Nothing
quadIT <-
function(nodes=14, mu=0, sigma=1,absrange=5, ngr=1, ergE=NULL)
{
# riqv_quer = resulting object of the Estep function. NULL if a parametric prior should be estimated
# ergE != NULL if nonpar estimation is required
if(all(is.null(ergE)))
{
nwpgru <- mapply(function(gruu,mug,si)
{
if(length(nodes)==1)
{
if(nodes < 7)
{
nodes <- 7
cat("#nodes == too small --> set to 7\n")
absrange <- 4
}
quadP <- seq(absrange*(-1), absrange, length.out = nodes)
quadweight <- dnorm(quadP)
quadweight1 <- quadweight/sum(quadweight)
quadP_shift <- quadP*si + mug
} else {
quadP <- nodes - mean(nodes)
quadweight <- dnorm(quadP)
quadweight1 <- quadweight/sum(quadweight)
quadP_shift <- quadP*si + mug
}
list(nodes=quadP_shift,weights=quadweight1)
},gruu=1:ngr,mug=mu,si=sigma,SIMPLIFY=FALSE)
return(nwpgru)
} else { # nonparametric things
# loop over GROUPS
nwpgru <- mapply( function(pg,gruu)
{
At <- rowSums(pg) # estimate empirical weights
quadnodes <- seq(absrange*(-1), absrange, length.out = nodes) # create nodes
if(any(At< 0.0001))
{ # if the values get too small (metric = Expected number of persons) set a small number
At[At < 0.0001] <- 0.0001
}
if(gruu == 1) # for reference group or in case there is only one group
{
QN <- stdrdize_hist(At,quadnodes) # first group gets an standardized histogram
} else
{
AtdAt <- At / sum(At)
QN <- list(nodes=quadnodes,weights=AtdAt)
}
return(QN)
}, pg=ergE$fiqG,gruu=1:ngr, SIMPLIFY=FALSE)
}
}
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