leo_wa <kwngai6022 <at> hotmail.com> writes:
>
>
> i want to plot the histogram and the curve in the same graph.if i have a set
> of data ,i plot the histogram and also want to see what distribution it
> was.So i want to plot the curve to know what distribution it like.
You will find below an example using only basic plotting functions.
The created function (called histplot) plots a histogram of the data,
along with a density kernel estimate of the distribution and
(if asked by option ncurve=T) the normal distribution
(with mean and sd computed from the original data).
Hope this will help
Matthieu
#generating random values to be plotted
dat <- rnorm(100)
#plotting function
histplot <- function(dat, breaks="Sturges", ncurve=TRUE, ...)
{
#compute the histogram and density of "dat"
hdat <- hist(dat, breaks=breaks, plot=F)
ddat <- density(dat)
#compute the xlim and ylim of the plot
# i.e. the min and max of the different superimposed
#plots (hist, density and normal curves)
xlim <- range(ddat$x)
if(ncurve)
{
#max of the normal curve
maxnorm <- pnorm(mean(dat), mean=mean(dat), sd=sd(dat))
ylim <- c(0, max(hdat$density,ddat$y,maxnorm))
}
else
{
ylim <- c(0, max(hdat$density,ddat$y))
}
#plotting
plot(hdat,
freq=F,
xlim=xlim, ylim=ylim, ...)
lines(ddat)
if (ncurve) curve(dnorm(x, mean=mean(dat), sd=(sd(dat))),
lty=3, add=TRUE)
}
#usage
histplot(dat)
histplot(dat, ncurve=F)
histplot(dat, col="blue") #arguments are passed to the hist plotting function
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