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mcmcsamp.R
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MCMCsamp <- function(object, mcmc = 1L, verbose = NULL, ...) UseMethod("MCMCsamp")
# from lme4/R/AllGeneric.R
MCMCsamp.Spautolm <- function(object, mcmc = 1L, verbose = NULL, ...,
burnin=0L, scale=1, listw, control=list()) {
con <- list(Imult=2, cheb_q=5, MC_p=16, MC_m=30, super=NULL,
spamPivot="MMD", in_coef=0.1, type="MC",
correct=TRUE, trunc=TRUE, SE_method="LU", nrho=200,
interpn=2000, small_asy=TRUE, small=1500, SElndet=NULL,
LU_order=FALSE)
timings <- list()
.ptime_start <- proc.time()
nmsC <- names(con)
con[(namc <- names(control))] <- control
if (length(noNms <- namc[!namc %in% nmsC]))
warning("unknown names in control: ", paste(noNms, collapse = ", "))
if (is.null(verbose)) verbose <- get("verbose", envir = .spatialregOptions)
stopifnot(is.logical(verbose))
if (!inherits(listw, "listw"))
stop("No neighbourhood list")
method <- object$method
family <- object$family
if (family == "SMA" && method != "eigen") stop("SMA only for eigen method")
X <- object$X
N <- nrow(X)
if (N != length(listw$neighbours))
stop("Input data and neighbourhood list have different dimensions")
stopifnot(ncol(X) == length(object$fit$coefficients))
weights <- object$weights
stopifnot(length(weights) == N)
can.sim <- FALSE
if (listw$style %in% c("W", "S"))
can.sim <- can.be.simmed(listw)
sum_lw <- sum(log(weights))
env <- new.env()
assign("Y", object$Y, envir=env)
assign("X", X, envir=env)
assign("n", N, envir=env)
assign("weights", weights, envir=env)
assign("can.sim", can.sim, envir=env)
assign("family", family, envir=env)
assign("method", method, envir=env)
assign("verbose", verbose, envir=env)
assign("listw", listw, envir=env)
assign("sum_lw", sum_lw, envir=env)
W <- as(listw, "CsparseMatrix")
if (family == "CAR") if (!isTRUE(all.equal(W, t(W))))
warning("Non-symmetric spatial weights in CAR model")
assign("W", W, envir=env)
I <- as_dsCMatrix_I(N)
assign("I", I, envir=env)
Sweights <- as(as(Diagonal(x=weights), "symmetricMatrix"),
"CsparseMatrix")
assign("Sweights", Sweights, envir=env)
timings[["data_setup"]] <- proc.time() - .ptime_start
.ptime_start <- proc.time()
if (verbose) cat(paste("\nJacobian calculated using "))
interval <- jacobianSetup(method, env, con, trs=object$trs,
interval=object$interval)
assign("interval", interval, envir=env)
timings[["logdet_setup"]] <- proc.time() - .ptime_start
.ptime_start <- proc.time()
start <- c(object$lambda, object$fit$coefficients)
V <- object$fdHess
stopifnot(nrow(V) == ncol(V))
stopifnot(nrow(V) == length(start))
res0 <- rwmetrop(logpost=f_spautolm_hess, start=start,
proposal=list(var=V, scale=scale), m=(mcmc+burnin), env=env)
# MCMCmetrop1R(fun=f_spautolm_hess, theta.init=start, burnin=burnin,
# mcmc=mcmc, thin=thin, verbose=ifelse(verbose, verbose_step, 0),
# seed=seed, logfun=TRUE, V=V, env=env)
res <- as.mcmc(res0$par[(burnin+1):(mcmc+burnin),])
attr(res, "accept") <- res0$accept
colnames(res) <- c(names(object$lambda), names(object$fit$coefficients))
attr(res, "type") <- family
timings[["rwmetrop"]] <- proc.time() - .ptime_start
attr(res, "timings") <- do.call("rbind", timings)[, c(1, 3)]
res
}
MCMCsamp.Sarlm <- function(object, mcmc = 1L, verbose = NULL, ...,
burnin=0L, scale=1, listw, listw2=NULL, control=list()) {
timings <- list()
.ptime_start <- proc.time()
con <- list(compiled_sse=FALSE, Imult=2, cheb_q=5, MC_p=16, MC_m=30,
super=NULL, spamPivot="MMD", in_coef=0.1, type="MC",
correct=TRUE, trunc=TRUE, SE_method="LU", nrho=200,
interpn=2000, small_asy=TRUE, small=1500, SElndet=NULL,
LU_order=FALSE)
nmsC <- names(con)
con[(namc <- names(control))] <- control
if (length(noNms <- namc[!namc %in% nmsC]))
warning("unknown names in control: ", paste(noNms, collapse = ", "))
if (is.null(verbose)) verbose <- get("verbose", envir = .spatialregOptions)
stopifnot(is.logical(verbose))
if (!inherits(listw, "listw"))
stop("No neighbourhood list")
type <- object$type
if (length(grep("sac", type) > 0)) Type <- "SAC"
else if (length(grep("error", type) > 0)) Type <- "ERROR"
else Type <- "LAG"
method <- object$method
X <- object$X
N <- nrow(X)
if (N != length(listw$neighbours))
stop("Input data and neighbourhood list have different dimensions")
stopifnot(ncol(X) == length(object$coefficients))
can.sim <- FALSE
if (listw$style %in% c("W", "S"))
can.sim <- can.be.simmed(listw)
