Nothing
glca_init <- function(model)
{
prob_gnr <- function(n) {
ran <- stats::runif(n)
return(ran / sum(ran))
}
Ng <- model$Ng; G <- model$G
C <- model$C; W <- model$W
M <- model$M; R <- model$R
P <- model$P; Q <- model$Q
coeff.inv <- model$coeff.inv
init = list()
if (W == 0L) {
if (P == 1L) {
init$gamma <- lapply(1L:G, function(g)
matrix(1L/C, Ng[g], C))
init$rho <- lapply(1L:G, function(g) lapply(1L:M, function(x)
do.call(rbind, lapply(1L:C, function(c) prob_gnr(R[x])))))
} else {
init$beta <- lapply(1L:G, function(g) matrix(0L, P, C - 1L))
init$rho <- lapply(1L:G, function(g) lapply(1L:M, function(x)
do.call(rbind, lapply(1L:C, function(c) prob_gnr(R[x])))))
}
} else {
if (P == 1L && Q == 0L) {
init$delta <- rep(1L, W) / W;
init$gamma <- do.call(rbind, lapply(1L:W, function(w)
{
ran = rep(1L, C) + stats::runif(C, -0.5, + 0.5)
return(ran / sum(ran))
}))
init$rho <- lapply(1L:M, function(x)
do.call(rbind, lapply(1L:C, function(c) prob_gnr(R[x]))))
} else {
init$delta <- rep(1L, W) / W;
if (coeff.inv)
init$beta <-
c(stats::rnorm(W * (C - 1L), 0L, 0.1) %x% c(1L, numeric(P - 1L)),
numeric(Q * (C - 1L)))
else
init$beta <- stats::rnorm((W * P + Q) * (C - 1L), 0L, 0.1)
init$rho <- lapply(1L:M, function(x)
do.call(rbind, lapply(1L:C, function(c) prob_gnr(R[x]))))
}
}
return(init)
}
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