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("circular-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('circular')
Loading required package: boot
This is version 0.3-2 of circular package
Please report any bugs or comments to <Claudio Agostinelli> claudio@unive.it 
The package redefine how function 'var' works 
In particular, (try 'methods(var)') 
 notice that 'var.default' is an alias for the original 'var' function 
 and that a method for data.frame is available.

Attaching package: 'circular'


	The following object(s) are masked from package:stats :

	 var 

> 
> 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 <- rvonmises(n=50, mu=pi, kappa=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 <- "aov.circular"
> 
> ### * aov.circular
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: aov.circular
> ### Title: Analysis of Variance for circular data
> ### Aliases: aov.circular print.aov.circular
> ### Keywords: models
> 
> ### ** Examples
> 
> 
> x <- c(rvonmises(50, 0, 1), rvonmises(100, pi/3, 10))
> group <- c(rep(0, 50), rep(1, 100))
> 
> aov.circular(x, group)

Call:
aov.circular(x = x, group = group)


 Circular Analysis of Variance: High Concentration F-Test 
 
         df     SS     MS     F         p
Between   1  6.263 6.2632 36.38 1.242e-08
Within  148 29.347 0.1983    NA        NA
Total   149 35.610 0.2390    NA        NA

 
> aov.circular(x, group, method="LRT")

Call:
aov.circular(x = x, group = group, method = "LRT")


 Circular Analysis of Variance: Likelihood Ratio Test 
 
 df:      1 
 ChiSq:   30.9 
 p.value: 2.716e-08 
 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "axis.circular"
> 
> ### * axis.circular
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: axis.circular
> ### Title: Add Axis to a Circular Plot
> ### Aliases: axis.circular
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data.vm <- rvonmises(n=100, mu=0, kappa=3) 
> plot(data.vm, axes=FALSE, ticks=FALSE)
> axis.circular(at=seq(0, 11/6*pi, pi/6), labels=c("0",
+ expression(frac(pi,6)), expression(paste(frac(1,3), pi)),
+ expression(frac(pi,2)), expression(paste(frac(2,3), pi)),
+ expression(paste(frac(5,6), pi)), expression(pi),
+ expression(paste(frac(7,6), pi)), expression(paste(frac(4,3), pi)),
+ expression(paste(frac(3,2), pi)), expression(paste(frac(5,3), pi)),
+ expression(paste(frac(11,6), pi))))
> 
> 
> 
> cleanEx(); ..nameEx <- "circular"
> 
> ### * circular
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Circular
> ### Title: Create Objects of class circular for Circular data.
> ### Aliases: circular as.circular is.circular print.circular
> ### Keywords: misc
> 
> ### ** Examples
> 
> x <- circular(c(pi, pi/3, pi/4))
> print(x)
Circular Data: 
Type = angles 
Units = radians 
Template = none 
Modulo = asis 
Zero = 0 
Rotation = counter 
[1] 3.1415927 1.0471976 0.7853982
> is.circular(x)
[1] TRUE
> 
> 
> 
> cleanEx(); ..nameEx <- "cor.circular"
> 
> ### * cor.circular
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cor.circular
> ### Title: Correlation Coefficient for Angular Variables
> ### Aliases: cor.circular
> ### Keywords: univar
> 
> ### ** Examples
> 
> # Generate two circular data sets, and compute their correlation.
> x <- rvonmises(n=50, mu=0, kappa=3)
> y <- x + rvonmises(n=50, mu=pi, kappa=10)
> cor.circular(x, y, test=TRUE)
$cor
[1] 0.9197672

$statistic
[1] 4.764692

$p.value
[1] 1.891424e-06

> 
> 
> 
> cleanEx(); ..nameEx <- "density.circular"
> 
> ### * density.circular
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: density.circular
> ### Title: Kernel Density Estimation for Circular Data
> ### Aliases: density.circular print.density.circular
> ### Keywords: distribution smooth
> 
> ### ** Examples
> 
> x <- rvonmises(n=100, mu=pi, kappa=2)
> res25 <- density(x, bw=25)
> plot(res25, points.plot=TRUE, xlim=c(-1.5,1))
Warning: parameter "points.plot" could not be set in high-level plot() function
Warning: parameter "points.plot" could not be set in high-level plot() function
Warning: parameter "points.plot" could not be set in high-level plot() function
Warning: parameter "points.plot" could not be set in high-level plot() function
Warning: parameter "points.plot" could not be set in high-level plot() function
Warning: parameter "points.plot" could not be set in high-level plot() function
> res50 <- density(x, bw=25, adjust=2)
> lines(res50, col=2)
> 
> 
> 
> cleanEx(); ..nameEx <- "equal.kappa.test"
> 
> ### * equal.kappa.test
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: equal.kappa.test
> ### Title: Equal Kappa Test
> ### Aliases: equal.kappa.test print.equal.kappa.test
> ### Keywords: htest
> 
> ### ** Examples
> 
> 
> x <- c(rvonmises(50, 0, 1), rvonmises(100, pi/3, 10))
> group <- c(rep(0, 50), rep(1, 100)) 
> 
> equal.kappa.test(x, group)

