>
> On 23/09/2009, at 11:26 PM, Christian Schulz wrote:
>
> > Hi,
> >
> >
> > nvars <- 902
> > data <- as.data.frame(matrix(runif(100*nvars),ncol=nvars))
> > colnames(data)[901] <- c('phenotype')
> > colnames(data)[902] <- c('outcome')
>
>
>
> Just ***WHAT*** do you think the ``c( )''
On 23/09/2009, at 11:26 PM, Christian Schulz wrote:
Hi,
nvars <- 902
data <- as.data.frame(matrix(runif(100*nvars),ncol=nvars))
colnames(data)[901] <- c('phenotype')
colnames(data)[902] <- c('outcome')
Just ***WHAT*** do you think the ``c( )'' is doing for you in
the construction
Hi,
nvars <- 902
data <- as.data.frame(matrix(runif(100*nvars),ncol=nvars))
colnames(data)[901] <- c('phenotype')
colnames(data)[902] <- c('outcome')
### catch all aic values ###
res <- matrix(nrow=900,ncol=2)
for (i in 1:(length(data)-2)) {
res[i,1] <- names(data)[i]
res[i,2] <- glm(outco
Dear R users,
Could you help my with the following problem?
I want to repeat a glm analysis with 2 independent variables for all 900
variables (snps) in my data set. So, I want to check whether snp1 has a
different effect on my outcome variable in patients and
controls(phenotype). And repeat that
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