P-P plot

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Does this give you what you want?

fit <- lm( Petal.Width ~ Petal.Length, data=iris)

tmp1 <- resid(fit)
tmp2 <- pnorm( tmp1, 0, summary(fit)$sigma )

par(mfrow=c(2,1))
qqnorm(tmp1)
qqline(tmp1)

plot( ppoints(length(tmp1)), sort(tmp2), xlab='Theoretical Percentiles',
 ylab='Sample Percentiles')
abline(0,1)

Most people these days prefer the qqplot to the pp plot, the qq-plot
gives more room to the set of points that are generally most
interesting.
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at intermountainmail.org
(801) 408-8111

> -----Original Message-----
> From: r-help-bounces at r-project.org 
> [mailto:r-help-bounces at r-project.org] On Behalf Of Maura E Monville
> Sent: Friday, September 28, 2007 1:17 PM
> To: r-help at stat.math.ethz.ch
> Subject: [R] P-P plot
> 
> Sorry for my silly questions. I'm a beginner with R and most 
> statistics concepts.
> I carried out a simple linear regression where the dependent 
> variable is explained through a combination of powers of 
> cos(independent variable).
> I can see R returned a good R^2 factor (> 0.99) but I have a 
> hard time at interpreting all the other info that R prints 
> out by using summary( regression.results), 
> residuals(regression.results), anova(regression.results),
> 
> plot(regression.results).
> I know sometimes R^2 might be misleading ..
> 
> I see that R provided a Q-Q plot by default.
> Is it possible to get a P-P plot ? I searched for that but 
> did not get anywhere ...
> 
> Thank you in advance.
> Best regards,
> 
> --
> Maura E.M
> 
> 	[[alternative HTML version deleted]]
> 
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