[R] Understanding power analysis in glm and binomial proportion test
Bert Gunter
gunter.berton at gene.com
Thu May 1 20:42:14 CEST 2014
While R is certainly used for statistical simulations as you showed,
this list is really for questions about R programming, not statistics.
While they certainly overlap and someone may respond here, I suggest
you post this to stats.stackexchange.com or other statistics site that
is specifically for such issues.
Cheers,
Bert
Bert Gunter
Genentech Nonclinical Biostatistics
(650) 467-7374
"Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom."
H. Gilbert Welch
On Thu, May 1, 2014 at 7:00 AM, Wall, Wade A ERDC-RDE-CERL-IL
<Wade.A.Wall at usace.army.mil> wrote:
> Hi all,
>
> I am trying to run a power analysis using simulated data to compare the power of a glm versus a binomial proportion test to detect differences in proportions. For example, suppose you have some proportion that decreases by some amount over X number of time steps.
> .4,.39,.38,.37 . . . . .01 and simulate data over those time steps based on the decrease. Does a glm approach (differences in slope) "outperform" an approach whereby you simply look at proportional differences. I ran simulations and basically summed up the number of times p<.05 divided by the number of trials. I was expecting that a glm approach would have more power because it would be utilizing all the data across multiple time steps, whereas a binomial proportion test is only comparing two populations ( the beginning and the end points).
>
> However the results indicated that a binomial proportion test had more power relative to a glm at fewer time steps and that, depending on the simulated decrease in proportions, the relationship between glm power and binomial proportion test power changed. Interestingly, a greater decreases, a binomial proportion test seems have greater power compared to a glm, which to me is counterintuitive since the slope should be greater.
>
> I am attaching the code below my questions in case anyone is interested.
>
> My questions are:
>
>
> (1) Am I interpreting the results and p-values correctly?
>
> (2) If I am interested in trends, does glm results really have lower power and, if so, is there a way to combine the two tests?
>
> I know that I am ignoring a lot of issues such as autocorrelation, but I am really just trying to understand the output.
>
> Any suggestions or insights would be appreciated.
>
> ##### Attempt at using glm model ######
> ltProb <- 0.4 ## longterm average of nest survival probability
> change <- c(.01,.03,.05)
> yrs <- seq(1,20, by=1) ## Years of inquiry, probability of detecting a yearly change nest survival
> samplesize <- 50 ## Reasonable sample size range
> reps <- 1000 ## of simulations per
> SurvProb <- ltProb ## initiating survival probablity for later use, nuisance
> power <- matrix(nrow=length(change)*length(yrs)*length(samplesize), ncol=5) ## creating a matrix to hold data
>
> scenario <- 0 ## initializing scenarios which will be a placeholder later on
> for (a in 1:length(change)){ ## loop through yearly pop change scenarios
> for (c in 1:length(samplesize)){ ## loop through sample size scenarios
> for (b in 1:length(yrs)){ ## loop through years sceanarios
> scenario=scenario+1
> power[scenario,1] <- change[a] ## filling matrix with pop change used
> power[scenario,2]<-ltProb*((1-change[a])**yrs[b])
> power[scenario,3] <- yrs[b] ## filling matrix with number of years used
> power[scenario,4] <- samplesize[c] ## filling matrix with sample size used
> }
> }
> }
> colnames(power) <- c("PopChange", "Proportion2","yrs", "Sample_Size", "Power")
> power=as.data.frame(power)
> power$Sample_Size=as.numeric(power$Sample_Size)
>
> scenario=levels(as.factor(power$PopChange)) ## To subset power
> ps = levels(as.factor(power$Sample_Size))
> results <- matrix(nrow=0, ncol=5) ## final matrix
> results=as.data.frame(results)
> reps=1000 ## set number of reps
>
> for (k in 1:length(scenario)){
> for (m in 1:length(ps)){
> sub=power[(power$PopChange==scenario[k]) & power$Sample_Size==ps[m],] ## Subset by scenario and sample size
> probs = matrix(nrow=1000,ncol=16) ## this is matrix for probabilities.
> dat=matrix(nrow=0,ncol=3,NA)
> dat=as.data.frame(dat)
> for (l in 1:reps){ ##This should be for the replicates
> dat=matrix(nrow=0,ncol=3,NA)
> dat=as.data.frame(dat)
> print(l)
> for (j in 1:nrow(sub)){
> tmp=rbinom(n=sub$Sample_Size[j],size=1,prob=sub[j,2])
> tmp.dat=cbind(tmp,rep(sub$yrs[j],sub$Sample_Size[j]),rep(sub$Proportion2[j],sub$Sample_Size[j]))
> dat=rbind(dat,tmp.dat)
> if (sub$yrs[j]>4){
> x=(glm(dat[,1]~dat[,2],family=binomial))
> probs[l,j-4]=ifelse(summary(x)$coefficients[2,4]<.05,1,0)
> }
> }
> }
> sub$Power[5:20]=apply(probs,2,sum)/nrow(probs)
> results=rbind(results,sub)
> }
> }
>
> tmp=results[!is.na(results$Power),] ### remove NAs
> tmp$SampleSize_Decline=paste(tmp$Sample_Size,tmp$PopChange,sep=":")
> tmp$SampleSize_Decline=as.factor(tmp$SampleSize_Decline)
>
> ### Now do the same using prop.test #####
> tmp$PropPower = NA
> for (i in 1:nrow(tmp)){
> print(i)
> x=0
> for (j in 1:1000){
> a = rbinom(tmp$Sample_Size[i],1,prob = (tmp$Proportion2[i]))
> b=binom.test(sum(a),length(a),p=.4,conf.level=.95,alternative="less")
> if (b$p.value < .05) {x=x+1}
> }
> tmp$PropPower[i] = x/1000
> }
>
> ##### graph results
> d=unique(tmp$PopChange)
> for (i in 1:length(d)){
> par(mfcol = c(1,2))
> sub = tmp[tmp$PopChange == d[i],]
> plot(sub$yrs,sub$Power,col="red", main = d[i], xlab = "Number of years",ylab="Power")
> points(sub$yrs,sub$PropPower,col="black")
> plot(sub$PropPower,sub$Power,xlab="Binomial Proportion Power", ylab="GLM Power",main=d[i])
> abline(0,1)
> readline("Press <return to continue")
> }
>
>
> ##### End Code #####
>
>
>
> Wade A. Wall
> US Army ERDC-CERL
> P.O. Box 9005
> Champaign, IL 61826-9005
> 1-217-373-4420
> Wade.A.Wall at usace.army.mil<mailto:Wade.A.Wall at usace.army.mil>
>
>
>
>
> [[alternative HTML version deleted]]
>
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