Thanks for providing a reproducible example.
Using the plyr package you can write your whole computation more compactly:
library(plyr)
library(caTools) #for trapz
auc <- ddply(lab.samples, .(challenge, subid),
function(df) {
df$time <- c(0, seq(60,by=10, len=nrow(df)-1))
summari
On 2012-03-19 15:00, Jorge I Velez wrote:
Hi David,
Thank you for the reproducible example!
Try
do.call(rbind, auc_stress)
cortisol amylase
2 919.05 6834.80
3 728.25 24422.05
4 2106.00 25908.35
6 636.40 12209.75
7 1925.95 4749.25
do.call(rbind, auc_control)
cortisol amyl
Hi David,
Thank you for the reproducible example!
Try
> do.call(rbind, auc_stress)
cortisol amylase
2 919.05 6834.80
3 728.25 24422.05
4 2106.00 25908.35
6 636.40 12209.75
7 1925.95 4749.25
> do.call(rbind, auc_control)
cortisol amylase
2 604.90 2458.00
4 587.65 29954.55
6
I could do this in various hacky ways, but what's the right way?
I have a nice application of the by function, which does what I want. The
output looks like this:
> auc_stress
lab.samples.stress$subid: 2
cortisol amylase
1 919.05 6834.8
-
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