Nothing
R/stanmarg_data.R
In blavaan: Bayesian Latent Variable Analysis
Defines functions
stanmarg_matdata
stanmarg_data
check_priors
format_priors
group_sparse_skeleton
make_sparse_skeleton
# This code is based on code from the LERSIL package, authored by Ben Goodrich
# Convert a skeleton matrix in R to the internal format used by Stan
#
# @param skeleton A matrix that indicates the restrictions placed on
# its elements. If `NA`, then the element is unrestricted. If
# `Inf` or `-Inf`, the element is unrestricted but is constrained
# to be positive or negative respectively. Otherwise, the element is
# fixed to the specified number, which is often zero but can be any finite
# value.
# @return A list containing the sparse representation of the input matrix
make_sparse_skeleton <- function(skeleton) {
stopifnot(is.matrix(skeleton))
#skeleton[is.na(skeleton)] <- 1L
vals <- c(t(skeleton)) # vals needs to be in row-major order
## addresses change to Matrix clashing with extract_sparse_parts
spmat <- Matrix::Matrix(!(skeleton==0L), doDiag=FALSE, sparse=TRUE)
parts <- rstan::extract_sparse_parts(spmat) #is.na(skeleton))
parts$w <- as.array(vals[!(vals==0L)]) #is.na(vals)])
parts$v <- as.array(parts$v)
parts$u <- as.array(parts$u)
return(parts)
}
group_sparse_skeleton <- function(skeleton) {
stopifnot(length(dim(skeleton)) == 3)
Ng <- dim(skeleton)[1]
g_len <- u_len <- array(NA, Ng)
tmpu <- tmpw <- tmpv <- list()
for (g in 1:Ng) {
## removing first dimension and maintaining dims 2+3 is tricky:
parts <- make_sparse_skeleton(array(skeleton[g,,],
dim = dim(skeleton)[2:3]))
wlen <- length(parts$w)
g_len[g] <- wlen
u_len[g] <- length(parts$u)
tmpu <- c(tmpu, list(parts$u))
tmpw <- c(tmpw, list(parts$w))
tmpv <- c(tmpv, list(parts$v))
}
vdat <- wdat <- matrix(0, Ng, max(g_len))
udat <- matrix(0, Ng, max(u_len))
for (g in 1:Ng) {
if (g_len[g] > 0) {
vdat[g, 1:g_len[g]] <- tmpv[[g]]
wdat[g, 1:g_len[g]] <- tmpw[[g]]
}
if (u_len[g] > 0) {
udat[g, 1:u_len[g]] <- tmpu[[g]]
}
}
out <- list(g_len = g_len, v = vdat, w = wdat, u = udat)
return(out)
}
# Get prior parameters in a manner that Stan will like
#
# @param lavpartable A lavaan partable with "priors" column
# @param mat The matrix for which we are obtaining priors
# @return A list containing the prior parameters
format_priors <- function(lavpartable, level = 1L) {
## parameter matrices are filled in by row, so need to make
## sure we get parameters in the right order!
if ("group" %in% names(lavpartable)) {
lavpartable <- lavpartable[order(lavpartable$group, lavpartable$col, lavpartable$row),]
} else {
lavpartable <- lavpartable[order(lavpartable$col, lavpartable$row),]
}
transtab <- list(c('lambda_y_mn', 'lambda_y_sd', 'len_lam_y'),
c('lambda_x_mn', 'lambda_x_sd', 'len_lam_x'),
c('gamma_mn', 'gamma_sd', 'len_gam'),
c('b_mn', 'b_sd', 'len_b'),
c('theta_sd_shape', 'theta_sd_rate', 'len_thet_sd', 'theta_pow'),
c('theta_x_sd_shape', 'theta_x_sd_rate', 'len_thet_x_sd', 'theta_x_pow'),
c('theta_r_alpha', 'theta_r_beta', 'len_thet_r'),
c('theta_x_r_alpha', 'theta_x_r_beta', 'len_thet_x_r'),
c('psi_sd_shape', 'psi_sd_rate', 'len_psi_sd', 'psi_pow'),
c('psi_r_alpha', 'psi_r_beta', 'len_psi_r'),
c('phi_sd_shape', 'phi_sd_rate', 'len_phi_sd', 'phi_pow'),
c('phi_r_alpha', 'phi_r_beta', 'len_phi_r'),
c('nu_mn', 'nu_sd', 'len_nu'),
c('alpha_mn', 'alpha_sd', 'len_alph'),
c('tau_mn', 'tau_sd', 'len_tau'))
mats <- c('lambda', 'lambda_x', 'gamma', 'beta', 'thetavar', 'cov.xvar', 'thetaoff',
'cov.xoff', 'psivar', 'psioff', 'phivar', 'phioff', 'nu', 'alpha', 'tau')
if (level == 2L) {
newmats <- c('lambda', 'beta', 'thetavar', 'thetaoff', 'psivar', 'psioff', 'nu', 'alpha')
subloc <- match(newmats, mats)
mats <- newmats
transtab <- transtab[subloc]
transtab <- lapply(transtab, function(x) paste0(x, '_c'))
}
out <- list()
for (i in 1:length(mats)) {
mat <- origmat <- mats[i]
if (grepl("var", mat)) {
mat <- gsub("var", "", mat)
prisel <- lavpartable$row == lavpartable$col
} else if (grepl("psioff", mat)) {
mat <- "lvrho"
prisel <- lavpartable$row != lavpartable$col
} else if (grepl("thetaoff", mat)) {
mat <- "rho"
prisel <- lavpartable$row != lavpartable$col
} else {
prisel <- rep(TRUE, length(lavpartable$row))
}
if (mat == "nu") {
prisel <- prisel & (lavpartable$mat %in% c(mat, "mean.x"))
} else {
prisel <- prisel & (lavpartable$mat == mat)
}
prisel <- prisel & (lavpartable$free > 0)
thepris <- lavpartable$prior[prisel]
if (length(thepris) > 0) {
textpris <- thepris[thepris != ""]
prisplit <- strsplit(textpris, "[, ()]+")
param1 <- sapply(prisplit, function(x) x[2])
if (!grepl("\\[", prisplit[[1]][3])) {
param2 <- sapply(prisplit, function(x) x[3])
} else {
param2 <- rep(NA, length(param1))
}
## check that var/sd/prec is same for all
powargs <- sapply(prisplit, tail, 1)
if (any(grepl("\\[", powargs))) {
if (length(unique(powargs)) > 1) stop(paste0("blavaan ERROR: In matrix ", mat, ", all priors must be placed on either vars, sds, or precisions."))