weights <- object$weights
if (is.null(weights)) weights <- rep(1, N)
stopifnot(length(weights) == N)
sum_lw <- sum(log(weights))
sw <- sqrt(weights)
env <- new.env()
assign("y", object$y, envir=env)
assign("x", X, envir=env)
assign("n", N, envir=env)
assign("listw", listw, envir=env)
assign("sum_lw", sum_lw, envir=env)
assign("sw", sw, envir=env)
assign("can.sim", can.sim, envir=env)
assign("method", method, envir=env)
assign("verbose", verbose, envir=env)
assign("family", "SAR", envir=env)
W <- as(listw, "CsparseMatrix")
wy <- c(as.matrix(W %*% c(object$y)))
assign("wy", wy, envir=env)
if (is.numeric(object$resvar)) V <- object$resvar[-1,-1]
else V <- object$fdHess
stopifnot(nrow(V) == ncol(V))
timings[["data_setup"]] <- proc.time() - .ptime_start
.ptime_start <- proc.time()
if (verbose) cat(paste("\nJacobian calculated using "))
if (Type == "SAC") {
interval1 <- jacobianSetup(method, env, con, trs=object$trs1,
interval=object$interval1, which=1)
assign("interval1", interval1, envir=env)
if (is.null(listw2)) listw2 <- listw
else if (!inherits(listw2, "listw")) stop("No 2nd neighbourhood list")
can.sim2 <- FALSE
if (listw2$style %in% c("W", "S"))
can.sim2 <- can.be.simmed(listw2)
assign("listw2", listw2, envir=env)
assign("can.sim2", can.sim2, envir=env)
interval2 <- jacobianSetup(method, env, con, trs=object$trs2,
interval=object$interval2, which=2)
assign("interval2", interval2, envir=env)
} else {
interval <- jacobianSetup(method, env, con, trs=object$trs,
interval=object$interval)
assign("interval", interval, envir=env)
assign("hf_calls", 0L, envir=env)
assign("compiled_sse", con$compiled_sse, envir=env)
assign("first_time", TRUE, envir=env)
}
timings[["logdet_setup"]] <- proc.time() - .ptime_start
.ptime_start <- proc.time()
if (Type == "LAG") {
if (con$compiled_sse) {
ptr <- .Call("hess_lag_init", PACKAGE="spatialreg")
assign("ptr", ptr, envir=env)
}
start <- c(object$rho, object$coefficients)
stopifnot(nrow(V) == length(start))
res0 <- rwmetrop(logpost=f_laglm_hess, start=start,
proposal=list(var=V, scale=scale), m=(mcmc+burnin), env=env)
# res <- MCMCmetrop1R(fun=f_laglm_hess, theta.init=start,
# burnin=burnin, mcmc=mcmc, thin=thin, verbose=ifelse(verbose,
# verbose_step, 0), seed=seed, logfun=TRUE, V=V, env=env)
if (con$compiled_sse) {
.Call("hess_lag_free", get("ptr", envir=env),
PACKAGE="spatialreg")
}
res <- as.mcmc(res0$par[(burnin+1):(mcmc+burnin),])
attr(res, "accept") <- res0$accept
colnames(res) <- c(names(object$rho), names(object$coefficients))
attr(res, "type") <- object$type
} else if (Type == "ERROR") {
WX <- as.matrix(W %*% X)
assign("WX", WX, envir=env)
if (con$compiled_sse) {
ptr <- .Call("hess_error_init", PACKAGE="spatialreg")
assign("ptr", ptr, envir=env)
}
start <- c(object$lambda, object$coefficients)
stopifnot(nrow(V) == length(start))
res0 <- rwmetrop(logpost=f_errlm_hess, start=start,
proposal=list(var=V, scale=scale), m=(mcmc+burnin), env=env)
# res <- MCMCmetrop1R(fun=f_errlm_hess, theta.init=start,
# burnin=burnin, mcmc=mcmc, thin=thin, verbose=ifelse(verbose,
# verbose_step, 0), seed=seed, logfun=TRUE, V=V, env=env)
if (con$compiled_sse) {
.Call("hess_error_free", get("ptr", envir=env),
PACKAGE="spatialreg")
}
res <- as.mcmc(res0$par[(burnin+1):(mcmc+burnin),])
attr(res, "accept") <- res0$accept
colnames(res) <- c(names(object$lambda), names(object$coefficients))
attr(res, "type") <- object$type
} else if (Type == "SAC") {
assign("WX", object$W2X, envir=env)
W2 <- as(listw2, "CsparseMatrix")
w2y <- c(as.matrix(W2 %*% object$y))
assign("w2y", w2y, envir=env)
w2wy <- c(as.matrix(W2 %*% wy))
assign("w2wy", w2wy, envir=env)
start <- c(object$rho, object$lambda, object$coefficients)
stopifnot(nrow(V) == length(start))
res0 <- rwmetrop(logpost=f_sac_hess, start=start,
proposal=list(var=V, scale=scale), m=(mcmc+burnin), env=env)
# res <- MCMCmetrop1R(fun=f_sac_hess, theta.init=start,
# burnin=burnin, mcmc=mcmc, thin=thin, verbose=ifelse(verbose,
# verbose_step, 0), seed=seed, logfun=TRUE, V=V, env=env)
res <- as.mcmc(res0$par[(burnin+1):(mcmc+burnin),])
attr(res, "accept") <- res0$accept
colnames(res) <- c(names(object$rho), names(object$lambda),
names(object$coefficients))
attr(res, "type") <- object$type
}
timings[["rwmetrop"]] <- proc.time() - .ptime_start
attr(res, "timings") <- do.call("rbind", timings)[, c(1, 3)]
if (attr(res, "accept") < 0.05)
warning("MCMCsamp: very low acceptance rate")
res
}