Call:
equal.kappa.test(x = x, group = group)


 Test for Homogeneity of Concentration Parameters 
 
 df:      1 
 ChiSq:   123.1 
 p.value: 0 
 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "lines.density.circular"
> 
> ### * lines.density.circular
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lines.density.circular
> ### Title: Add a Plot for Kernel Density Estimation for Circular Data
> ### Aliases: lines.density.circular
> ### Keywords: dplot
> 
> ### ** Examples
> 
> x <- rvonmises(n=100, mu=pi, kappa=2)
> res25 <- density(x, bw=25)
> plot(res25, points.plot=TRUE, xlim=c(-1.5,1))
Warning: parameter "points.plot" could not be set in high-level plot() function
Warning: parameter "points.plot" could not be set in high-level plot() function
Warning: parameter "points.plot" could not be set in high-level plot() function
Warning: parameter "points.plot" could not be set in high-level plot() function
Warning: parameter "points.plot" could not be set in high-level plot() function
Warning: parameter "points.plot" could not be set in high-level plot() function
> res50 <- density(x, bw=25, adjust=2)
> lines(res50, col=2)
> 
> 
> 
> cleanEx(); ..nameEx <- "lm.circular"
> 
> ### * lm.circular
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lm.circular
> ### Title: Circular-Circular and Circular-Linear Regression
> ### Aliases: lm.circular lm.circular.cc lm.circular.cl print.lm.circular.cl
> ### Keywords: models regression
> 
> ### ** Examples
> 
> # Generate a data set of dependent circular variables.
> x <- circular(runif(50, 0, 2*pi))
> y <- atan(0.15*cos(x) + 0.25*sin(x), 0.35*sin(x)) + rvonmises(n=50, mu=0,
+ kappa=5)
> 
> # Fit a circular-circular regression model.
> circ.lm <- lm.circular(y, x, order=1)
> # Obtain a crude plot a data and fitted regression line.
> plot.default(x, y)
> circ.lm$fitted[circ.lm$fitted>pi] <- circ.lm$fitted[circ.lm$fitted>pi] - 2*pi 
> 
> points.default(x[order(x)], circ.lm$fitted[order(x)], type='l')
> 
> 
> 
> cleanEx(); ..nameEx <- "lsfit.circle"
> 
> ### * lsfit.circle
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lsfit.circle
> ### Title: Fit a 2D circle to an (x,y) dataset
> ### Aliases: lsfit.circle print.lsfit.circle
> ### Keywords: models
> 
> ### ** Examples
> 
> 
> data(coope)
> res <- lsfit.circle(x=x.coope, y=y.coope)
> res

Call:
lsfit.circle(x = x.coope, y = y.coope)

          r       a       b     
 Coeff:   4.1059  3.0432  0.7457

 Summary in Rectangular Coordinates 
       x                y         
 Min.   :-1.100   Min.   :-3.000  
 1st Qu.: 0.600   1st Qu.:-1.450  
 Median : 3.850   Median : 1.300  
 Mean   : 3.388   Mean   : 1.087  
 3rd Qu.: 5.800   3rd Qu.: 4.000  
 Max.   : 7.500   Max.   : 4.700  

 Summary in Polar Coordinates of Recentered Observations 
  angles           radius     
 n   :8.0000   Min.   :3.831  
 Mean:0.7818   1st Qu.:3.979  
 Rho :0.1182   Median :4.074  
               Mean   :4.106  
               3rd Qu.:4.198  
               Max.   :4.491  
> 
> plot(res)
> 
> par(mfcol=c(1,2))
> plot(res$angles)
> hist(res$radius)
> 
> windrose(x=res$angles, y=res$radius)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "mean.circular"
> 
> ### * mean.circular
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mean.circular
> ### Title: Mean Direction
> ### Aliases: mean.circular
> ### Keywords: univar
> 
> ### ** Examples
> 
> # Compute the mean direction of a random sample of observations.
> data <- circular(runif(50, 0, pi))
> mean(data)
Circular Data: 
Type = angles 
Units = radians 
Template = none 
Modulo = asis 
Zero = 0 
Rotation = counter 
[1] 1.703981
> 
> 
> 
> cleanEx(); ..nameEx <- "mle.vonmises"
> 
> ### * mle.vonmises
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mle.vonmises
> ### Title: von Mises Maximum Likelihood Estimates
> ### Aliases: mle.vonmises print.mle.vonmises
> ### Keywords: htest
> 
> ### ** Examples
> 
> x <- rvonmises(n=50, mu=0, kappa=5)
> mle.vonmises(x) # estimation of mu and kappa