}
powpar <- 1
powarg <- powargs[1]
if (grepl("\\[var\\]", powarg)) {
powpar <- 2
} else if (!grepl("\\[sd\\]", powarg)) {
powpar <- -2
}
param1 <- array(as.numeric(param1), length(param1))
param2 <- array(as.numeric(param2), length(param2))
} else {
param1 <- array(0, 0)
param2 <- array(0, 0)
powpar <- 1
}
out[[ transtab[[i]][1] ]] <- param1
out[[ transtab[[i]][2] ]] <- param2
out[[ transtab[[i]][3] ]] <- length(param1)
if (origmat %in% c('thetavar', 'cov.xvar', 'psivar', 'phivar')) {
out[[ transtab[[i]][4] ]] <- powpar
}
} # mats
return(out)
}
# Check that priors match what is in the stan file
#
# @param lavpartable A lavaan partable with "priors" column
# @param mat The matrix for which we are obtaining priors
# @return nothing
check_priors <- function(lavpartable) {
right_pris <- sapply(dpriors(target = "stan"), function(x) strsplit(x, "[, ()]+")[[1]][1])
pt_pris <- sapply(lavpartable$prior[lavpartable$prior != ""], function(x) strsplit(x, "[, ()]+")[[1]][1])
names(pt_pris) <- lavpartable$mat[lavpartable$prior != ""]
primatch <- match(names(pt_pris), names(right_pris))
badpris <- which(pt_pris != right_pris[primatch])
if (length(badpris) > 0) {
badtxt <- unique(paste(names(pt_pris)[badpris], pt_pris[badpris]))
stop(paste0("blavaan ERROR: For target='stan', the following priors are not allowed:\n", paste(badtxt, collapse = "\n"), "\n See dpriors(target='stan') for each parameter's required distribution."))
}
}
#' Obtain data list for stanmarg.
stanmarg_data <- function(YX = NULL, S = NULL, YXo = NULL, N, Ng, grpnum, # data
miss, Np, Nobs, Obsvar, # missing
ord, Nord, ordidx, contidx, nlevs,
Noent, Nordobs, OrdObsvar, # ordinal
Xvar, Xdatvar, Xbetvar, Nx, Nx_between, # fixed.x
startrow, endrow, save_lvs = FALSE, do_test = TRUE,
Lambda_y_skeleton, # skeleton matrices
Lambda_x_skeleton, Gamma_skeleton, B_skeleton,
Theta_skeleton, Theta_r_skeleton,
Theta_x_skeleton, Theta_x_r_skeleton,
Psi_skeleton, Psi_r_skeleton, Phi_skeleton,
Phi_r_skeleton, Nu_skeleton, Alpha_skeleton, Tau_skeleton,
w1skel, w2skel, w3skel, # eq constraint matrices
w4skel, w5skel, w6skel, w7skel, w8skel,
w9skel, w10skel, w11skel, w12skel, w13skel,
w14skel, w15skel, emiter,
lam_y_sign, lam_x_sign, # sign constraint matrices
gam_sign, b_sign, psi_r_sign, fullpsi, phi_r_sign,
lavpartable = NULL, # for priors
dumlv = NULL, # for sampling lvs
wigind = NULL, # wiggle indicator
pri_only = FALSE, # prior predictive sampling
do_reg = FALSE, # regression sampling
multilev, mean_d, cov_w, log_lik_x, cov_d, nclus, cluster_size, # level 2 data
ncluster_sizes, mean_d_full, cov_d_full, log_lik_x_full, xbar_w, xbar_b,
cov_b, gs, cluster_sizes, cluster_size_ns, between_idx, N_between,
within_idx, N_within, both_idx, N_both, ov_idx1, ov_idx2, p_tilde, N_lev,
Lambda_y_skeleton_c = NULL, # level 2 matrices
B_skeleton_c = NULL, Theta_skeleton_c = NULL, Theta_r_skeleton_c = NULL,
Psi_skeleton_c = NULL, Psi_r_skeleton_c = NULL, Nu_skeleton_c = NULL,
Alpha_skeleton_c = NULL,
w1skel_c = NULL, w4skel_c = NULL, w5skel_c = NULL, w7skel_c = NULL,
w9skel_c = NULL, w10skel_c = NULL, w13skel_c = NULL, w14skel_c = NULL,
lam_y_sign_c = NULL, b_sign_c = NULL, psi_r_sign_c = NULL,
phi_r_sign_c = NULL, fullpsi_c = NULL, dumlv_c = NULL, wigind_c = NULL,
...) {
dat <- list()
dat$Ng <- Ng
dat$N <- array(N, length(N))
dat$Ntot <- sum(dat$N)
dat$grpnum <- grpnum
dat$missing <- miss
dat$Np <- Np
dat$Nobs <- array(Nobs, length(Nobs))
dat$Obsvar <- Obsvar
dat$ord <- ord
dat$Nord <- Nord
dat$Noent <- Noent
dat$Nordobs <- Nordobs
dat$OrdObsvar <- OrdObsvar
dat$ordidx <- ordidx
dat$contidx <- contidx
dat$nlevs <- nlevs
dat$startrow <- startrow
dat$endrow <- endrow
dat$Xvar <- Xvar
dat$Xdatvar <- Xdatvar
dat$Xbetvar <- Xbetvar
dat$Nx <- array(Nx, length(Nx))
dat$Nx_between <- array(Nx_between, length(Nx_between))
dat$emiter <- emiter
dat$do_reg <- do_reg
dat$pri_only <- pri_only
dat$multilev <- multilev
dat$YX <- YX
dat$YXo <- YXo
dat$use_suff <- 1L
if (ord | multilev) dat$use_suff <- 0L
dat$has_data <- 0L
dat$use_cov <- 0L
if (pri_only) {
dat$use_suff <- 0L
if (dim(Nu_skeleton)[2] == 0L) dat$use_cov <- 1L
tmparr <- array(dim = c(dat$Ng, ncol(YX) + 1, ncol(YX) + 1))
for (i in 1:Ng) {
tmparr[i,,] <- diag(nrow=ncol(YX) + 1)
}
dat$S <- tmparr
dat$YXbar <- array(0, dim = c(dat$Ng, ncol(YX)))
} else {
if (missing(YX)) {
dat$has_data <- 0L
if (is.null(S)) stop("S must be specified if YX is missing")
if (!is.list(S)) stop("S must be a list")
if (is.null(N)) stop("N must be specified if YX is missing")
if (miss) stop("blavaan ERROR: missingness requires raw data.")
