Nothing
`generateLpatterns` <-
function(env2)
#######################################################################
# all possible response patterns and/or difference patterns
#######################################################################
#
# CASE LIKERT
{
# data: difference patterns
diffsdat<-differences(get("dat",get("ENV",environment(patt.design))))
#print(head(diffsdat))
# patterns
ncatL<-get("ncatL",get("ENV",environment(patt.design)))
patterns<-all_patterns(0,(ncatL-1)) # all possible likert patterns
diffs<-differences(patterns) # all possible diference patterns
## for the time being responses are centered around 0
# shift data/patterns to 0:(ncatPC-1)
# diffs<-diffs-min(diffs)
# diffsdat<-diffsdat-min(diffsdat)
if (get("blnReducecat",get("ENV",environment(patt.design)))) {
# both next statements for -1/1 parameterisation (instead of 0/1)
# small values in diff correspond to first obj preferred if blnRevert=FALSE
diffs<-ifelse(diffs<0,1,ifelse(diffs>0,-1,0))
diffsdat<-ifelse(diffsdat<0,1,ifelse(diffsdat>0,-1,0))
assign("ncatPC",3,envir=get("ENV",environment(patt.design)))
}
# remove redundant patterns from diffs
diffs<-unique(diffs, MARGIN=1)
# convert diffs (patterns) to string
env2$dpattStr <- convert2strings(diffs) # character representation
env2$datStr<-convert2strings(diffsdat)
env2$npatt<-length(env2$dpattStr) # number of unique possible patterns
env2$diffs<-diffs # numeric representation
env2$blnUndec<-TRUE
}
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