nsRFA (version 0.1-6)

DISTPLOTS: Distribution plots

Description

Sample values are plotted against their empirical distribution in graphs where points belonging to a particular distribution should lie on a straight line.

Usage

plotpos (x, ...)
 normplot (x, line=TRUE, ...)
 lognormplot (x, line=TRUE, ...)
 gumbelplot (x, line=TRUE, ...)
 pointspos (x, ...)
 normpoints (x, ...)
 gumbelpoints (x, ...)
 regionalplotpos (x, cod, ...)
 regionalnormplot (x, cod, ...)
 regionallognormplot (x, cod, ...)
 regionalgumbelplot (x, cod, ...)

Arguments

x

vector representing a data-sample

line

if TRUE (default) a straigth line indicating the normal, lognormal or Gumbel distribution with parameters estimated from x is plotted

cod

array that defines the data subdivision among sites

...

graphical parameters as xlab, ylab, main, ...

Value

• Representation of the values of x vs their empirical probability function $F$ in a cartesian, normal, lognormal or Gumbel plot. $F$ is defined with the plotting position $F=(n-0.5)/n$. The straigth line (if line=TRUE) indicate the normal, lognormal or Gumbel distribution with parameters estimated from x. The regional plots draw samples of a region on the same plot.

  pointspos, normpoints and gumbelpoints are the analogous of points, they can be used to add points or lines to plotpos, normplot, lognormplot and gumbelplot. normpoints can be used either in normplot or lognormplot.

See Also

These functons are analogous to qqnorm; for the distributions, see Normal, Lognormal, LOGNORM, GUMBEL.

Examples

x <- rnorm(30,10,2)
plotpos(x)
normplot(x)
normplot(x,xlab=expression(D[m]),ylab=expression(hat(F)),
         main="Normal plot",cex.main=1,font.main=1)
normplot(x,line=FALSE)

x <- rlnorm(30,log(100),log(10))
normplot(x)
lognormplot(x)

x <- rand.gumb(30,1000,100)
normplot(x)
gumbelplot(x)

x <- rnorm(30,10,2)
y <- rnorm(50,10,3)
z <- c(x,y)
codz <- c(rep(1,30),rep(2,50))
regionalplotpos(z,codz)
regionalnormplot(z,codz,xlab="z")
regionallognormplot(z,codz)
regionalgumbelplot(z,codz)

plotpos(x)
pointspos(y,pch=2,col=2)

x <- rnorm(50,10,2)
F <- seq(0.01,0.99,by=0.01)
qq <- qnorm(F,10,2)
plotpos(x)
pointspos(qq,type="l")

normplot(x,line=FALSE)
normpoints(x,type="l",lty=2,col=3)

lognormplot(x)
normpoints(x,type="l",lty=2,col=3)

gumbelplot(x)
gumbelpoints(x,type="l",lty=2,col=3)

# distributions comparison in probabilistic graphs
x <- rnorm(50,10,2)
F <- seq(0.001,0.999,by=0.001)
loglikelhood <- function(param) {-sum(dgamma(x, shape=param[1], 
                scale=param[2], log=TRUE))}
parameters <- optim(c(1,1),loglikelhood)$par
qq <- qgamma(F,shape=parameters[1],scale=parameters[2])
plotpos(x)
pointspos(qq,type="l")

normplot(x,line=FALSE)
normpoints(qq,type="l")

lognormplot(x,line=FALSE)
normpoints(qq,type="l")