Nothing
# mcs: sublist of mod components to be sampled in a block
# e: environment of create_sampler
create_mc_block <- function(mcs, e=parent.frame()) {
type <- "block"
name <- "coef_"
debug <- any(sapply(mcs, `[[`, "debug"))
vec_list <- list()
if (e$control$auto.order.block) {
# order the components such that sparse matrices come first; may help find a better Cholesky permutation
o <- unname(which(vapply(mcs, function(m) isDiagonal(m$X), TRUE))) # start with diagonal matrices
if (length(o))
o <- c(o, seq_along(mcs)[-o][order(vapply(mcs[-o], function(m) sparsity(m$X), 1), decreasing=TRUE)])
else
o <- order(vapply(mcs, function(m) sparsity(m$X), 1), decreasing=TRUE)
mcs <- mcs[o]
rm(o)
}
# start with empty matrices
X <- matrix(0, e$n, 0L) # block design matrix
ind <- 0L
for (mc in mcs) {
if (mc$type == "gen" && mc$gl)
X <- cbind(X, mc$X, zeroMatrix(e$n, mc$glp$q))
else
X <- cbind(X, mc$X)
vec_list[[mc$name]] <- (ind + 1L):ncol(X)
ind <- ncol(X)
}
rm(ind)
X <- economizeMatrix(X)
# template for (updating) blocked precision matrix
# each mc$Q for gen is either ddi or dsC; the same holds true for mc$Q0 for reg and mec
get_Q <- function(mc) if (mc$type == "gen") mc$Q else mc$Q0
if (all(sapply(mcs, function(mc) class(get_Q(mc))[[1L]] == "ddiMatrix"))) {
QT <- Cdiag(unlist(lapply(mcs, function(mc) ddi_diag(get_Q(mc))), use.names=FALSE))
} else {
x <- NULL
i <- NULL
size <- 0L
p <- 0L
for (mc in mcs) {
Q <- get_Q(mc)
if (class(Q)[1L] == "ddiMatrix") {
x <- c(x, ddi_diag(Q))
i <- c(i, size + seq_len(nrow(Q)) - 1L)
p <- c(p, p[length(p)] + seq_len(nrow(Q)))
} else {
x <- c(x, Q@x)
i <- c(i, size + Q@i)
p <- c(p, p[length(p)] + Q@p[-1L])
}
size <- size + nrow(Q)
}
QT <- new("dsCMatrix", i=i, p=p, x=x, uplo="U", Dim=c(size, size))
rm(size, x, i, p)
}
rm(get_Q)
# individual Q matrices no longer needed (we still have kron_prod closures)
for (mc in mcs) if (mc$type == "gen") rm("Q", envir=mc)
get_Qvector <- function(p, tau) {
Qvector <- NULL
for (mc in mcs)
if (mc$type == "gen")
Qvector <- c(Qvector, p[[mc$name_Q]])
else
Qvector <- c(Qvector, tau * mc$Q0@x)
Qvector
}
if (is.null(e$control$CG)) {
if (e$modeled.Q)
XX <- crossprod_sym(X, crossprod_sym(Diagonal(x=runif(e$n, 0.9, 1.1)), e$Q0))
else
XX <- economizeMatrix(crossprod_sym(X, e$Q0), symmetric=TRUE, drop.zeros=TRUE)
# derive constraint matrix, if any
if (any(sapply(mcs, function(mc) !is.null(mc$R)))) {
R <- zeroMatrix(0L, 0L)
for (mc in mcs) {
if (is.null(mc$R)) {
if (mc$type == "gen" && mc$gl)
R <- rbind(R, zeroMatrix(mc$q + mc$glp$q, ncol(R)))
else
R <- rbind(R, zeroMatrix(mc$q, ncol(R)))
} else {
if (mc$type == "gen" && mc$gl)
R <- bdiag(R, mc$glp$R)
else
R <- bdiag(R, mc$R)
}
}
if (nrow(R) != ncol(X)) stop("incompatible dimensions of design and constraint matrices")
# TODO remove individual R matrices as they are not needed in the single block sampler
# add support for additional constraints defined over the whole coefficient vector
# In the case of constraints the XX + Q matrix often becomes singular
# sampling from such a IGMRF can be done by first adding a multiple of tcrossprod(R) to it (Rue and Held, 2005)
# most convenient is to add it to XX;
# But this is not always required. Better add as little as is necessary to get pd (takes up lots of memory for lengthy random walks ...)
