Nothing
R/lav_start.R
In lavaan: Latent Variable Analysis
Defines functions
lav_start_check_cov
lav_start
# lav_start.R: provide starting values for model parameters
#
# YR 30/11/2010: initial version
# YR 08/06/2011: add fabin3 start values for factor loadings
# YR 14 Jan 2014: moved to lav_start.R
# fill in the 'ustart' column in a User data.frame with reasonable
# starting values, using the sample data
lav_start <- function(start.method = "default",
lavpartable = NULL,
lavsamplestats = NULL,
lavh1 = NULL, # fixme: only use lavh1?
model.type = "sem",
mimic = "lavaan",
reflect = FALSE, # rotation only
samplestats.flag = TRUE,
order.lv.by = "none" # rotation only
) {
# check arguments
stopifnot(is.list(lavpartable))
# categorical?
categorical <- any(lavpartable$op == "|")
# correlation structure?
correlation <- any(lavpartable$op == "~*~")
# conditional.x?
conditional.x <- any(lavpartable$exo == 1L &
lavpartable$op %in% c("~", "<~"))
# ord.names <- unique(lavpartable$lhs[ lavpartable$op == "|" ])
# nlevels?
nlevels <- lav_partable_nlevels(lavpartable)
# reflect/order.lv.by
if (is.null(reflect)) {
reflect <- FALSE
}
if (is.null(order.lv.by)) {
order.lv.by <- "index"
}
# check start.method
if (mimic == "lavaan") {
start.initial <- "lavaan"
} else if (mimic == "Mplus") {
start.initial <- "mplus"
} else {
# FIXME: use LISREL/EQS/AMOS/.... schemes
start.initial <- "lavaan"
}
# start.method
start.user <- NULL
if (is.character(start.method)) {
start.method.lc <- tolower(start.method)
if (start.method.lc != "simple" && !samplestats.flag) {
start.method.lc <- start.method <- "simple"
}
if (start.method.lc == "default") {
# nothing to do
} else if (start.method == "simple") {
start <- numeric(length(lavpartable$ustart))
# if(categorical || correlation) {
start[which(lavpartable$op == "=~")] <- 0.7
# } else {
# start[ which(lavpartable$op == "=~") ] <- 1.0
# }
start[which(lavpartable$op == "~*~")] <- 1.0
ov.names.ord <- vnames(lavpartable, "ov.ord")
var.idx <- which(lavpartable$op == "~~" &
lavpartable$lhs == lavpartable$rhs &
!(lavpartable$lhs %in% ov.names.ord))
start[var.idx] <- 1.0
user.idx <- which(!is.na(lavpartable$ustart))
start[user.idx] <- lavpartable$ustart[user.idx]
return(start) # assuming fixed.x = FALSE!
} else if (start.method == "est") {
return(lavpartable$est)
} else if (start.method.lc %in% c("simple", "lavaan", "mplus")) {
start.initial <- start.method.lc
} else {
lav_msg_stop(gettext("unknown value for start argument"))
}
} else if (is.list(start.method)) {
start.user <- start.method
} else if (is.numeric(start.method)) {
nx.free <- sum(lavpartable$free > 0L)
if (length(start.method) != nx.free) {
lav_msg_stop(gettextf(
"start argument contains %1$s elements; but parameter table
expects %2$s free parameters.", length(start.method), nx.free))
}
lavpartable$ustart[lavpartable$free > 0L] <- start.method
} else if (inherits(start.method, "lavaan")) {
start.user <- parTable(start.method)
}
# check model list elements, if provided
if (!is.null(start.user)) {
if (is.null(start.user$lhs) ||
is.null(start.user$op) ||
is.null(start.user$rhs)) {
lav_msg_stop(gettext(
"problem with start argument: model list does not contain
all elements: lhs/op/rhs"))
}
if (!is.null(start.user$est)) {
# excellent, we got an est column; nothing to do
} else if (!is.null(start.user$start)) {
# no est column, but we use the start column
start.user$est <- start.user$start
} else if (!is.null(start.user$ustart)) {
# no ideal, but better than nothing
start.user$est <- start.user$ustart