## TODO if YXbar missing, set use_cov = 1 otherwise use_cov = 0 (but need new ppp)
dat$use_cov <- 1L
dat$S <- array(0, dim = c(dat$Ng, nrow(S[[1]]) + 1, nrow(S[[1]]) + 1))
for (i in 1:Ng) {
dat$S[i, 1:nrow(S[[1]]), 1:nrow(S[[1]])] <- S[[i]]
}
dat$YX <- array(0, dim = c(dat$Ntot, ncol(S[[1]])))
dat$YXo <- array(0, dim = c(dat$Ntot, ncol(YXo)))
dat$YXbar <- array(0, dim = c(dat$Ng, ncol(S[[1]])))
} else {
if (NROW(YX) != dat$Ntot) stop("blavaan ERROR: nrow(YX) != Ntot.")
dat$YXbar <- array(0, dim=c(Np, NCOL(YX)))
dat$S <- array(1, dim=c(Np, NCOL(YX) + 1, NCOL(YX) + 1))
dat$has_data <- 1L
if (dim(Nu_skeleton)[2] == 0L) dat$use_cov <- 1L
if (length(contidx) > 0) {
for (i in 1:dat$Np) {
tmpyxbar <- colMeans(YX[(startrow[i] : endrow[i]), , drop = FALSE])
tmpyxbar[is.na(tmpyxbar)] <- 0
dat$YXbar[i,] <- tmpyxbar
tmpN <- endrow[i] - startrow[i] + 1
if(tmpN > 1) {
tmpS <- cov(YX[(startrow[i] : endrow[i]), , drop = FALSE]) * (tmpN - 1) / tmpN
} else {
tmpS <- matrix(0, NCOL(YX), NCOL(YX))
}
dat$S[i,1:NCOL(YX),1:NCOL(YX)] <- tmpS
}
}
}
}
dat$save_lvs <- save_lvs
dat$do_test <- do_test
## level 2 data
dat$mean_d <- mean_d
dat$cov_w <- cov_w
dat$log_lik_x <- log_lik_x
dat$cov_d <- cov_d
dat$mean_d_full <- mean_d_full
dat$cov_d_full <- cov_d_full
dat$log_lik_x_full <- log_lik_x_full
dat$xbar_w <- xbar_w
dat$xbar_b <- xbar_b
dat$cov_b <- cov_b
dat$gs <- gs
dat$nclus <- nclus
dat$cluster_size <- cluster_size
dat$ncluster_sizes <- ncluster_sizes
dat$cluster_sizes <- cluster_sizes
dat$cluster_size_ns <- cluster_size_ns
dat$between_idx <- between_idx; dat$N_between <- N_between
dat$within_idx <- within_idx; dat$N_within <- N_within
dat$both_idx <- both_idx; dat$N_both <- N_both
dat$ov_idx1 <- ov_idx1; dat$ov_idx2 <- ov_idx2
dat$p_tilde <- p_tilde
dat$N_lev <- N_lev
## level 1 matrix info
dat <- c(dat, stanmarg_matdata(dat, Lambda_y_skeleton, Lambda_x_skeleton,
Gamma_skeleton, B_skeleton, Theta_skeleton,
Theta_r_skeleton, Theta_x_skeleton, Theta_x_r_skeleton,
Psi_skeleton, Psi_r_skeleton, Phi_skeleton, Phi_r_skeleton,
Nu_skeleton, Alpha_skeleton, Tau_skeleton, dumlv, level = 1L))
dat$lam_y_sign <- lam_y_sign
dat$w1skel <- w1skel
#dat$lam_x_sign <- lam_x_sign
dat$w3skel <- w3skel
dat$gam_sign <- gam_sign
dat$w4skel <- w4skel
dat$b_sign <- b_sign
dat$w5skel <- w5skel
dat$w6skel <- w6skel
dat$w7skel <- w7skel
dat$w8skel <- w8skel
dat$w9skel <- w9skel
dat$w10skel <- w10skel
dat$psi_r_sign <- psi_r_sign
dat$fullpsi <- fullpsi
#dat$w11skel <- w11skel
#dat$w12skel <- w12skel
#dat$phi_r_sign <- phi_r_sign
dat$w13skel <- w13skel
dat$w14skel <- w14skel
dat$w15skel <- w15skel
## level 2 matrices
dat <- c(dat, stanmarg_matdata(dat, Lambda_y_skeleton = Lambda_y_skeleton_c,
B_skeleton = B_skeleton_c, Theta_skeleton = Theta_skeleton_c,
Theta_r_skeleton = Theta_r_skeleton_c,
Psi_skeleton = Psi_skeleton_c, Psi_r_skeleton = Psi_r_skeleton_c,
Nu_skeleton = Nu_skeleton_c, Alpha_skeleton = Alpha_skeleton_c,
dumlv = dumlv_c, level = 2L))
dat$lam_y_sign_c <- lam_y_sign_c
dat$w1skel_c <- w1skel_c
dat$w4skel_c <- w4skel_c
dat$b_sign_c <- b_sign_c
dat$w5skel_c <- w5skel_c
dat$w7skel_c <- w7skel_c
dat$w9skel_c <- w9skel_c
dat$w10skel_c <- w10skel_c
dat$psi_r_sign_c <- psi_r_sign_c
dat$fullpsi_c <- fullpsi_c
dat$w13skel_c <- w13skel_c
dat$w14skel_c <- w14skel_c
## priors, first making sure they match what is in the stan file
check_priors(lavpartable)
dat$wigind <- wigind
dat$wigind_c <- wigind_c
if (!dat$multilev) {
dat <- c(dat, format_priors(lavpartable))
dat <- c(dat, format_priors(lavpartable[0,], level = 2L))
} else {
levlabs <- blav_partable_level_values(lavpartable)
dat <- c(dat, format_priors(lavpartable[lavpartable$level == levlabs[1],]))
dat <- c(dat, format_priors(lavpartable[lavpartable$level == levlabs[2],], level = 2L))
}
return(dat)
}
## create data on model matrices
stanmarg_matdata <- function(indat, Lambda_y_skeleton, Lambda_x_skeleton = NULL,
Gamma_skeleton = NULL, B_skeleton, Theta_skeleton,
Theta_r_skeleton, Theta_x_skeleton = NULL,
Theta_x_r_skeleton = NULL, Psi_skeleton,
Psi_r_skeleton, Phi_skeleton = NULL,
Phi_r_skeleton = NULL, Nu_skeleton, Alpha_skeleton,
Tau_skeleton = NULL, dumlv = NULL, level = 1L) {
dat <- list()
dat$p <- dim(Lambda_y_skeleton)[2]
dat$m <- dim(Lambda_y_skeleton)[3]
tmpres <- group_sparse_skeleton(Lambda_y_skeleton)
dat$len_w1 <- max(tmpres$g_len)
dat$w1 <- tmpres$w
dat$v1 <- tmpres$v
dat$u1 <- tmpres$u
dat$wg1 <- array(tmpres$g_len, length(tmpres$g_len))