Call:
mle.vonmises(x = x)

mu: -0.06985  ( 0.06237 )

kappa: 5.668  ( 1.067 )

> mle.vonmises(x, mu=0) # estimation of kappa only

Call:
mle.vonmises(x = x, mu = 0)

mu: 0  ( 0 )

kappa: 5.544  ( 1.041 )

mu is known
> 
> 
> 
> cleanEx(); ..nameEx <- "mle.vonmises.bootstrap.ci"
> 
> ### * mle.vonmises.bootstrap.ci
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mle.vonmises.bootstrap.ci
> ### Title: Bootstrap Confidence Intervals
> ### Aliases: mle.vonmises.bootstrap.ci print.mle.vonmises.bootstrap.ci
> ### Keywords: htest
> 
> ### ** Examples
> 
> x <- rvonmises(n=25, mu=0, kappa=3)
> x.bs <- mle.vonmises.bootstrap.ci(x, alpha=.10)
> par(mfcol=c(1,2))
> rose.diag(x.bs$mu, bins=30, main=expression(mu))
> hist(x.bs$kappa, main=expression(kappa))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "mle.wrappedcauchy"
> 
> ### * mle.wrappedcauchy
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mle.wrappedcauchy
> ### Title: Wrapped Cauchy Maximum Likelihood Estimates
> ### Aliases: mle.wrappedcauchy print.mle.wrappedcauchy
> ### Keywords: htest
> 
> ### ** Examples
> 
> x <- rwrappednormal(n=50, mu=0, rho=0.5)
> mle.wrappednormal(x) # estimation of mu and rho

Call:
mle.wrappednormal(x = x)

mu: 0.1277 

rho: 0.6226 

sd: 0.9735 

> mle.wrappednormal(x, mu=0) # estimation of rho only

Call:
mle.wrappednormal(x = x, mu = 0)

mu: 0 

rho: 0.6193 

sd: 0.9789 

mu is known
> 
> 
> 
> cleanEx(); ..nameEx <- "mle.wrappednormal"
> 
> ### * mle.wrappednormal
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mle.wrappednormal
> ### Title: Wrapped Normal Maximum Likelihood Estimates
> ### Aliases: mle.wrappednormal print.mle.wrappednormal
> ### Keywords: htest
> 
> ### ** Examples
> 
> x <- rwrappednormal(n=50, mu=0, rho=0.5)
> mle.wrappednormal(x) # estimation of mu and rho (and sd)

Call:
mle.wrappednormal(x = x)

mu: 0.1277 

rho: 0.6226 

sd: 0.9735 

> mle.wrappednormal(x, mu=0) # estimation of rho (and sd) only

Call:
mle.wrappednormal(x = x, mu = 0)