# Another option is to add a multiple of I to XX + Q and correct with MH
} else {
R <- NULL
}
if (any(sapply(mcs, function(mc) !is.null(mc$S)))) {
S <- zeroMatrix(0L, 0L)
for (mc in mcs) {
if (is.null(mc$S))
S <- rbind(S, zeroMatrix(mc$q, ncol(S)))
else
S <- bdiag(S, mc$S)
}
if (nrow(S) != ncol(X)) stop("incompatible dimensions of design and constraint matrices")
# TODO remove individual S matrices as they are not needed in the single block sampler
# add support for additional constraints defined over the whole coefficient vector
} else {
S <- NULL
}
if (e$family$family == "gamma") {
if (!e$control$add.outer.R || is.null(R)) {
mat_sum <- make_mat_sum(M0=XX, M1=QT)
XX_Q <- mat_sum(QT)
cholQ <- build_chol(XX_Q)
} else {
stop("TBI: blocked sampler for gamma family with constraints")
}
} else {
sparse_template(environment(), update.XX=e$modeled.Q || any(sapply(mcs, `[[`, "type") == "mec"),
add.outer.R=e$control$add.outer.R, prior.only=e$prior.only)
}
} else {
# TODO check that the only constraints are GMRF equality constraints
S <- NULL
CG_sampler <- setup_CG_sampler(mbs=mcs, X=X, sampler=e, control = e$control$CG)
}
if (e$compute.weights) {
# form the q_all x m matrix corresponding to the linear predictor as represented componentwise in linpred
# TODO do we really need to store both X and t(X) in this case?
linpred <- if (is.null(e$linpred))
economizeMatrix(t(X), strip.names=FALSE)
else
economizeMatrix(t(do.call(cbind, lapply(e$linpred[names(vec_list)], function(x) environment(x)$Xnew))), allow.tabMatrix=FALSE)
}
if (e$prior.only) return(environment())
# BEGIN draw function
draw <- function(p) {}
if (debug) draw <- add(draw, quote(browser()))
if (e$single.block) {
if (e$e.is.res || e$use.offset || e$family$family == "poisson")
draw <- add(draw, quote(p$e_ <- e$y_eff()))
# otherwise p$e_ = e$y_eff() = 0
} else {
for (m in seq_along(mcs)) {
# residuals could also be computed using the block mb$X,
# but this way it is usually faster due to more efficient matrix types
# TODO composite matrix type
if (e$e.is.res)
draw <- add(draw, bquote(p$e_ <- p[["e_"]] + mcs[[.(m)]]$linpred(p)))
else
draw <- add(draw, bquote(p$e_ <- p[["e_"]] - mcs[[.(m)]]$linpred(p)))
}
}
draw <- add(draw, bquote(tau <- .(if (e$sigma.fixed) 1 else quote(1 / p[["sigma_"]]^2))))
# update the block-diagonal joint precision matrix
draw <- add(draw, quote(attr(QT, "x") <- get_Qvector(p, tau)))
if (e$family$family == "gamma") {
# TODO if only reg components cholQ is fixed
draw <- add(draw, quote(cholQ$update(mat_sum(QT))))
# compute alpha and kappa
if (e$family$alpha.fixed) {
alpha <- e$family$get_shape()
if (e$single.block)
kappa <- alpha * e$y
else {
kappa0 <- alpha * e$y
draw <- add(draw, quote(kappa <- kappa0 * exp(-p[["e_"]])))
}
} else {
draw <- add(draw, quote(alpha <- e$family$get_shape(p)))
if (e$single.block)
draw <- add(draw, quote(kappa <- alpha * e$y))
else
draw <- add(draw, quote(kappa <- alpha * e$y * exp(-p[["e_"]])))
}
draw <- add(draw, quote(z <- rMLiG(e$n, alpha, kappa)))
draw <- add(draw, quote(Hz <- crossprod_mv(X, z)))
# contributions from mcs
draw <- add(draw, quote(
for (m in seq_along(mcs)) {
if (m == 1L) ind <- 1L
mc <- mcs[[m]]
if (mc$type == "reg") {
if (mc$informative.prior) {
if (mc$zero.mean)
z <- rMLiG(mc$q, rep.int(mc$prior$a, mc$q), rep.int(mc$prior$a, mc$q))
else
z <- rMLiG(mc$q, rep.int(mc$prior$a, mc$q), mc$prior$a * exp((1/sqrt(mc$prior$a)) * sqrt(mc$prior$precision) * mc$prior$mean))
Hz[ind:(ind + mc$q - 1L)] <- Hz[ind:(ind + mc$q - 1L)] + sqrt(mc$prior$precision) * z
}
} else {
stop("TBI: blocked sampler for gamma sampling distribution with other than reg components")
}
ind <- ind + mc$q
}
))
draw <- add(draw, quote(coef <- cholQ$solve(Hz)))
} else {
if (!is.null(S)) { # need to reconstruct coef_ as input to TMVN sampler
# TODO store coef_ component and only replace the subcomponents with PX
# and check whether this works in case of gen component with gl=TRUE
draw <- add(draw, quote(
