There are multiple ways of doing this, but here are a couple. To just test the fixed effect of treatment you can use the glm function:
test <- read.table(text=" replicate treatment n X 1 A 32 4 1 B 33 18 2 A 20 6 2 B 21 18 3 A 7 0 3 B 8 4 ", header=TRUE) fit1 <- glm( cbind(X,n-X) ~ treatment, data=test, family=binomial) summary(fit1) Note that the default baseline value may differ between R and SAS, which would result in a reversed sign on the slope coefficient (and different intercept). To include replicate as a random effect you need an additional package, here I use lme4 and the glmer function: library(lme4) fit2 <- glmer( cbind(X, n-X) ~ treatment + (1|replicate), data=test, family=binomial) summary(fit2) On Tue, Sep 27, 2016 at 9:03 PM, Shuhua Zhan <sz...@uoguelph.ca> wrote: > Hello R-experts, > I am interested to determine if the ratio of counts from two groups differ > across two distinct treatments. For example, we have three replicates of > treatment A, and three replicates of treatment B. For each treatment, we have > counts X from one group and counts Y from another group. My understanding is > that that GLIMMIX procedure in SAS can calculate whether the ratio of counts > in one group (X/X+Y) significantly differs between treatments. > > I think this is the way you do it in SAS. The replicate and treatment > variables are self-explanatory. The first number (n) refers to the total > counts X + Y; the second number (X) refers to the counts X. Is there a way to > do this in R? Since we have 20,000 datasets to be tested, it may be easier to > retrive the significant test as the given dataset below and its p>F value and > mean ratios of treatments in R than SAS. > > > data test; > input replicate treatment$ n X; > datalines; > 1 A 32 4 > 1 B 33 18 > 2 A 20 6 > 2 B 21 18 > 3 A 7 0 > 3 B 8 4 > ; > > proc glimmix data=test; > class replicate treatment; > model X/n = treatment / solution; > random intercept / subject=replicate; > run; > > ods select lsmeans; > proc glimmix data=test; > class replicate treatment; > model X/n = treatment / solution; > random intercept / subject=replicate; > lsmeans treatment / cl ilink; > run; > > I appreciate your help in advance! > Joshua > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. -- Gregory (Greg) L. Snow Ph.D. 538...@gmail.com ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
