Easiest thing to do: use the optional ylim argument to plot (taking values like
c(0, maxhx) or possibly a little wider) which will let you set the bounds
directly.
Michael
PS It's possible to get the following to be much more easily extensible using
loops and what not, but as its very late in my time zone, I'll wait til the
morning to work it out (for fear of making yet another public mistake :-P) For
now, note that it's perfectly ok to use
maxhx <- max(hx1, hx2, hx3)
which will save you a few lines.
On Feb 24, 2012, at 5:58 PM, FJ M <chicagobrownb...@hotmail.com> wrote:
>
> I am plotting three Pearson Type IV distributions. It looks like I have to
> plot the distribution with the highest value of y and then use lines() to add
> the two distributions that are shorter / have lower max values of y. The
> following code figures out which distribution has the max y value, plots it
> first and then uses lines for the other two distributions with a series of
> three if statements. This works. I run R from a batch file that reads the
> following in a text file.
>
> I want to graph dozens of distributions and I am looking for a more elegant
> way to do this.
>
> New to R, experienced C programmer, thanks in advance. Frank
>
>
>
> colors <- c("red", "blue", "darkgreen", "gold", "black")
> labels <- c("1", "2","3")
> pdf("C:\\Users\\Frank\\Documents\\R\\Scripts\\pt4_Graph.pdf")
> ## load Pearson package
> library(PearsonDS)
> ##range for x axis
> no_of_increments<- 100
> x <- seq(-0.06, +0.06, length=no_of_increments)
> ##parameters for the plots of three distributions
> mx<- c(1.95, 18.35,1.93)
> nux<- c(0.08,-1.02,0.25)
> locationx<- c(0.0048,-0.00254,0.00189)
> scalex<- c(0.0115,0.082187,0.026675)
> ## calculate probability density function
> hx1<- dpearsonIV(x,m=mx[1],nu=nux[1],location=locationx[1],scale=scalex[1])
> hx2<- dpearsonIV(x,m=mx[2],nu=nux[2],location=locationx[2],scale=scalex[2])
> hx3<- dpearsonIV(x,m=mx[3],nu=nux[3],location=locationx[3],scale=scalex[3])
> ##calculate max of each distribtion
> maxhx1<- max(hx1)
> maxhx2<- max(hx2)
> maxhx3<- max(hx3)
> maxhx<- max(hx1,hx2,hx3)
> maxhx1
> maxhx2
> maxhx3
> maxhx
>
> if (maxhx1==maxhx)
> {plot(x, hx1 , type="l", lwd=2, col=colors[1], xlab="x value",
> ylab="Density", main="pt4")
> for (i in 2:3){
> lines(x,
> dpearsonIV(x,m=mx[i],nu=nux[i],location=locationx[i],scale=scalex[i]), lwd=2,
> col=colors[i])}
> legend("topright", inset=.05, title="Distributions",
> labels, lwd=2, lty=c(1, 1, 1, 1, 2), col=colors)
> grid()
> }
>
> if (maxhx2==maxhx) {plot(x, hx2 , type="l", lwd=2, xlab="x value",
> ylab="Density", main="SPX", col=colors[2])
> {
> lines(x,
> dpearsonIV(x,m=mx[1],nu=nux[1],location=locationx[1],scale=scalex[1]), lwd=2,
> col=colors[1])
> lines(x,
> dpearsonIV(x,m=mx[3],nu=nux[3],location=locationx[3],scale=scalex[3]), lwd=2,
> col=colors[3])
> legend("topright", inset=.05, title="Distributions",
> labels, lwd=2, lty=c(1, 1, 1, 1, 2), col=colors)
> grid()
> }
>
> if (maxhx3==maxhx)
> {plot(x, hx3 , type="l", lwd=2, col=colors[3], xlab="x value",
> ylab="Density", main="SPX")
> for (i in 1:2){
> lines(x,
> dpearsonIV(x,m=mx[i],nu=nux[i],location=locationx[i],scale=scalex[i]), lwd=2,
> col=colors[i])}
> legend("topright", inset=.05, title="Distributions",
> labels, lwd=2, lty=c(1, 1, 1, 1, 2), col=colors)
> grid()
> }
>
>
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and provide commented, minimal, self-contained, reproducible code.
