In tnearey/bhmnl: Bayesian Hierarichical Multinomial Logistic( with FactorableResponses)

# ---------TestMake_respFacStimDtb= function(){ ----------
# Future expansion ??

tst=readline( "do test y/n? ")
srDtb=data.table(myFactorialHMNL_CreateData())
# str(srDtb)
# Classes ‘data.table’ and 'data.frame':    100 obs. of  6 variables:
#     $ syl   : Factor w/ 6 levels "ba","bi","bu",..: 4 3 2 6 6 1 5 6 5 2 ...
# $ x     : num  0.253 -2.029 -2.043 1.369 -0.226 ...
# $ y     : num  0.788 0.769 2.332 -1.008 -0.119 ...
# $ chosen: Factor w/ 6 levels "ba","bi","bu",..: 4 3 2 6 6 1 5 6 5 2 ...
# $ cn   : Factor w/ 2 levels "b","d": 2 1 1 2 2 1 2 2 2 1 ...
# $ vow   : Factor w/ 3 levels "a","i","u": 1 3 2 3 3 1 2 3 2 2 ...
# - attr(*, ".internal.selfref")=<externalptr
#
#


respFacDtb=make_respFacDtb(srDtb,'syl',c('syl','cn','vow'))
respVarColNames = names(respFacDtb)
catln('before respFacDtb expansion')
showRespFacDtbLevs(respFacDtb)
# readline('Here')
# 

if (tst!='y') stop('Stopped by user')
# Maybe if this is defined before the call to make_rsDmx?
# contrTreat=function(...) car::contr.Treatment(...)
# This has responsecategory varying fastest in rows then stimuli
rsDmx= make_Dmx(~cn+vow+cn:vow+cn:x+vow:y+cn:m, respFacStimDtbPadded,respVarColNames,stimVarColNames)
srDmx=make_Dmx(~cn+vow+cn:vow+cn:x+vow:y+cn:m, stimRespFacDtbPadded,respVarColNames,stimVarColNames)
catln('rsDmx')
print(head(rsDmx,n=20))
catln('srDmx')
print(head(srDmx,n=20))


## ------------------------------------------------------------------------

X=list()
rowOffset=0
# We also have to pull off the subject (Respondant) variable
for (n in 1:nObs){
    X[[n]]=rsDmx[rowOffset+1:C, ]
    rowOffset=rowOffset+C
}

# data {
#   int<lower=2> C; // Number of alternatives (choices) in each scenario
#   int<lower=1> K; // Number of alternatives
#   int<lower=1> R; // Number of respondents
#   int <lower=1> N; // total number of observations (grand trials) //// int<lower=1> S; // Number of scenarios per respondent
#   int<lower=1,upper=C> Y[N]; // YB[R, S]; // best choices
#   // int<lower=1,upper=C> YW[R, S]; // worst choices
#   matrix[C, K] X[N]; // was   matrix[C, K] X[R, S]; // matrix of attributes for each obs
#   int<lower=1, upper=R> RID[N]; // Added tmn. Serial identifier for respondant r on grandtrial N.
# }
stanData=list( N=as.integer(nObs), C= as.integer(nrespCat), K = as.integer(ncol(rsDmx)),
               R=


)

tnearey/bhmnl documentation built on Nov. 5, 2019, 10:55 a.m.