for (mc in mcs) p[["coef_"]][vec_list[[mc$name]]] <- p[[mc$name]]
))
}
# update mec component columns of X
# TODO more efficient update of only those elements that can change (for dgC or matrix X)
for (mc in mcs)
if (mc$type == "mec")
draw <- add(draw, bquote(X[, vec_list[[.(mc$name)]]] <- p[[.(mc$name_X)]]))
if (e$single.block && !e$modeled.Q && !any(sapply(mcs, `[[`, "type") == "mec") && e$family$link != "probit")
Xy <- crossprod_mv(X, e$Q0 %m*v% e$y_eff())
else
draw <- add(draw, quote(Xy <- crossprod_mv(X, e$Q_e(p))))
if (is.null(e$control$CG)) {
if (e$modeled.Q) {
if (e$Q0.type == "symm")
draw <- add(draw, quote(XX <- crossprod_sym(X, p[["QM_"]])))
else {
cps_template <- NULL
if (inherits(X, "dgCMatrix")) {
tryCatch(
cps_template <- sparse_crossprod_sym_template(X, e$control$max.size.cps.template),
error = function(e) {
# template too large
NULL
}
)
}
if (is.null(cps_template)) {
draw <- add(draw, quote(XX <- crossprod_sym(X, p[["Q_"]])))
} else {
draw <- add(draw, quote(XX <- cps_template(p[["Q_"]])))
}
}
} else if (any(sapply(mcs, `[[`, "type") == "mec")) {
draw <- add(draw, quote(XX <- crossprod_sym(X, e$Q0)))
}
draw <- add(draw, quote(Qlist <- update(XX, QT, 1, 1/tau)))
draw <- add(draw, bquote(coef <- MVNsampler$draw(p, .(if (e$sigma.fixed) 1 else quote(p[["sigma_"]])), Q=Qlist$Q, Imult=Qlist$Imult, Xy=Xy)[[.(name)]]))
} else {
draw <- add(draw, quote(CGstart <- vector("numeric", ncol(X))))
draw <- add(draw, quote(for (mc in mcs) CGstart[vec_list[[mc$name]]] <- p[[mc$name]]))
draw <- add(draw, quote(coef <- CG_sampler$draw(p, Xy, X, QT, e, start=CGstart)))
}
}
# split coef and assign to the separate coefficient batches
for (m in seq_along(mcs)) {
if (mcs[[m]]$type == "gen" && mcs[[m]]$gl) {
draw <- add(draw, bquote(u <- coef[vec_list[[.(mcs[[m]]$name)]]]))
draw <- add(draw, bquote(p[[.(mcs[[m]]$name)]] <- u[mcs[[.(m)]]$i.v]))
draw <- add(draw, bquote(p[[.(mcs[[m]]$name_gl)]] <- u[mcs[[.(m)]]$i.alpha]))
} else {
draw <- add(draw, bquote(p[[.(mcs[[m]]$name)]] <- coef[vec_list[[.(mcs[[m]]$name)]]]))
}
if (e$e.is.res) {
draw <- add(draw, bquote(mv_update(p[["e_"]], plus=FALSE, mcs[[.(m)]][["X"]], p[[.(mcs[[m]]$name)]])))
} else {
if (m == 1L && e$single.block && !e$use.offset && e$family$family != "poisson") {
# in this case p$e_ = e$y_eff() = 0
draw <- add(draw, quote(p$e_ <- mcs[[1L]]$linpred(p)))
} else {
#draw <- add(draw, bquote(p$e_ <- p[["e_"]] + mcs[[.(m)]]$linpred(p)))
draw <- add(draw, bquote(mv_update(p[["e_"]], plus=TRUE, mcs[[.(m)]][["X"]], p[[.(mcs[[m]]$name)]])))
}
}
}
if (e$compute.weights) {
# TODO solve-sparse method that returns dense
draw <- add(draw, quote(p$weights_ <- X %m*m% as.matrix(MVNsampler$cholQ$solve(linpred))))
if (e$modeled.Q) {
if (e$Q0.type == "symm")
draw <- add(draw, quote(p$weights_ <- p[["QM_"]] %m*m% p[["weights_"]]))
else
draw <- add(draw, quote(p$weights_ <- p[["Q_"]] * p[["weights_"]]))
} else {
if (e$Q0.type != "unit") {
draw <- add(draw, quote(p$weights_ <- e$Q0 %m*m% p[["weights_"]]))
}
}
}
draw <- add(draw, quote(p))
# END draw function
start <- function(p) {}
if (is.null(e$control$CG)) {
if (e$family$family == "gamma") {
start <- add(start, bquote(coef <- Crnorm(.(ncol(X)))))
} else {
start <- add(start, bquote(coef <- MVNsampler$start(p, e$scale_sigma)[[.(name)]]))
}
start <- add(start, quote(
for (mc in mcs) {
if (mc$type == "gen" && mc$gl) {
u <- coef[vec_list[[mc$name]]]
if (is.null(p[[mc$name]])) p[[mc$name]] <- u[mc$i.v]
if (is.null(p[[mc$name_gl]])) p[[mc$name_gl]] <- u[mc$i.alpha]
} else {
if (is.null(p[[mc$name]])) p[[mc$name]] <- coef[vec_list[[mc$name]]]
}
}
))
} else {
start <- add(start, quote(
for (mc in mcs) {
if (mc$type == "gen" && mc$fastGMRFprior)
p[[mc$name]] <- mc$rprior(p)[[mc$name]]
else
p[[mc$name]] <- Crnorm(mc$q, sd=e$scale_sigma)
}
))
}
# TODO check formats of user-provided start values
start <- add(start, quote(p))
rm(mc)
environment()
}
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