Starting with data from a microarray experiment and I would like to analyse the
influence of two factors (age, treatment) on gene expression.
Looking through the r-help archives and the web I tried the following:
I put my data in a dataframe similar to this one:
> example.df <- as.data.frame(matrix(data=runif(32,100,1000), nrow=4, ncol=4))
> example.df <-
> cbind(c("young","young","young","young","old","old","old","old"),c("drug",
> "control", "drug","control","drug","control","drug","control"), example.df)
> colnames(example.df) <- c(c("age","treatment"),paste("gene",1:4,sep=""))
> rownames(example.df) <- paste("sample", 1:8, sep="")
> example.df
age treatment gene1 gene2 gene3 gene4
sample1 young drug 392 878 908 740
sample2 young control 167 263 711 392
sample3 young drug 155 252 242 547
sample4 young control 333 348 295 300
sample5 old drug 392 878 908 740
sample6 old control 167 263 711 392
sample7 old drug 155 252 242 547
sample8 old control 333 348 295 300
Now I would like to define a function that will crop the p-values from an Anova
(so that I can use the function with a 'for loop' later on to go through all
the genes):
> p.fun <- function(arg) {
two_way_anova <- aov(arg ~ age * treatment, data = example.df)
two_way_sum <- summary(two_way_anova)
p_values <- two_way_sum[[1]]$"Pr(>F)"[1:3]
return(p_values)
}
Unfortunately my setup seems to be flawed as I'm not capable to call my
function:
> p.fun(gene1)
Error in eval(expr, envir, enclos) : object 'gene1' not found
> p.fun("gene1")
Error in model.frame.default(formula = arg ~ age * treatment, data =
example.df, :
variable lengths differ (found for 'age')
Any help on this would be truly appreciated.
Many thanks,
Mic Falb
University of Heidelberg
______________________________________________
R-help@r-project.org 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.
