On Aug 3, 2010, at 1:47 PM, imrib wrote:
>
> Hi all
> My data table (g) contains a continues data column (plant.height) and other
> columns (columns 8 to 57),
>
> each with number of levels of different factors. ANOVA test was done and
> the p-values were extracted
>
> as follos:
>
> a <- function(x) anova(lm(plant.height ~ x))$"Pr(>F)"[1]
>
> r<- apply(g[,8:57],2,a)
This looks like an invitation to disaster. apply() will coerce g[,8:57] to a
matrix, losing all factor definitions. As it happens, lm() will survive this,
because
> lm(0:1~c("a","b"))
Call:
lm(formula = 0:1 ~ c("a", "b"))
Coefficients:
(Intercept) c("a", "b")b
0 1
Warning message:
In model.matrix.default(mt, mf, contrasts) :
variable 'c("a", "b")' converted to a factor
But kruskal.test dies on the similar construction, essentially because
> is.finite("a")
[1] FALSE
................
Try it with lapply(g[,8:57], kw) instead.
-pd
>
> If I try to do a Kruskal-Wallis test :
>
> kw <- function(x) kruskal.test(plant.height ~ x)$"p.value"
>
> r.kw <- apply(g[,8:57],2,kw)
>
> I get the following error message:
>
> Error in kruskal.test.default(c(0.16, 0, 0.007, 0.078, 0, 0.08, 0.19, :
>
> all group levels must be finite
>
> Why do I get this error ? (the values in c() are the plant.height values)
>
> Thanks
>
>
> --
> View this message in context:
> http://r.789695.n4.nabble.com/Kruskal-Walllis-test-tp2311712p2311712.html
> Sent from the R help mailing list archive at Nabble.com.
>
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--
Peter Dalgaard
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd....@cbs.dk Priv: pda...@gmail.com
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