} else {
lav_msg_stop(gettext(
"problem with start argument: could not find est/start column
in model list"))
}
}
# global settings
# 0. everyting is zero
start <- numeric(length(lavpartable$ustart))
# 1. =~ factor loadings:
if (categorical || correlation) {
# if std.lv=TRUE, 0.8 is too large
start[which(lavpartable$op == "=~")] <- 0.7
} else {
start[which(lavpartable$op == "=~")] <- 1.0
}
# 2. (residual) lv variances for latent variables
lv.names <- vnames(lavpartable, "lv") # all groups
lv.var.idx <- which(lavpartable$op == "~~" &
lavpartable$lhs %in% lv.names &
lavpartable$lhs == lavpartable$rhs)
start[lv.var.idx] <- 0.05
# start[lv.var.idx] <- 0.5 # new in 0.6-2? (for optim.parscale = "stand")
# 3. latent response scales (if any)
delta.idx <- which(lavpartable$op == "~*~")
start[delta.idx] <- 1.0
# group-specific settings
ngroups <- lav_partable_ngroups(lavpartable)
# for now, if no group column, add one (again), until we rewrite
# this function to handle block/group hybrid settings
if (is.null(lavpartable$group) && ngroups == 1L) {
lavpartable$group <- rep(1L, length(lavpartable$lhs))
lavpartable$group[lavpartable$block == 0L] <- 0L
}
for (g in 1:ngroups) {
# group values (not necessarily 1,2,... anymore)
group.values <- lav_partable_group_values(lavpartable)
# info from user model for this group
if (conditional.x) {
ov.names <- vnames(lavpartable, "ov.nox", group = group.values[g])
} else {
ov.names <- vnames(lavpartable, "ov", group = group.values[g])
}
if (categorical) {
ov.names.num <- vnames(lavpartable, "ov.num", group = group.values[g])
ov.names.ord <- vnames(lavpartable, "ov.ord", group = group.values[g])
} else {
ov.names.num <- ov.names
}
lv.names <- vnames(lavpartable, "lv", group = group.values[g])
lv.names.efa <- vnames(lavpartable, "lv.efa", group = group.values[g])
ov.names.x <- vnames(lavpartable, "ov.x", group = group.values[g])
# just for the nlevels >1 case
ov.names <- unique(unlist(ov.names))
ov.names.num <- unique(unlist(ov.names.num))
lv.names <- unique(unlist(lv.names))
lv.names.efa <- unique(unlist(lv.names.efa))
ov.names.x <- unique(unlist(ov.names.x))
# residual ov variances (including exo/ind, to be overriden)
ov.var.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~~" &
lavpartable$lhs %in% ov.names.num &
lavpartable$lhs == lavpartable$rhs)
sample.var.idx <- match(lavpartable$lhs[ov.var.idx], ov.names)
if (model.type == "unrestricted") {
# this does not work if conditional.x = TRUE...
if (!is.null(lavsamplestats@missing.h1[[g]])) {
start[ov.var.idx] <-
diag(lavsamplestats@missing.h1[[g]]$sigma)[sample.var.idx]
} else {
start[ov.var.idx] <-
diag(lavsamplestats@cov[[g]])[sample.var.idx]
}
} else {
if (start.initial == "mplus") {
if (conditional.x && nlevels == 1L) {
start[ov.var.idx] <-
(1.0 - 0.50) * lavsamplestats@res.var[[1L]][sample.var.idx]
} else {
start[ov.var.idx] <-
(1.0 - 0.50) * lavsamplestats@var[[1L]][sample.var.idx]
}
} else {
if (conditional.x && nlevels == 1L) {
start[ov.var.idx] <-
(1.0 - 0.50) * diag(lavsamplestats@res.cov[[g]])[sample.var.idx]
} else {
start[ov.var.idx] <-
(1.0 - 0.50) * diag(lavsamplestats@cov[[g]])[sample.var.idx]
}
}
}
# 1-fac measurement models: loadings, psi, theta
if (start.initial %in% c("lavaan", "mplus") &&
model.type %in% c("sem", "cfa")) {
# fabin3 estimator (2sls) of Hagglund (1982) per factor
for (f in lv.names) {
# not for efa factors
if (f %in% lv.names.efa) {
next
}
lambda.idx <- which(lavpartable$lhs == f &
lavpartable$op == "=~" &
lavpartable$group == group.values[g])