mu: 0 

rho: 0.6193 

sd: 0.9789 

mu is known
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.circular"
> 
> ### * plot.circular
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.circular
> ### Title: Circular Data Plot
> ### Aliases: plot.circular
> ### Keywords: hplot
> 
> ### ** Examples
> 
> # Generate 100 observations from a von Mises distribution.
> # with mean direction 0 and concentration 3.
> data.vm <- rvonmises(n=100, mu=0, kappa=3) 
> # Plot data set. All points do not fit on plot.
> plot(data.vm, stack=TRUE, bins=150) 
> # Shrink the plot so that all points fit.
> plot(data.vm, stack=TRUE, bins=150, shrink=1.5) 
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.density.circular"
> 
> ### * plot.density.circular
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.density.circular
> ### Title: Plot Method for Kernel Density Estimation for Circular Data
> ### Aliases: plot.density.circular
> ### Keywords: dplot
> 
> ### ** Examples
> 
> x <- rvonmises(n=100, mu=pi, kappa=2)
> res25 <- density(x, bw=25)
> plot(res25, points.plot=TRUE, xlim=c(-1.5,1))
Warning: parameter "points.plot" could not be set in high-level plot() function
Warning: parameter "points.plot" could not be set in high-level plot() function
Warning: parameter "points.plot" could not be set in high-level plot() function
Warning: parameter "points.plot" could not be set in high-level plot() function
Warning: parameter "points.plot" could not be set in high-level plot() function
Warning: parameter "points.plot" could not be set in high-level plot() function
> res50 <- density(x, bw=25, adjust=2)
> lines(res50, col=2)
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.edf"
> 
> ### * plot.edf
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.edf
> ### Title: Plot Circular Empirical Distribution Function
> ### Aliases: plot.edf lines.edf
> ### Keywords: hplot
> 
> ### ** Examples
> 
> # Compare the edf's of two simulated sets of data.
> data1 <- rvonmises(n=10, mu=0, kappa=3)
> data2 <- rvonmises(n=10, mu=0, kappa=1)
> plot.edf(data1, xlab="Data", ylab="EDF", main="Plots of Two EDF's")
> lines.edf(data2, lty=2, col=2)
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.lsfit.circle"
> 
> ### * plot.lsfit.circle
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.lsfit.circle
> ### Title: Plot method for lsfit.circle function
> ### Aliases: plot.lsfit.circle
> ### Keywords: hplot
> 
> ### ** Examples
> 
> 
> data(coope)
> res <- lsfit.circle(x=x.coope, y=y.coope)
> plot(res)
> 
> 
> 
> cleanEx(); ..nameEx <- "points.circular"
> 
> ### * points.circular
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: points.circular
> ### Title: Add Points to a Circular Plot
> ### Aliases: points.circular
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data.1 <- rvonmises(n=100, mu=0, kappa=3)
> data.2 <- rvonmises(n=100, mu=pi/3, kappa=3) 
> res <- plot(data.1, stack=FALSE, bins=150, shrink=1.5, col=1) 
> points(data.2, plot.info=res, col=2)
> 
> 
> 
> 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
> 
> x <- rvonmises(n=25, mu=0, kappa=3)
> pp.plot(x)
> x <- c(rvonmises(n=20, mu=0, kappa=7), rvonmises(n=20, mu=pi, kappa=7))
> pp.plot(x)
> 
> 
> 
> cleanEx(); ..nameEx <- "range.circular"
> 
> ### * range.circular
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: range.circular
> ### Title: Circular Range
> ### Aliases: range.circular
> ### Keywords: univar htest
> 
> ### ** Examples
> 
> data <- rvonmises(n=50, mu=0, kappa=2)
> range(data, test=TRUE)
$range
[1] 4.231505

$p.value
[1] 1.933770e-07

> data <- circular(runif(50, 0, 2*pi))
> range(data, test=TRUE)
$range
[1] 5.411513

$p.value
[1] 0.03302356

> 
> 
> 
> cleanEx(); ..nameEx <- "rayleigh.test"
> 
> ### * rayleigh.test
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rayleigh.test
> ### Title: Rayleigh Test of Uniformity
> ### Aliases: rayleigh.test print.rayleigh.test
> ### Keywords: htest
> 
> ### ** Examples
> 
> x <- rvonmises(n=25, mu=pi, kappa=2)
> # General alternative
> rayleigh.test(x)

       Rayleigh Test of Uniformity 
       General Unimodal Alternative 

Test Statistic:  0.6967 
P-value:  0 

> # Specified alternative
> rayleigh.test(x, mu=0)

       Rayleigh Test of Uniformity 
       Alternative with Specified Mean Direction:  0 