if (level == 1L) {
dat$q <- dim(Lambda_x_skeleton)[2]
dat$n <- dim(Lambda_x_skeleton)[3]
#tmpres <- group_sparse_skeleton(Lambda_x_skeleton)
#dat$len_w2 <- max(tmpres$g_len)
#dat$w2 <- tmpres$w
#dat$v2 <- tmpres$v
#dat$u2 <- tmpres$u
#dat$wg2 <- array(tmpres$g_len, length(tmpres$g_len))
#dat$w2skel <- w2skel
tmpres <- group_sparse_skeleton(Gamma_skeleton)
dat$len_w3 <- max(tmpres$g_len)
dat$w3 <- tmpres$w
dat$v3 <- tmpres$v
dat$u3 <- tmpres$u
dat$wg3 <- array(tmpres$g_len, length(tmpres$g_len))
}
tmpres <- group_sparse_skeleton(B_skeleton)
dat$len_w4 <- max(tmpres$g_len)
dat$w4 <- tmpres$w
dat$v4 <- tmpres$v
dat$u4 <- tmpres$u
dat$wg4 <- array(tmpres$g_len, length(tmpres$g_len))
dThet <- Theta_skeleton
for (g in 1:indat$Ng) {
tmpmat <- as.matrix(dThet[g,,])
tmpmat[lower.tri(tmpmat)] <- tmpmat[upper.tri(tmpmat)] <- 0L
dThet[g,,] <- tmpmat
}
tmpres <- group_sparse_skeleton(dThet)
dat$len_w5 <- max(tmpres$g_len)
dat$w5 <- sqrt(tmpres$w) # because we do SRS in the model
dat$v5 <- tmpres$v
dat$u5 <- tmpres$u
dat$wg5 <- array(tmpres$g_len, length(tmpres$g_len))
## for lv sampling
usethet <- array(which(diag(as.matrix(Theta_skeleton[1,,])) != 0))
dat$w5use <- length(usethet)
dat$usethet <- usethet
if (level == 1L) {
dThetx <- Theta_x_skeleton
for (g in 1:indat$Ng) {
tmpmat <- as.matrix(dThetx[g,,])
tmpmat[lower.tri(tmpmat)] <- tmpmat[upper.tri(tmpmat)] <- 0L
dThetx[g,,] <- tmpmat
}
tmpres <- group_sparse_skeleton(dThetx)
dat$len_w6 <- max(tmpres$g_len)
dat$w6 <- tmpres$w
dat$v6 <- tmpres$v
dat$u6 <- tmpres$u
dat$wg6 <- array(tmpres$g_len, length(tmpres$g_len))
}
tmpres <- group_sparse_skeleton(Theta_r_skeleton)
dat$len_w7 <- max(tmpres$g_len)
dat$w7 <- tmpres$w
dat$v7 <- tmpres$v
dat$u7 <- tmpres$u
dat$wg7 <- array(tmpres$g_len, length(tmpres$g_len))
if (level == 1L) {
tmpres <- group_sparse_skeleton(Theta_x_r_skeleton)
dat$len_w8 <- max(tmpres$g_len)
dat$w8 <- tmpres$w
dat$v8 <- tmpres$v
dat$u8 <- tmpres$u
dat$wg8 <- array(tmpres$g_len, length(tmpres$g_len))
}
dPsi <- Psi_skeleton
for (g in 1:indat$Ng) {
tmpmat <- as.matrix(dPsi[g,,])
tmpmat[lower.tri(tmpmat)] <- tmpmat[upper.tri(tmpmat)] <- 0L
dPsi[g,,] <- tmpmat
}
tmpres <- group_sparse_skeleton(dPsi)
dat$len_w9 <- max(tmpres$g_len)
dat$w9 <- sqrt(tmpres$w) ## because we do SRS in the model
dat$v9 <- tmpres$v
dat$u9 <- tmpres$u
dat$wg9 <- array(tmpres$g_len, length(tmpres$g_len))
## for lv sampling
usepsi <- useorig <- array(which(diag(as.matrix(Psi_skeleton[1,,])) != 0))
if (length(dumlv) > 0) {
dums <- match(dumlv, usepsi)
usepsi <- array(usepsi[-dums[!is.na(dums)]])
}
dat$w9use <- length(usepsi)
dat$usepsi <- usepsi
dat$nopsi <- array((1:dim(Psi_skeleton)[2])[-useorig])
dat$w9no <- length(dat$nopsi)
tmpres <- group_sparse_skeleton(Psi_r_skeleton)
dat$len_w10 <- max(tmpres$g_len)
dat$w10 <- tmpres$w
dat$v10 <- tmpres$v
dat$u10 <- tmpres$u
dat$wg10 <- array(tmpres$g_len, length(tmpres$g_len))
if (level == 1L) {
dPhi <- Phi_skeleton
for (g in 1:indat$Ng) {
tmpmat <- as.matrix(dPhi[g,,])
tmpmat[lower.tri(tmpmat)] <- tmpmat[upper.tri(tmpmat)] <- 0L
dPhi[g,,] <- tmpmat
}
#tmpres <- group_sparse_skeleton(dPhi)
#dat$len_w11 <- max(tmpres$g_len)
#dat$w11 <- tmpres$w
#dat$v11 <- tmpres$v
#dat$u11 <- tmpres$u
#dat$wg11 <- array(tmpres$g_len, length(tmpres$g_len))
#tmpres <- group_sparse_skeleton(Phi_r_skeleton)
#dat$len_w12 <- max(tmpres$g_len)
#dat$w12 <- tmpres$w
#dat$v12 <- tmpres$v
#dat$u12 <- tmpres$u
#dat$wg12 <- array(tmpres$g_len, length(tmpres$g_len))
}
if (indat$has_data & is.null(Nu_skeleton)) stop("blavaan ERROR: Nu_skeleton not provided")
tmpres <- group_sparse_skeleton(Nu_skeleton)
dat$len_w13 <- max(tmpres$g_len)
dat$w13 <- tmpres$w
dat$v13 <- tmpres$v
dat$u13 <- tmpres$u
dat$wg13 <- array(tmpres$g_len, length(tmpres$g_len))
tmpres <- group_sparse_skeleton(Alpha_skeleton)
dat$len_w14 <- max(tmpres$g_len)
dat$w14 <- tmpres$w
dat$v14 <- tmpres$v
dat$u14 <- tmpres$u
dat$wg14 <- array(tmpres$g_len, length(tmpres$g_len))
if (level == 1L) {
tmpres <- group_sparse_skeleton(Tau_skeleton)
dat$len_w15 <- max(tmpres$g_len)
dat$w15 <- tmpres$w
dat$v15 <- tmpres$v
dat$u15 <- tmpres$u
dat$wg15 <- array(tmpres$g_len, length(tmpres$g_len))
}
## level 2 mats get "_c" suffix
if (level == 2L) names(dat) <- paste0(names(dat), "_c")
return(dat)
}
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Defines functions stanmarg_matdata stanmarg_data check_priors format_priors group_sparse_skeleton make_sparse_skeleton