# standardized?
std.lv <- FALSE
var.f.idx <- which(lavpartable$lhs == f &
lavpartable$op == "~~" &
lavpartable$group == group.values[g] &
lavpartable$rhs == f)
if (length(var.f.idx) > 0L &&
all(lavpartable$free[var.f.idx] == 0) &&
all(lavpartable$ustart[var.f.idx] == 1)) {
std.lv <- TRUE
}
# no second order
if (any(lavpartable$rhs[lambda.idx] %in% lv.names)) next
# get observed indicators for this latent variable
ov.idx <- match(lavpartable$rhs[lambda.idx], ov.names)
if (length(ov.idx) > 0L && !any(is.na(ov.idx))) {
if (lavsamplestats@missing.flag && nlevels == 1L) {
COV <- lavsamplestats@missing.h1[[g]]$sigma[ov.idx,
ov.idx,
drop = FALSE
]
} else {
if (conditional.x && nlevels == 1L) {
COV <- lavsamplestats@res.cov[[g]][ov.idx,
ov.idx,
drop = FALSE
]
} else {
COV <- lavsamplestats@cov[[g]][ov.idx,
ov.idx,
drop = FALSE
]
}
}
# fabin for 1-factor
fabin <- lav_cfa_1fac_fabin(COV,
std.lv = std.lv,
lambda.only = TRUE,
method = "fabin3"
)
# factor loadings
tmp <- fabin$lambda
tmp[!is.finite(tmp)] <- 1.0 # just in case (eg 0/0)
# check for negative triad if nvar=3L (new in 0.6-8)
if (!is.null(fabin$neg.triad) && fabin$neg.triad) {
if (std.lv) {
tmp <- rep(0.7, length(tmp))
} else {
tmp <- rep(1.0, length(tmp))
