DISTPLOTS {nsRFA} | R Documentation |
Sample values are plotted against their empirical distribution in graphs where points belonging to a particular distribution should lie on a straight line.
plotpos (x, ...) unifplot (x, line=TRUE, ...) normplot (x, line=TRUE, ...) lognormplot (x, line=TRUE, ...) gumbelplot (x, line=TRUE, ...) pointspos (x, ...) unifpoints (x, ...) normpoints (x, ...) gumbelpoints (x, ...) regionalplotpos (x, cod, ...) regionalnormplot (x, cod, ...) regionallognormplot (x, cod, ...) regionalgumbelplot (x, cod, ...)
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 , ... |
Representation of the values of x
vs their empirical probability function F in a cartesian, uniform, normal, lognormal or Gumbel plot.
plotpos
and unifplot
are analogous except for the axis notation, unifplot
has the same notation as normplot
, lognormplot
, ...
F is defined with the plotting position F=(n-0.5)/n.
The straigth line (if line
=TRUE) indicate the uniform, normal, lognormal or Gumbel distribution with parameters estimated from x
.
The regional plots draw samples of a region on the same plot.
pointspos
, normpoints
, ... are the analogous of points
, they can be used to add points or lines to plotpos
, normplot
, ...
normpoints
can be used either in normplot
or lognormplot
.
Alberto Viglione, e-mail: alviglio@tiscali.it.
These functons are analogous to qqnorm
; for the distributions, see Normal
, Lognormal
, LOGNORM
, GUMBEL
.
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")