# This code is based on code from the LERSIL package, authored by Ben Goodrich
# Convert a skeleton matrix in R to the internal format used by Stan
#
# @param skeleton A matrix that indicates the restrictions placed on
# its elements. If `NA`, then the element is unrestricted. If
# `Inf` or `-Inf`, the element is unrestricted but is constrained
# to be positive or negative respectively. Otherwise, the element is
# fixed to the specified number, which is often zero but can be any finite
# value.
# @return A list containing the sparse representation of the input matrix
make_sparse_skeleton <- function(skeleton) {
stopifnot(is.matrix(skeleton))
#skeleton[is.na(skeleton)] <- 1L
vals <- c(t(skeleton)) # vals needs to be in row-major order
## addresses change to Matrix clashing with extract_sparse_parts
spmat <- Matrix::Matrix(!(skeleton==0L), doDiag=FALSE, sparse=TRUE)
parts <- rstan::extract_sparse_parts(spmat) #is.na(skeleton))
parts$w <- as.array(vals[!(vals==0L)]) #is.na(vals)])
parts$v <- as.array(parts$v)
parts$u <- as.array(parts$u)
return(parts)
}
group_sparse_skeleton <- function(skeleton) {
stopifnot(length(dim(skeleton)) == 3)
Ng <- dim(skeleton)[1]
g_len <- u_len <- array(NA, Ng)
tmpu <- tmpw <- tmpv <- list()
for (g in 1:Ng) {
## removing first dimension and maintaining dims 2+3 is tricky:
parts <- make_sparse_skeleton(array(skeleton[g,,],
dim = dim(skeleton)[2:3]))
wlen <- length(parts$w)
g_len[g] <- wlen
u_len[g] <- length(parts$u)
tmpu <- c(tmpu, list(parts$u))
tmpw <- c(tmpw, list(parts$w))
tmpv <- c(tmpv, list(parts$v))
}
vdat <- wdat <- matrix(0, Ng, max(g_len))
udat <- matrix(0, Ng, max(u_len))
for (g in 1:Ng) {
if (g_len[g] > 0) {
vdat[g, 1:g_len[g]] <- tmpv[[g]]
wdat[g, 1:g_len[g]] <- tmpw[[g]]
}
if (u_len[g] > 0) {
udat[g, 1:u_len[g]] <- tmpu[[g]]
}
}
out <- list(g_len = g_len, v = vdat, w = wdat, u = udat)
return(out)
}
# Get prior parameters in a manner that Stan will like
#
# @param lavpartable A lavaan partable with "priors" column
# @param mat The matrix for which we are obtaining priors
# @return A list containing the prior parameters
format_priors <- function(lavpartable, level = 1L) {
## parameter matrices are filled in by row, so need to make
## sure we get parameters in the right order!
if ("group" %in% names(lavpartable)) {
lavpartable <- lavpartable[order(lavpartable$group, lavpartable$col, lavpartable$row),]
} else {
lavpartable <- lavpartable[order(lavpartable$col, lavpartable$row),]
}
transtab <- list(c('lambda_y_mn', 'lambda_y_sd', 'len_lam_y'),
c('lambda_x_mn', 'lambda_x_sd', 'len_lam_x'),
c('gamma_mn', 'gamma_sd', 'len_gam'),
c('b_mn', 'b_sd', 'len_b'),
c('theta_sd_shape', 'theta_sd_rate', 'len_thet_sd', 'theta_pow'),
c('theta_x_sd_shape', 'theta_x_sd_rate', 'len_thet_x_sd', 'theta_x_pow'),
c('theta_r_alpha', 'theta_r_beta', 'len_thet_r'),
c('theta_x_r_alpha', 'theta_x_r_beta', 'len_thet_x_r'),
c('psi_sd_shape', 'psi_sd_rate', 'len_psi_sd', 'psi_pow'),
c('psi_r_alpha', 'psi_r_beta', 'len_psi_r'),
c('phi_sd_shape', 'phi_sd_rate', 'len_phi_sd', 'phi_pow'),
c('phi_r_alpha', 'phi_r_beta', 'len_phi_r'),
c('nu_mn', 'nu_sd', 'len_nu'),
c('alpha_mn', 'alpha_sd', 'len_alph'),
c('tau_mn', 'tau_sd', 'len_tau'))
mats <- c('lambda', 'lambda_x', 'gamma', 'beta', 'thetavar', 'cov.xvar', 'thetaoff',
'cov.xoff', 'psivar', 'psioff', 'phivar', 'phioff', 'nu', 'alpha', 'tau')
if (level == 2L) {
newmats <- c('lambda', 'beta', 'thetavar', 'thetaoff', 'psivar', 'psioff', 'nu', 'alpha')
subloc <- match(newmats, mats)
mats <- newmats
transtab <- transtab[subloc]
transtab <- lapply(transtab, function(x) paste0(x, '_c'))
}
out <- list()
for (i in 1:length(mats)) {
mat <- origmat <- mats[i]
if (grepl("var", mat)) {
mat <- gsub("var", "", mat)
prisel <- lavpartable$row == lavpartable$col
} else if (grepl("psioff", mat)) {
mat <- "lvrho"
prisel <- lavpartable$row != lavpartable$col
} else if (grepl("thetaoff", mat)) {
mat <- "rho"
prisel <- lavpartable$row != lavpartable$col
} else {
prisel <- rep(TRUE, length(lavpartable$row))
}
if (mat == "nu") {
prisel <- prisel & (lavpartable$mat %in% c(mat, "mean.x"))
} else {
prisel <- prisel & (lavpartable$mat == mat)
}
prisel <- prisel & (lavpartable$free > 0)
thepris <- lavpartable$prior[prisel]
if (length(thepris) > 0) {
textpris <- thepris[thepris != ""]
prisplit <- strsplit(textpris, "[, ()]+")
param1 <- sapply(prisplit, function(x) x[2])
if (!grepl("\\[", prisplit[[1]][3])) {
param2 <- sapply(prisplit, function(x) x[3])
} else {
param2 <- rep(NA, length(param1))
}
## check that var/sd/prec is same for all
powargs <- sapply(prisplit, tail, 1)
if (any(grepl("\\[", powargs))) {
if (length(unique(powargs)) > 1) stop(paste0("blavaan ERROR: In matrix ", mat, ", all priors must be placed on either vars, sds, or precisions."))
