phoc <-
function(dframe, dv, sid, indfacs)
 {
 
 dv = dframe[,names(dframe) == dv]
 sid = dframe[,names(dframe) == sid]
 indfacs = dframe[,names(dframe) %in%  indfacs]
 indfacs = apply(as.matrix(indfacs),1, paste, collapse=":")
  
 u = unique(indfacs)
 n = length(u)
 mat = NULL
 for(j in 1:(n-1)){
 w = u[j]
 temp = indfacs == w
 wdata = dv[temp]
 wspeak = sid[temp]
 if(any(duplicated(wspeak)))
 stop("Unique values per subject-factor combination required; aggregate over factors and try again")

 x = u[(j+1):n]
 for(k in x){
 paired = FALSE
 temp = indfacs == k
 kdata = dv[temp]
 kspeak = sid[temp]
 if(any(duplicated(kspeak)))
 stop("Unique values per subject-factor combination required; aggregate over factors and try again")
 
 
 if(length(wspeak)==length(kspeak))
 # if the lengths of the speaker vectors are the same
 {
 # make sure they are all the same
 temp = all(wspeak==kspeak)
 if(temp)
 # if so
 {
 # make sure they're aligned by speaker
 m = match(wspeak, kspeak)
 kdata = kdata[m]
 # and set paired to T
 paired = T
 }
 }
 # do the t-test
 res = t.test(wdata, kdata, paired=paired)
 mat$res = rbind(mat$res, c(res[[1]], res[[2]], res[[3]]))
 mat$name = c(mat$name, paste(u[j], k, sep="-"))
 mat$paired = c(mat$paired, paired)
 }
 }
mat$bonf = nrow(mat$res)
rownames(mat$res) = mat$name
colnames(mat$res) = c("t", "df", "prob-adj")
# Bonf. adjust
mat$res[,3] = mat$res[,3] * mat$bonf
mat$res[,3][mat$res[,3] > 1] = 1
 mat
 }

phsel <- function(phocresults, k=1)
{
# written by Jonathan Harrington

# choose between whether the output is from
# phoc() or from glht()
phsel.glht <-
function(mmphoc, k = 1)
{
mx = names(mmphoc$test$tstat)
omatx = cbind(mmphoc$test$tstat, mmphoc$test$pvalues)
colnames(omatx) = c("z value", "Adjusted p values")
mx.un = matrix(unlist(strsplit(mx, " - ")), ncol=2, byrow=T)
# Number of independent variables
nx = length(unlist(strsplit(mx.un[1,1], ".", fixed=TRUE)))
leftx = matrix(unlist(strsplit(mx.un[,1], ".", fixed=TRUE)), ncol=nx, byrow=T)
rightx = matrix(unlist(strsplit(mx.un[,2], ".", fixed=TRUE)), ncol=nx, byrow=T)

# leave out one or more  of the columns
leftx = as.matrix(leftx[,-k])
rightx = as.matrix(rightx[,-k])
matx = NULL
for(j in 1:nrow(leftx)){
vecx = all(leftx[j,]==rightx[j,])
matx = c(matx, vecx)
}
(omatx[matx,])
}

phsel.anova <- function(tukeyoutput, k=1)
{
# written by Jonathan Harrington
# the data is modifief from K. Johnson (Pitt_Shoaf2.txt)
# psh = read.table(file.path(pfad, "psh.txt"))
# carry out ANOVA
# psh.aov = aov(rt ~ Overlap * Position, data=psh)
# summary(psh.aov)
# Tukey-Test
# psh.tk = TukeyHSD(psh.aov)
# Here are the components of the Tukey-Test
# The interaction term is Overlap:Position
# and it is is position 3
# names(psh.tk)
# Select the results of the Tukey test keeping
# the first factor, Position constant
# phsel(psh.tk[[3]])
# the same
# phsel(psh.tk[[3]], 1)
# Select the results of the Tukey test keeping
# the second factor, Overlap constant
# phsel(psh.tk[[3]], 2)

m = rownames(tukeyoutput)
m.un = matrix(unlist(strsplit(m, "-")), ncol=2, byrow=T)
# Number of independent variables
n = length(unlist(strsplit(m.un[1,1], ":", fixed=TRUE)))
left = matrix(unlist(strsplit(m.un[,1], ":", fixed=TRUE)), ncol=n, byrow=T)
right = matrix(unlist(strsplit(m.un[,2], ":", fixed=TRUE)), ncol=n, byrow=T)

# leave out one or more  of the columns
left = as.matrix(left[,-k])
right = as.matrix(right[,-k])
mat = NULL
for(j in 1:nrow(left)){
vec = all(left[j,]==right[j,])
mat = c(mat, vec)
}

as.matrix(tukeyoutput[mat,])
}


# function begins here and chooses between ghlt and anova
if(any(class(phocresults) == "glht"))
rphoc = phsel.glht(phocresults, k)
else
rphoc = phsel.anova(phocresults, k)
rphoc
}


. <-
function (..., .env = parent.frame()) 
{
    structure(as.list(match.call()[-1]), env = .env, class = "quoted")
}