plot( qt(ppoints(x), 9), sort(x) )
Before I consider the "best-fitting probability distribution", I want to know something about the nature of the application and what the numbers claim to represent: If they are discrete counts, I will not even consider a normal distribution except as an approximation. If they are money or physical measurements like power or grams in applications where they should never be negative, then I may want to take logarithms first before I do anything else. If lifetime data, I will consider lognormal and Weibull, and prepare a cumulative hazard plot before doing much else. If a normal probability plot shows skewness, I will look for another distribution or a transformation that makes sense with the application. If it shows discontinuities, I will consider mixtures. By the time you start considering mixtures, the number of alternative distributional models becomes infinite.
hope this helps. spencer graves
Paul Meagher wrote:
My apologies for the last email that only contained the message and not my reply. Here is what I meant to send.
----- Original Message ----- From: "Richard A. O'Keefe" <[EMAIL PROTECTED]>
To: <[EMAIL PROTECTED]>
Sent: Thursday, September 04, 2003 2:56 AM
Subject: Re: [R] Overlaying graphs
I do not know how to overlay the curve graphic on top of hist graphic.
Do you know about the "add=TRUE" option for plot()?
I learned about it from one of the list members and it worked ok for me. This is the recipe I finally came up with:
fat <- read.table("fat.dat", header=TRUE) mu <- mean(fat$height) sdev <- sd(fat$height) par (fin=c(4,4)) hist(fat$height, br=20, freq=FALSE, col="lightblue", border="black", xlab="Male Height in Inches", main = paste("Histogram of" , "Male Height")) curve(dnorm(x, mu, sdev), add=TRUE, from=64, to=78, col="red", lwd=5)
I am hoping to show visually that the normal curve overlays the obtained probability distribution when plotted on the same graph. Unfortunately, I an not sure how to overlay them. Can anyone point me in the right
direction
or show me the code.
This is a bad way to do it anyway. What you want is a qqnorm plot. See ?qqnorm.
Yes qqnorm looks like a better tool for this particular job. It does not appear to be very general in the sense that you could visually inspect whether poissson distributed data conforms to a theoretical poisson distribution.
I guess this leads to two more questions:
1. Is the Anderson-Darling goodness-of-fit test the recommended analytic test for determining whether a normal distribution conforms to a theoretical normal distribution.
2. Does R have a suite of "best-fit" tools for finding the best fitting-probability distribution for any observed probability distribution?
Regards, Paul Meagher
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