}
powpar <- 1
powarg <- powargs[1]
if (grepl("\\[var\\]", powarg)) {
powpar <- 2
} else if (!grepl("\\[sd\\]", powarg)) {
powpar <- -2
}
param1 <- array(as.numeric(param1), length(param1))
param2 <- array(as.numeric(param2), length(param2))
} else {
param1 <- array(0, 0)
param2 <- array(0, 0)
powpar <- 1
}
out[[ transtab[[i]][1] ]] <- param1
out[[ transtab[[i]][2] ]] <- param2
out[[ transtab[[i]][3] ]] <- length(param1)
if (origmat %in% c('thetavar', 'cov.xvar', 'psivar', 'phivar')) {
out[[ transtab[[i]][4] ]] <- powpar
}
} # mats
return(out)
}
# Check that priors match what is in the stan file
#
# @param lavpartable A lavaan partable with "priors" column
# @param mat The matrix for which we are obtaining priors
# @return nothing
check_priors <- function(lavpartable) {
right_pris <- sapply(dpriors(target = "stan"), function(x) strsplit(x, "[, ()]+")[[1]][1])
pt_pris <- sapply(lavpartable$prior[lavpartable$prior != ""], function(x) strsplit(x, "[, ()]+")[[1]][1])
names(pt_pris) <- lavpartable$mat[lavpartable$prior != ""]
primatch <- match(names(pt_pris), names(right_pris))
badpris <- which(pt_pris != right_pris[primatch])
if (length(badpris) > 0) {
badtxt <- unique(paste(names(pt_pris)[badpris], pt_pris[badpris]))
stop(paste0("blavaan ERROR: For target='stan', the following priors are not allowed:\n", paste(badtxt, collapse = "\n"), "\n See dpriors(target='stan') for each parameter's required distribution."))
}
}
#' Obtain data list for stanmarg.
stanmarg_data <- function(YX = NULL, S = NULL, YXo = NULL, N, Ng, grpnum, # data
miss, Np, Nobs, Obsvar, # missing
ord, Nord, ordidx, contidx, nlevs,
Noent, Nordobs, OrdObsvar, # ordinal
Xvar, Xdatvar, Xbetvar, Nx, Nx_between, # fixed.x
startrow, endrow, save_lvs = FALSE, do_test = TRUE,
Lambda_y_skeleton, # skeleton matrices
Lambda_x_skeleton, Gamma_skeleton, B_skeleton,
Theta_skeleton, Theta_r_skeleton,
Theta_x_skeleton, Theta_x_r_skeleton,
Psi_skeleton, Psi_r_skeleton, Phi_skeleton,
Phi_r_skeleton, Nu_skeleton, Alpha_skeleton, Tau_skeleton,
w1skel, w2skel, w3skel, # eq constraint matrices
w4skel, w5skel, w6skel, w7skel, w8skel,
w9skel, w10skel, w11skel, w12skel, w13skel,
w14skel, w15skel, emiter,
lam_y_sign, lam_x_sign, # sign constraint matrices
gam_sign, b_sign, psi_r_sign, fullpsi, phi_r_sign,
lavpartable = NULL, # for priors
dumlv = NULL, # for sampling lvs
wigind = NULL, # wiggle indicator
pri_only = FALSE, # prior predictive sampling
do_reg = FALSE, # regression sampling
multilev, mean_d, cov_w, log_lik_x, cov_d, nclus, cluster_size, # level 2 data
ncluster_sizes, mean_d_full, cov_d_full, log_lik_x_full, xbar_w, xbar_b,
cov_b, gs, cluster_sizes, cluster_size_ns, between_idx, N_between,
within_idx, N_within, both_idx, N_both, ov_idx1, ov_idx2, p_tilde, N_lev,
Lambda_y_skeleton_c = NULL, # level 2 matrices
B_skeleton_c = NULL, Theta_skeleton_c = NULL, Theta_r_skeleton_c = NULL,
Psi_skeleton_c = NULL, Psi_r_skeleton_c = NULL, Nu_skeleton_c = NULL,
Alpha_skeleton_c = NULL,
w1skel_c = NULL, w4skel_c = NULL, w5skel_c = NULL, w7skel_c = NULL,
w9skel_c = NULL, w10skel_c = NULL, w13skel_c = NULL, w14skel_c = NULL,
lam_y_sign_c = NULL, b_sign_c = NULL, psi_r_sign_c = NULL,
phi_r_sign_c = NULL, fullpsi_c = NULL, dumlv_c = NULL, wigind_c = NULL,
...) {
dat <- list()
dat$Ng <- Ng
dat$N <- array(N, length(N))
dat$Ntot <- sum(dat$N)
dat$grpnum <- grpnum
dat$missing <- miss
dat$Np <- Np
dat$Nobs <- array(Nobs, length(Nobs))
dat$Obsvar <- Obsvar
dat$ord <- ord
dat$Nord <- Nord
dat$Noent <- Noent
dat$Nordobs <- Nordobs
dat$OrdObsvar <- OrdObsvar
dat$ordidx <- ordidx
dat$contidx <- contidx
dat$nlevs <- nlevs
dat$startrow <- startrow
dat$endrow <- endrow
dat$Xvar <- Xvar
dat$Xdatvar <- Xdatvar
dat$Xbetvar <- Xbetvar
dat$Nx <- array(Nx, length(Nx))
dat$Nx_between <- array(Nx_between, length(Nx_between))
dat$emiter <- emiter
dat$do_reg <- do_reg
dat$pri_only <- pri_only
dat$multilev <- multilev
dat$YX <- YX
dat$YXo <- YXo
dat$use_suff <- 1L
if (ord | multilev) dat$use_suff <- 0L
dat$has_data <- 0L
dat$use_cov <- 0L
if (pri_only) {
dat$use_suff <- 0L
if (dim(Nu_skeleton)[2] == 0L) dat$use_cov <- 1L
tmparr <- array(dim = c(dat$Ng, ncol(YX) + 1, ncol(YX) + 1))
for (i in 1:Ng) {
tmparr[i,,] <- diag(nrow=ncol(YX) + 1)
}
dat$S <- tmparr
dat$YXbar <- array(0, dim = c(dat$Ng, ncol(YX)))
} else {
if (missing(YX)) {
dat$has_data <- 0L
if (is.null(S)) stop("S must be specified if YX is missing")
if (!is.list(S)) stop("S must be a list")
if (is.null(N)) stop("N must be specified if YX is missing")
if (miss) stop("blavaan ERROR: missingness requires raw data.")
