On 2007-03-16, [EMAIL PROTECTED] <[EMAIL PROTECTED]> wrote:
> for example:
> I have got these data, organized in a dataframe.
>
> sample1 sample2 sample3 sample4 group
> replicate1 1.00 0.02 0.35 0.50 A
> replicate2 1.00 0.02 1.54 1.11 A
> replicate3 1.00 0.02 1.54 1.11 A
> replicate4 1.00 0.02 1.54 1.11 A
> replicate5 1.00 0.10 0.18 0.72 B
> replicate6 1000.00 0.75 0.86 7.26 B
> replicate7 1000.00 0.75 0.18 0.36 B
> replicate8 1000.00 0.75 12.09 0.74 B
> replicate9 1000.00 0.75 12.09 0.84 C
> replicate10 1000.00 0.98 0.65 0.50 C
> replicate11 2.00 6.00 6.00 2.00 C
> replicate12 6.00 6.00 2.00 6.00 C
>
>
> Using "aov()" I can run a test on each column. but I would
> like to run the ANOVAs for each colum (that in my case are hundreds)
> in an automated way.
...
> SAMPLE ANOVA
> sample1 ok
> sample2 ok
> sample3 not significant
> ....
>
sapply(sample.df[,1:4],
FUN=function(x) summary.aov(aov(x~sample.df$group))[[1]][1,"Pr(>F)"])
sapply applies a function to each 'column' of a dataframe, returning
the result as a vector.
FUN=function(x) ... is an anonymous function that inserts the column
of the dataframe into the following function for each column as sapply
loops through them.
summary.aov(...) produces a list of tables, although in this case the
list is only one table long. [[1]][1,"Pr(>F)"] extracts the p-value
from the first row of the first table.
The result for your example is:
sapply(sample.df[,1:4],
FUN=function(x)summary.aov(aov(x~sample.df$group))[[1]][1,"Pr(>F)"])
sample1 sample2 sample3 sample4
0.09961436 0.04405756 0.49289026 0.67389417
--
Regards,
Tyler Smith
______________________________________________
[email protected] mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