Test Statistic:  -0.6966 
P-value:  1 

> 
> 
> 
> cleanEx(); ..nameEx <- "rho.circular"
> 
> ### * rho.circular
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rho.circular
> ### Title: Mean Resultant Length
> ### Aliases: rho.circular
> ### Keywords: univar
> 
> ### ** Examples
> 
> # Compute the mean resultant length of a random sample of observations.
> data <- circular(runif(100, 0, 2*pi))
> rho.circular(data)
[1] 0.09539008
> 
> 
> 
> 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.
> x <- circular(runif(50, 0, 2*pi))
> rose.diag(x, bins = 18, main = 'Uniform Data')
> points(x)
> # Generate von Mises data and create several rose diagrams.
> x <- rvonmises(n=25, mu=0, kappa=5, zero=pi/4)
> y <- rose.diag(x, bins=18) # Points fall out of bounds.
> points(x, plot.info=y, stack=TRUE)
> y <- rose.diag(x, bins=36, prop=1.5, shrink=1.5) # Adjust optional parameters to fit all points on plot.
> points(x, plot.info=y, stack=TRUE)
> 
> 
> 
> cleanEx(); ..nameEx <- "rstable"
> 
> ### * rstable
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rstable
> ### Title: Random Generation from the Stable Family of Distributions
> ### Aliases: rstable
> ### Keywords: distribution
> 
> ### ** Examples
> 
> hist(rstable(200, 1.5, .5)) #fairly long tails, skewed right
> 
> 
> 
> cleanEx(); ..nameEx <- "summary.circular"
> 
> ### * summary.circular
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: summary.circular
> ### Title: Circular Summary Statistics
> ### Aliases: summary.circular
> ### Keywords: univar
> 
> ### ** Examples
> 
> # Compute summary statistics of a random sample of observations. 
> data <- circular(runif(50, 0, pi))
> summary(data)
         n       Mean        Rho 
50.0000000  1.7039815  0.6815599 
> summary(data.frame(data, runif(50, 0, pi)))
   data         runif.50..0..pi.
 n   :50.0000   Min.   :0.1851  
 Mean: 1.7040   1st Qu.:1.0007  
 Rho : 0.6816   Median :1.4362  
                Mean   :1.5805  
                3rd Qu.:2.4002  
                Max.   :3.0179  
> 
> 
> 
> cleanEx(); ..nameEx <- "var.circular"
> 
> ### * var.circular
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: var.circular
> ### Title: A measure of variance for Circular Data
> ### Aliases: var.circular
> ### Keywords: univar
> 
> ### ** Examples
> 
> # Compute the mean resultant length of a random sample of observations.
> x <- rvonmises(n=100, mu=0, kappa=1)
> var(x)
[1] 0.4771242
> 
> 
> 
> cleanEx(); ..nameEx <- "watson.test"
> 
> ### * watson.test
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: watson.test
> ### Title: Watson's Test
> ### Aliases: watson.test print.watson.test
> ### Keywords: htest
> 
> ### ** Examples
> 
> # Generate data from the uniform distribution on the circle.
> x <- circular(runif(100, 0, 2*pi))  
> watson.test(x)

       Watson's Test for Circular Uniformity 
 
Test Statistic: 0.0903 
P-value > 0.10 
 
> # Generate data from a von Mises distribution.
> x <- rvonmises(n=50, mu=0, kappa=4) 
> watson.test(x, alpha=0.05, dist="vonmises")

       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.test"
> 
> ### * watson.two.test
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: watson.two.test
> ### Title: Watson's Two-Sample Test of Homogeneity
> ### Aliases: watson.two.test print.watson.two.test
> ### Keywords: htest
> 
> ### ** Examples
> 
> # Perform a two-sample test of homogeneity on two
> # simulated data sets.
> data1 <- rvonmises(n=20, mu=0, kappa=3)
> data2 <- rvonmises(n=20, mu=pi, kappa=2)
> watson.two.test(data1, data2, alpha=0.05)

      Watson's Two-Sample Test of Homogeneity 

Test Statistic: 0.8149 
Level 0.05 Critical Value: 0.187 
Reject Null Hypothesis 

> watson.two.test(data1, data2)

      Watson's Two-Sample Test of Homogeneity 

Test Statistic: 0.8149 
P-value < 0.001 
 
> 
> 
> 
> cleanEx(); ..nameEx <- "windrose"
> 
> ### * windrose
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: windrose
> ### Title: Windrose Generator
> ### Aliases: windrose
> ### Keywords: hplot
> 
> ### ** Examples
> 
> # Random distribution of direction and magnitude in degrees
> 
> dir <- circular(runif(100, 0, 360), units="degrees")
> mag <-  rgamma(100, 15)
> sample <- data.frame(dir=dir, mag=mag)
> 
> par(mfrow=c(2,2))
> res <- windrose(sample)
> ## we join two pedals and keep the same shrink (scale of the plot)
> breaks <- seq(0, 2 * pi, by = pi/6)
> breaks <- breaks[-2]
> windrose(sample, breaks=breaks, main="The same but with two pedals joined", shrink=res$shrink)
> ## change the rotation
> sample <- data.frame(dir=circular(dir, units="degrees", rotation="clock"), mag=mag)
> windrose(sample, breaks=breaks, main="Change the rotation", shrink=res$shrink)
> ## use geographics template
> sample <- data.frame(dir=circular(dir, units="degrees", template="geographics"), mag=mag)
> windrose(sample, breaks=breaks, main="Use the template 'geographics'", shrink=res$shrink)
> 
> ## do the same plot but in radians
> dir <- conversion.circular(dir)
> windrose(x=dir, y=mag, xlim=c(-1.3, 1.3))
> 
> ## magnify some part of the plot
> windrose(x=dir, y=mag, xlim=c(0, 1.3))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  4.26 0.09 4.36 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')