## TODO if YXbar missing, set use_cov = 1 otherwise use_cov = 0 (but need new ppp)
dat$use_cov <- 1L
dat$S <- array(0, dim = c(dat$Ng, nrow(S[[1]]) + 1, nrow(S[[1]]) + 1))
for (i in 1:Ng) {
dat$S[i, 1:nrow(S[[1]]), 1:nrow(S[[1]])] <- S[[i]]
}
dat$YX <- array(0, dim = c(dat$Ntot, ncol(S[[1]])))
dat$YXo <- array(0, dim = c(dat$Ntot, ncol(YXo)))
dat$YXbar <- array(0, dim = c(dat$Ng, ncol(S[[1]])))
} else {
if (NROW(YX) != dat$Ntot) stop("blavaan ERROR: nrow(YX) != Ntot.")
dat$YXbar <- array(0, dim=c(Np, NCOL(YX)))
dat$S <- array(1, dim=c(Np, NCOL(YX) + 1, NCOL(YX) + 1))
dat$has_data <- 1L
if (dim(Nu_skeleton)[2] == 0L) dat$use_cov <- 1L
if (length(contidx) > 0) {
for (i in 1:dat$Np) {
tmpyxbar <- colMeans(YX[(startrow[i] : endrow[i]), , drop = FALSE])
tmpyxbar[is.na(tmpyxbar)] <- 0
dat$YXbar[i,] <- tmpyxbar
tmpN <- endrow[i] - startrow[i] + 1
if(tmpN > 1) {
tmpS <- cov(YX[(startrow[i] : endrow[i]), , drop = FALSE]) * (tmpN - 1) / tmpN
} else {
tmpS <- matrix(0, NCOL(YX), NCOL(YX))
}
dat$S[i,1:NCOL(YX),1:NCOL(YX)] <- tmpS
}
}
}
}
dat$save_lvs <- save_lvs
dat$do_test <- do_test
## level 2 data
dat$mean_d <- mean_d
dat$cov_w <- cov_w
dat$log_lik_x <- log_lik_x
dat$cov_d <- cov_d
dat$mean_d_full <- mean_d_full
dat$cov_d_full <- cov_d_full
dat$log_lik_x_full <- log_lik_x_full
dat$xbar_w <- xbar_w
dat$xbar_b <- xbar_b
dat$cov_b <- cov_b
dat$gs <- gs
dat$nclus <- nclus
dat$cluster_size <- cluster_size
dat$ncluster_sizes <- ncluster_sizes
dat$cluster_sizes <- cluster_sizes
dat$cluster_size_ns <- cluster_size_ns
dat$between_idx <- between_idx; dat$N_between <- N_between
dat$within_idx <- within_idx; dat$N_within <- N_within
dat$both_idx <- both_idx; dat$N_both <- N_both
dat$ov_idx1 <- ov_idx1; dat$ov_idx2 <- ov_idx2
dat$p_tilde <- p_tilde
dat$N_lev <- N_lev
## level 1 matrix info
dat <- c(dat, stanmarg_matdata(dat, Lambda_y_skeleton, Lambda_x_skeleton,
Gamma_skeleton, B_skeleton, Theta_skeleton,
Theta_r_skeleton, Theta_x_skeleton, Theta_x_r_skeleton,
Psi_skeleton, Psi_r_skeleton, Phi_skeleton, Phi_r_skeleton,
Nu_skeleton, Alpha_skeleton, Tau_skeleton, dumlv, level = 1L))
dat$lam_y_sign <- lam_y_sign
dat$w1skel <- w1skel
#dat$lam_x_sign <- lam_x_sign
dat$w3skel <- w3skel
dat$gam_sign <- gam_sign
dat$w4skel <- w4skel
dat$b_sign <- b_sign
dat$w5skel <- w5skel
dat$w6skel <- w6skel
dat$w7skel <- w7skel
dat$w8skel <- w8skel
dat$w9skel <- w9skel
dat$w10skel <- w10skel
dat$psi_r_sign <- psi_r_sign
dat$fullpsi <- fullpsi
#dat$w11skel <- w11skel
#dat$w12skel <- w12skel
#dat$phi_r_sign <- phi_r_sign
dat$w13skel <- w13skel
dat$w14skel <- w14skel
dat$w15skel <- w15skel
## level 2 matrices
dat <- c(dat, stanmarg_matdata(dat, Lambda_y_skeleton = Lambda_y_skeleton_c,
B_skeleton = B_skeleton_c, Theta_skeleton = Theta_skeleton_c,
Theta_r_skeleton = Theta_r_skeleton_c,
Psi_skeleton = Psi_skeleton_c, Psi_r_skeleton = Psi_r_skeleton_c,
Nu_skeleton = Nu_skeleton_c, Alpha_skeleton = Alpha_skeleton_c,
dumlv = dumlv_c, level = 2L))
dat$lam_y_sign_c <- lam_y_sign_c
dat$w1skel_c <- w1skel_c
dat$w4skel_c <- w4skel_c
dat$b_sign_c <- b_sign_c
dat$w5skel_c <- w5skel_c
dat$w7skel_c <- w7skel_c
dat$w9skel_c <- w9skel_c
dat$w10skel_c <- w10skel_c
dat$psi_r_sign_c <- psi_r_sign_c
dat$fullpsi_c <- fullpsi_c
dat$w13skel_c <- w13skel_c
dat$w14skel_c <- w14skel_c
## priors, first making sure they match what is in the stan file
check_priors(lavpartable)
dat$wigind <- wigind
dat$wigind_c <- wigind_c
if (!dat$multilev) {
dat <- c(dat, format_priors(lavpartable))
dat <- c(dat, format_priors(lavpartable[0,], level = 2L))
} else {
levlabs <- blav_partable_level_values(lavpartable)
dat <- c(dat, format_priors(lavpartable[lavpartable$level == levlabs[1],]))
dat <- c(dat, format_priors(lavpartable[lavpartable$level == levlabs[2],], level = 2L))
}
return(dat)
}
## create data on model matrices
stanmarg_matdata <- function(indat, Lambda_y_skeleton, Lambda_x_skeleton = NULL,
Gamma_skeleton = NULL, B_skeleton, Theta_skeleton,
Theta_r_skeleton, Theta_x_skeleton = NULL,
Theta_x_r_skeleton = NULL, Psi_skeleton,
Psi_r_skeleton, Phi_skeleton = NULL,
Phi_r_skeleton = NULL, Nu_skeleton, Alpha_skeleton,
Tau_skeleton = NULL, dumlv = NULL, level = 1L) {
dat <- list()
dat$p <- dim(Lambda_y_skeleton)[2]
dat$m <- dim(Lambda_y_skeleton)[3]
tmpres <- group_sparse_skeleton(Lambda_y_skeleton)
dat$len_w1 <- max(tmpres$g_len)
dat$w1 <- tmpres$w
dat$v1 <- tmpres$v
dat$u1 <- tmpres$u
