Colleague,
smokers <- c( 83, 90, 129, 70 )
patients <- c( 86, 93, 136, 82 )
pairwise.prop.test(smokers, patients)
# Output
Pairwise comparisons using Pairwise comparison of proportions
data: smokers out of patients
1 2 3
2 1.000 - -
3 1.
Hi,
I'm wondering if i can use the function "TukeyHSD" to perform the all pairwise
comparisons of a "aov()" model with one factor (e.g., GROUP) and one continuous
covariate (e.g., AGE). I did for example:
library(multcomp)
data('litter', package = 'multcomp')
litter.aov <- aov(weight ~ gesttime
Hi Arun,
Many thanks for following this through. Based on your earlier suggestion
with CEBPA, I also edited the code and now it seems work (I removed the non
exiting names). I looked into the current code that you have sent below
and I learnt a few more things. So thank you again.
Your skills i
Hi,
Using the example code without removing CEBPA:
gset<- read.table("Names.txt",header=TRUE,stringsAsFactors=FALSE)
temp1<- read.table("Data.txt",header=TRUE,stringsAsFactors=FALSE)
lst1<-split(temp1,temp1$Names)
mat1<-combn(gset[,1],2)
library(plyr)
lst2<-lapply(split(mat1,col(mat1)),function(x
Hi Manisha,
I didn't run your dataset as I am on the way to college. But, from the error
reported, I think it will be due to some missing combinations in one of the
dataset. For ex. if you run my previous code without removing CEBPA:
ie.
mat1<- combn(gset[,1],2)
lst2<-lapply(split(mat1,col(ma
HI,
Not sure about what your expected output would be. Also 'CEBPA' was not
present in the Data.txt.
gset<- read.table("Names.txt",header=TRUE,stringsAsFactors=FALSE)
temp1<- read.table("Data.txt",header=TRUE,stringsAsFactors=FALSE)
lst1<-split(temp1,temp1$Names)
mat1<-combn(gset[-1,1],2) #rem
On Apr 9, 2012, at 3:17 PM, Kerapi wrote:
Hi!,
I'm really hoping someone out there will be able to help me.
I recently started my MSc dissertation on Population Projection
Matrices, which has been going well until now. I am trying to set-up
a general script that does a pairwise comparison
Hi!,
I'm really hoping someone out there will be able to help me.
I recently started my MSc dissertation on Population Projection Matrices, which
has been going well until now. I am trying to set-up a general script that does
a pairwise comparison of all elements in my matrices.
So for exampl
analysis of variance was signifigant
there are a couple of observations that are NA
I would like to do a means seperation
TukeyHSD or pairwise.t.test
how do I deal with the NAs
I will provide the data if necessary
--
Let's not spend our time and resources thinking about things that are
so littl
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