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gjamPriorTemplate <- function(formula, xdata, ydata, lo = NULL, hi = NULL){
# template for prior coefficient matrix
# lo - list of lower limits
# hi - list of upper limits
tmp <- grep('_',colnames(ydata))
if(length(tmp) > 0)stop("remove '_' from colnames(ydata)")
tmp <- grep('_',colnames(xdata))
if(length(tmp) > 0)stop("remove '_' from colnames(xdata)")
if( !is.character(formula) )formula <- as.formula( formula )
x <- model.matrix( formula, xdata )
S <- ncol(ydata) # no. responses
Q <- ncol(x) # no. predictors
xnames <- colnames(x)
xnames[1] <- 'intercept'
ynames <- colnames(ydata)
beta <- matrix(0,Q,S)
rownames(beta) <- xnames
colnames(beta) <- ynames
loBeta <- beta - Inf
hiBeta <- beta + Inf
if( !is.null(lo) ){
loBeta <- .setLoHi(plist = lo, pmat = loBeta, xnames, ynames)
}
if( !is.null(hi) ){
hiBeta <- .setLoHi(plist = hi, pmat = hiBeta, xnames, ynames)
}
wna <- which( sapply(lo, is.na) )
if(!is.null(hi))wna <- c( wna, which( sapply(hi, is.na) ) )
if(length(wna) > 0){
rc <- columnSplit( names(wna), '_')
rc <- rc[ rc[,2] %in% rownames(loBeta), ] # reference level for factors will be absent
loBeta[ rc[,2], rc[,1] ] <- hiBeta[ rc[,2], rc[,1] ] <- 0
}
attr(loBeta,'formula') <- attr(hiBeta,'formula') <- formula
list(lo = loBeta, hi = hiBeta)
}
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