/500
> #Compute wrapped normal density function
> density <- c(1:500)
> for(i in 1:500) density[i] <- dwrpnorm(theta[i], pi, .75)
> plot(theta, density)
> #Approximate area under density curve
> sum(density*2*pi/500)
[1] 1
> 
> 
> 
> cleanEx(); ..nameEx <- "est.kappa"
> 
> ### * est.kappa
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: est.kappa
> ### Title: Estimate Kappa
> ### Aliases: est.kappa
> ### Keywords: htest
> 
> ### ** Examples
> 
> data <- rvm(15, 0, 3)
> est.kappa(data)
[1] 3.672059
> est.kappa(data, bias=TRUE)
[1] 2.972310
> 
> 
> 
> cleanEx(); ..nameEx <- "est.rho"
> 
> ### * est.rho
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: est.rho
> ### Title: Mean Resultant Length
> ### Aliases: est.rho
> ### Keywords: htest
> 
> ### ** Examples
> 
> # Compute the mean resultant length of a random sample of observations.
> data <- runif(100, 0, 2*pi)
> est.rho(data)
[1] 0.09539008
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.edf"
> 
> ### * plot.edf
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.edf
> ### Title: Plot Empirical Distribution Function
> ### Aliases: plot.edf
> ### Keywords: hplot
> 
> ### ** Examples
> 
> # Compare the edf's of two simulated sets of data.
> data1 <- rvm(10, 0, 3)
> data2 <- rvm(10, 0, 1)
> plot.edf(data1, xlab="Data", ylab="EDF", main="Plots of Two EDF's")
> par(new=TRUE)
> plot.edf(data2, axes=FALSE, xlab="", ylab="", lty=2)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "pp.plot"
> 
> ### * pp.plot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pp.plot
> ### Title: von Mises Probability-Probability Plot
> ### Aliases: pp.plot
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data <- rvm(25, 0, 3)
> pp.plot(data)
   mu    kappa
1 0.5 13.41532
> data <- c(rvm(20, 0, 7), rvm(20, pi, 7))
> pp.plot(data)
   mu    kappa
1 0.5 13.03138
> 
> 
> 
> cleanEx(); ..nameEx <- "r.test"
> 
> ### * r.test
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: r.test
> ### Title: Rayleigh Test of Uniformity: General Unimodal Alternative
> ### Aliases: r.test
> ### Keywords: htest
> 
> ### ** Examples
> 
> data <- rvm(25, pi, 2)
> r.test(data)
$r.bar
[1] 0.6967402

$p.value
[1] 1.105626e-06

> 
> 
> 
> cleanEx(); ..nameEx <- "rose.diag"
> 
> ### * rose.diag
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rose.diag
> ### Title: Rose Diagram
> ### Aliases: rose.diag
> ### Keywords: hplot
> 
> ### ** Examples
> 
> # Generate uniform data and create several rose diagrams.  
> # Some optional parameters may be needed to optimize plots.
> data <- runif(50, 0, 2*pi)
> rose.diag(data, bins = 18, main = 'Uniform Data')
Loading required package: MASS
> rose.diag(data, bins = 18, main = 'Stacked Points', pts=TRUE)
> # Generate von Mises data and create several rose diagrams.
> data <- rvm(25, 0, 5)
> rose.diag(data, bins=18, pts=TRUE) # Points fall out of bounds.
> rose.diag(data, bins=36, prop=1.5, pts=TRUE, shrink=1.5)
> # Adjust optional parameters to fit all points on plot.
> 
> 
> 
> cleanEx(); ..nameEx <- "rstable"
> 
> ### * rstable
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rstable
> ### Title: Random Generator for Stable Family of Distributions
> ### Aliases: rstable
> ### Keywords: distribution
> 
> ### ** Examples
> 
> hist(rstable(200, 1.5, .5)) #fairly long tails, skewed right
> 
> 
> 
> cleanEx(); ..nameEx <- "v0.test"
> 
> ### * v0.test
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: v0.test
> ### Title: Rayleigh Test of Uniformity: Alternative with Specified Mean
> ###   Direction
> ### Aliases: v0.test
> ### Keywords: htest
> 
> ### ** Examples
> 
> data <- rvm(25, pi, 2)
> v0.test(data, mu0=pi)
$r0.bar
[1] 0.696599

$p.value
[1] 8.67713e-08

> 
> 
> 
> cleanEx(); ..nameEx <- "vm.bootstrap.ci"
> 
> ### * vm.bootstrap.ci
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: vm.bootstrap.ci
> ### Title: Bootstrap Confidence Intervals
> ### Aliases: vm.bootstrap.ci
> ### Keywords: htest
> 
> ### ** Examples
> 
> x <- rvm(25, 0, 3)
> x.bs <- vm.bootstrap.ci(x, alpha=.10)
Loading required package: boot
Bootstrap Confidence Intervals for Mean Direction and Concentration 
Confidence Level:   90 % 
Mean Direction:            Low = -0.26   High = 0.09 
Concentration Parameter:   Low = 2.93   High = 7.21 
> hist(x.bs$kappa.reps)
> 
> 
> 
> cleanEx(); ..nameEx <- "watson"
> 
> ### * watson
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: watson
> ### Title: Watson's Test
> ### Aliases: watson
> ### Keywords: htest
> 
> ### ** Examples
> 
> # Generate data from the uniform distribution on the circle.
> data <- runif(100, 0, 2*pi)  
> watson(data)

       Watson's Test for Circular Uniformity 
 
Test Statistic: 0.0903 
P-value > 0.10 
 
> # Generate data from a von Mises distribution.
> data <- rvm(50, 0, 4) 
> watson(data, 0.05, dist='vm')

       Watson's Test for the von Mises Distribution 
 
Test Statistic: 0.0476 
Level 0.05 Critical Value: 0.117 
Do Not Reject Null Hypothesis 
 
> 
> 
> 
> cleanEx(); ..nameEx <- "watson.two"
> 
> ### * watson.two
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: watson.two
> ### Title: Watson's Two-Sample Test of Homogeneity
> ### Aliases: watson.two
> ### Keywords: htest
> 
> ### ** Examples
> 
> # Perform a two-sample test of homogeneity on two
> # simulated data sets.
> data1 <- rvm(20, 0, 3)
> data2 <- rvm(20, pi, 2)
> watson.two(data1, data2, alpha=0.05, plot=TRUE)

       Watson's Two-Sample Test of Homogeneity 
 
Test Statistic: 0.8149 
Level 0.05 Critical Value: 0.187 
Reject Null Hypothesis 
 
> watson.two(data1, data2)

       Watson's Two-Sample Test of Homogeneity 
 
Test Statistic: 0.8149 
P-value < 0.001 
 
> 
> 
> 
> cleanEx(); ..nameEx <- "wrpcauchy.ml"
> 
> ### * wrpcauchy.ml
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: wrpcauchy.ml
> ### Title: Wrapped Cauchy Maximum Likelihood Estimates
> ### Aliases: wrpcauchy.ml
> ### Keywords: htest
> 
> ### ** Examples
> 
> # Generate data from a wrapped Cauchy distribution.
> data <- rwrpcauchy(50, 0, 0.75)
> # Compute the sample mean direction and resultant length.
> mu0 <- circ.mean(data)
> rho0 <- est.rho(data)
> # Estimate the MLE's of the Cauchy distribution parameters.
> wrpcauchy.ml(data, mu0, rho0)
        mu       rho
1 6.192822 0.7232508
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  2.05 0.01 2.09 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')