Hello
I have data that are categorical both independent variable and dependent as
well having levels more than 3. How can i check the normality of my data?
I have tried the example given of Shapiro-Wilk for levels of factors
data
summary(chickwts)
## linear model and ANOVA
fm <- lm(weight ~ feed, data = chickwts)
anova(fm)
## QQ plot for residuals + Shapiro-Wilk test
shapiro.test(residuals(fm))
## separate tests for all groups of observations
## (with some formatting)
do.call("rbind", with(chickwts, tapply(weight, feed,
function(x) unlist(shapiro.test(x)[c("statistic", "p.value")]))))
But ended up with Error message that x should be numeric and more comments
see below.
Hope to get some help on this
Thanks,
Nancy
## linear model and ANOVA
> fm <- lm(retaliation ~ occupation, data = kazi)
Warning messages:
1: In model.response(mf, "numeric") :
using type = "numeric" with a factor response will be ignored
2: In Ops.factor(y, z$residuals) : ‘-’ not meaningful for factors
> anova(fm)
Error in if (ssr < 1e-10 * mss) warning("ANOVA F-tests on an essentially
perfect fit are unreliable") :
missing value where TRUE/FALSE needed
In addition: Warning message:
In Ops.factor(object$residuals, 2) : ‘^’ not meaningful for factors
> ## QQ plot for residuals + Shapiro-Wilk test
> shapiro.test(residuals(fm))
Error in class(y) <- oldClass(x) :
adding class "factor" to an invalid object
> ## separate tests for all groups of observations
> ## (with some formatting)
> do.call("rbind", with(kazi, tapply(retaliation, occupation,
+ function(x)
unlist(shapiro.test(x)[c("statistic", "p.value")]))))
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