dat$wg1 <- array(tmpres$g_len, length(tmpres$g_len))
if (level == 1L) {
dat$q <- dim(Lambda_x_skeleton)[2]
dat$n <- dim(Lambda_x_skeleton)[3]
#tmpres <- group_sparse_skeleton(Lambda_x_skeleton)
#dat$len_w2 <- max(tmpres$g_len)
#dat$w2 <- tmpres$w
#dat$v2 <- tmpres$v
#dat$u2 <- tmpres$u
#dat$wg2 <- array(tmpres$g_len, length(tmpres$g_len))
#dat$w2skel <- w2skel
tmpres <- group_sparse_skeleton(Gamma_skeleton)
dat$len_w3 <- max(tmpres$g_len)
dat$w3 <- tmpres$w
dat$v3 <- tmpres$v
dat$u3 <- tmpres$u
dat$wg3 <- array(tmpres$g_len, length(tmpres$g_len))
}
tmpres <- group_sparse_skeleton(B_skeleton)
dat$len_w4 <- max(tmpres$g_len)
dat$w4 <- tmpres$w
dat$v4 <- tmpres$v
dat$u4 <- tmpres$u
dat$wg4 <- array(tmpres$g_len, length(tmpres$g_len))
dThet <- Theta_skeleton
for (g in 1:indat$Ng) {
tmpmat <- as.matrix(dThet[g,,])
tmpmat[lower.tri(tmpmat)] <- tmpmat[upper.tri(tmpmat)] <- 0L
dThet[g,,] <- tmpmat
}
tmpres <- group_sparse_skeleton(dThet)
dat$len_w5 <- max(tmpres$g_len)
dat$w5 <- sqrt(tmpres$w) # because we do SRS in the model
dat$v5 <- tmpres$v
dat$u5 <- tmpres$u
dat$wg5 <- array(tmpres$g_len, length(tmpres$g_len))
## for lv sampling
usethet <- array(which(diag(as.matrix(Theta_skeleton[1,,])) != 0))
dat$w5use <- length(usethet)
dat$usethet <- usethet
if (level == 1L) {
dThetx <- Theta_x_skeleton
for (g in 1:indat$Ng) {
tmpmat <- as.matrix(dThetx[g,,])
tmpmat[lower.tri(tmpmat)] <- tmpmat[upper.tri(tmpmat)] <- 0L
dThetx[g,,] <- tmpmat
}
tmpres <- group_sparse_skeleton(dThetx)
dat$len_w6 <- max(tmpres$g_len)
dat$w6 <- tmpres$w
dat$v6 <- tmpres$v
dat$u6 <- tmpres$u
dat$wg6 <- array(tmpres$g_len, length(tmpres$g_len))
}
tmpres <- group_sparse_skeleton(Theta_r_skeleton)
dat$len_w7 <- max(tmpres$g_len)
dat$w7 <- tmpres$w
dat$v7 <- tmpres$v
dat$u7 <- tmpres$u
dat$wg7 <- array(tmpres$g_len, length(tmpres$g_len))
if (level == 1L) {
tmpres <- group_sparse_skeleton(Theta_x_r_skeleton)
dat$len_w8 <- max(tmpres$g_len)
dat$w8 <- tmpres$w
dat$v8 <- tmpres$v
dat$u8 <- tmpres$u
dat$wg8 <- array(tmpres$g_len, length(tmpres$g_len))
}
dPsi <- Psi_skeleton
for (g in 1:indat$Ng) {
tmpmat <- as.matrix(dPsi[g,,])
tmpmat[lower.tri(tmpmat)] <- tmpmat[upper.tri(tmpmat)] <- 0L
dPsi[g,,] <- tmpmat
}
tmpres <- group_sparse_skeleton(dPsi)
dat$len_w9 <- max(tmpres$g_len)
dat$w9 <- sqrt(tmpres$w) ## because we do SRS in the model
dat$v9 <- tmpres$v
dat$u9 <- tmpres$u
dat$wg9 <- array(tmpres$g_len, length(tmpres$g_len))
## for lv sampling
usepsi <- useorig <- array(which(diag(as.matrix(Psi_skeleton[1,,])) != 0))
if (length(dumlv) > 0) {
dums <- match(dumlv, usepsi)
usepsi <- array(usepsi[-dums[!is.na(dums)]])
}
dat$w9use <- length(usepsi)
dat$usepsi <- usepsi
dat$nopsi <- array((1:dim(Psi_skeleton)[2])[-useorig])
dat$w9no <- length(dat$nopsi)
tmpres <- group_sparse_skeleton(Psi_r_skeleton)
dat$len_w10 <- max(tmpres$g_len)
dat$w10 <- tmpres$w
dat$v10 <- tmpres$v
dat$u10 <- tmpres$u
dat$wg10 <- array(tmpres$g_len, length(tmpres$g_len))
if (level == 1L) {
dPhi <- Phi_skeleton
for (g in 1:indat$Ng) {
tmpmat <- as.matrix(dPhi[g,,])
tmpmat[lower.tri(tmpmat)] <- tmpmat[upper.tri(tmpmat)] <- 0L
dPhi[g,,] <- tmpmat
}
#tmpres <- group_sparse_skeleton(dPhi)
#dat$len_w11 <- max(tmpres$g_len)
#dat$w11 <- tmpres$w
#dat$v11 <- tmpres$v
#dat$u11 <- tmpres$u
#dat$wg11 <- array(tmpres$g_len, length(tmpres$g_len))
#tmpres <- group_sparse_skeleton(Phi_r_skeleton)
#dat$len_w12 <- max(tmpres$g_len)
#dat$w12 <- tmpres$w
#dat$v12 <- tmpres$v
#dat$u12 <- tmpres$u
#dat$wg12 <- array(tmpres$g_len, length(tmpres$g_len))
}
if (indat$has_data & is.null(Nu_skeleton)) stop("blavaan ERROR: Nu_skeleton not provided")
tmpres <- group_sparse_skeleton(Nu_skeleton)
dat$len_w13 <- max(tmpres$g_len)
dat$w13 <- tmpres$w
dat$v13 <- tmpres$v
dat$u13 <- tmpres$u
dat$wg13 <- array(tmpres$g_len, length(tmpres$g_len))
tmpres <- group_sparse_skeleton(Alpha_skeleton)
dat$len_w14 <- max(tmpres$g_len)
dat$w14 <- tmpres$w
dat$v14 <- tmpres$v
dat$u14 <- tmpres$u
dat$wg14 <- array(tmpres$g_len, length(tmpres$g_len))
if (level == 1L) {
tmpres <- group_sparse_skeleton(Tau_skeleton)
dat$len_w15 <- max(tmpres$g_len)
dat$w15 <- tmpres$w
dat$v15 <- tmpres$v
dat$u15 <- tmpres$u
dat$wg15 <- array(tmpres$g_len, length(tmpres$g_len))
}
## level 2 mats get "_c" suffix
if (level == 2L) names(dat) <- paste0(names(dat), "_c")
return(dat)
}
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