# Testing sample sizes

test.samp = function(maxN=10000,infn,outfn,test="exact",ssize=24,startsize=24) {

 # maxN: number of simulated experiments
 # infn: input data filename
 # outfn: output filename
 # test: "exact" for exact tests, anything else for mixed-effects logistic regression
 # ssize: size of actual experiment (number of speakers)
 # startsize: first size to test (subsequent sizes are smaller)

 # Load data
 claim = read.table(infn,T)
 attach(claim)

 # Prepare output file
 write(c("data:",infn),outfn,ncolumns=2,sep="\t")
 write(c("test:",test),outfn,ncolumns=2,append=T,sep="\t")
 write(c("maxN =",maxN),outfn,ncolumns=2,append=T,sep="\t")

 # Get factor name(s)
 cn = colnames(claim)
 Fact1 = NULL
 Fact2 = NULL
 Fact1 = get(cn[3])
 if(length(cn) == 5) {
  Fact2 = get(cn[4]) # Second factor, if any
 }

 # Choose sample
 ans = matrix(nrow = ssize, ncol = 2)
 i = startsize
 keeptry = TRUE
 while (i > 0 & keeptry) {
  i = i - 1
  ans[i,1] = i
  count.sig = 0
  nsamp = choose(ssize,i)
  combos = t(combn((1:ssize),i))
  if (nsamp >= maxN) {
   N = maxN
  } else {
   N = nsamp
  }
  for (j in 1:N) {
   if (N == maxN) {
    s = sample((1:ssize),i)
   } else {
    s = combos[j,]
   }
   samp = is.element(Speaker,s)
   Sp = Speaker[samp]
   J = Judgment[samp]
   F1 = Fact1[samp]
   if (!is.null(Fact2)) {
    F2 = Fact2[samp]
    claim.res = data.frame(Sp,J,F1,F2)
   } else {
    claim.res = data.frame(Sp,J,F1)
   }
   if (test == "exact") {
    if (!is.null(Fact2)) {
     you.p = small.exp(J,F1,F2,group=Sp)[3]
    } else {
     you.p = small.exp(J,F1,group=Sp)
    }
   } else {
    if (!is.null(Fact2)) {
     you.p = summary(lmer(J ~ F1*F2 + (1|Sp), family="binomial",
      data=claim.res))@coefs[4,4]
    } else {
     you.p = summary(lmer(J ~ F1 + (1|Sp), family="binomial",
      data=claim.res))@coefs[2,4]
    }
   }
   if (you.p < 0.05) {
    count.sig = count.sig + 1
   }
  }
  samp.p = count.sig/N
  ans[i,2] = samp.p
  write(c(i,ans[i,2]),outfn,ncolumns=2,append=T,sep="\t")
  if (samp.p < 0.5) {
   keeptry = FALSE
  }
 }
 detach(claim)
 return(ans)
}