R help - Feb 2008 - How to calculate normality of the residuals from a test in R?

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You can use shapiro.test

shapiro.test(mod1$res)

Also you can plot the value:

 plot(mod1, 2)
text(par("usr")[1]+1, par("usr")[4]-.5,
labels=paste("p-value",
round(shapiro.test(mod1$res)$p.value, 4), sep="="))


On 07/02/2008, Falco tinnunculus <kestrel78 at gmail.com>
wrote:
> Hi,
>
> How do I calculate normality distribution of the residuals from a test in
R?
>
> I have tried plot(mod1), and I get a nice plot, but no p-value... is there
> some other ways to calculate this?
>
> Regards Kes,
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
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>

-- 
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On Thu, 2008-02-07 at 11:56 +0100, Falco tinnunculus
wrote:
> Hi,
> 
> How do I calculate normality distribution of the residuals from a test in
R?
> 
> I have tried plot(mod1), and I get a nice plot, but no p-value... is there
> some other ways to calculate this?
> 
> Regards Kes,
You could use a QQ-plot to judge normality of residuals.

x <- runif(100)
y <- 3 + (0.8 * x) + rnorm(100, mean = 0, sd = 0.5)
mod <- lm(y ~ x)

mod.resi <- resid(mod)
qqnorm(mod.resi)
qqline(mod.resi)

Systematic deviations from the line, indicate departures from normality
(in this case), such as heavy tails, skewness. If the residuals were
normally distributed they should be scattered around the line.

G

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