}
}
start[lambda.idx] <- tmp
# factor variance
# if(!std.lv) {
# start[var.f.idx] <- fabin$psi
# # if residual var, make smaller
# y.idx <- which(lavpartable$lhs == f &
# lavpartable$group == group.values[g] &
# lavpartable$op == "~")
# if(length(y.idx) > 0L) {
# # how much explained variance do we expect?
# # we take 0.50
# start[var.f.idx] <- 0.5 * start[var.f.idx]
# }
# # no negative variances (we get these if we have an
# # inconsistent triad (eg, covariance signs are +,+,-)
# if(start[var.f.idx] < 0) {
# start[var.f.idx] <- 0.05
# }
# }
# NOTE: fabin (sometimes) gives residual variances
# that are larger than the original variances...
# residual variances -- order?
# res.idx <- which(lavpartable$lhs %in% ov.names[ov.idx] &
# lavpartable$op == "~~" &
# lavpartable$group == group.values[g] &
# lavpartable$rhs == lavpartable$lhs)
# start[res.idx] <- fabin$theta
# negative variances?
# neg.idx <- which(start[res.idx] < 0)
# if(length(neg.idx) > 0L) {
# start[res.idx][neg.idx] <- 0.05
# }
}
} # fabin3
# efa?
nefa <- lav_partable_nefa(lavpartable)
if (nefa > 0L) {
efa.values <- lav_partable_efa_values(lavpartable)
for (set in seq_len(nefa)) {
# determine ov idx for this set
ov.efa <-
unique(lavpartable$rhs[lavpartable$op == "=~" &
lavpartable$block == g &
lavpartable$efa == efa.values[set]])
lv.efa <-
unique(lavpartable$lhs[lavpartable$op == "=~" &
lavpartable$block == g &
lavpartable$efa == efa.values[set]])
lambda.idx <- which(lavpartable$lhs %in% lv.efa &
lavpartable$op == "=~" &
lavpartable$group == group.values[g])
theta.idx <- which(lavpartable$lhs %in% ov.efa &
lavpartable$op == "~~" &
lavpartable$lhs == lavpartable$rhs &
lavpartable$group == group.values[g])
# get observed indicators for these EFA lv variables
ov.idx <- match(
unique(lavpartable$rhs[lambda.idx]),
ov.names
)
if (length(ov.idx) > 0L && !any(is.na(ov.idx))) {
if (lavsamplestats@missing.flag && nlevels == 1L) {
COV <- lavsamplestats@missing.h1[[g]]$sigma[ov.idx,
ov.idx,
drop = FALSE
]
} else {
if (conditional.x) {
COV <- lavsamplestats@res.cov[[g]][ov.idx,
ov.idx,
drop = FALSE
]
} else {
COV <- lavsamplestats@cov[[g]][ov.idx,
ov.idx,
drop = FALSE
]
}
}
# EFA solution with zero upper-right corner
EFA <- lav_efa_extraction(
S = COV,
nfactors = length(lv.efa),
method = "ML",
order.lv.by = order.lv.by,
# order.lv.by = "none",
# reflect = reflect,
reflect = FALSE,
corner = TRUE
)
# factor loadings
tmp <- as.numeric(EFA$LAMBDA)
tmp[!is.finite(tmp)] <- 1.0 # just in case (eg 0/0)
start[lambda.idx] <- tmp
# residual variances
tmp <- diag(EFA$THETA)
tmp[!is.finite(tmp)] <- 1.0 # just in case
start[theta.idx] <- tmp
}
} # set
} # efa
} # factor loadings
if (model.type == "unrestricted") {
# fill in 'covariances' from lavsamplestats
cov.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~~" &
lavpartable$lhs != lavpartable$rhs)
lhs.idx <- match(lavpartable$lhs[cov.idx], ov.names)
rhs.idx <- match(lavpartable$rhs[cov.idx], ov.names)
if (!is.null(lavsamplestats@missing.h1[[g]])) {
start[cov.idx] <- lavsamplestats@missing.h1[[g]]$sigma[
cbind(lhs.idx, rhs.idx)
]
} else {
start[cov.idx] <- lavsamplestats@cov[[g]][
cbind(lhs.idx, rhs.idx)
]
}
}
# variances of ordinal variables - set to 1.0
if (categorical) {
ov.var.ord.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~~" &
lavpartable$lhs %in% ov.names.ord &
lavpartable$lhs == lavpartable$rhs)
start[ov.var.ord.idx] <- 1.0
}
# 3g) intercepts/means
ov.int.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~1" &
lavpartable$lhs %in% ov.names)
sample.int.idx <- match(lavpartable$lhs[ov.int.idx], ov.names)
if (lavsamplestats@missing.flag && nlevels == 1L) {
start[ov.int.idx] <- lavsamplestats@missing.h1[[g]]$mu[sample.int.idx]
} else {
if (conditional.x && nlevels == 1L) {
start[ov.int.idx] <- lavsamplestats@res.int[[g]][sample.int.idx]
} else {
start[ov.int.idx] <- lavsamplestats@mean[[g]][sample.int.idx]
}
}
# TODo: if marker.int.zero = TRUE, set lv means to marker means,
# and the non-marker means to
# lavsamplestats@mean[[g]] - LAMBDA %*% ALPHA
# where ALPHA = means of the markers
# 4g) thresholds
th.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "|")
if (length(th.idx) > 0L) {
th.names.lavpartable <- paste(lavpartable$lhs[th.idx], "|",
lavpartable$rhs[th.idx],
sep = ""
)
th.names.sample <-
lavsamplestats@th.names[[g]][lavsamplestats@th.idx[[g]] > 0L]
# th.names.sample should identical to
# vnames(lavpartable, "th", group = group.values[g])
if (conditional.x && nlevels == 1L) {
th.values <-
lavsamplestats@res.th[[g]][lavsamplestats@th.idx[[g]] > 0L]
} else {
th.values <-
lavsamplestats@th[[g]][lavsamplestats@th.idx[[g]] > 0L]
}
start[th.idx] <- th.values[match(
th.names.lavpartable,
th.names.sample
)]
}
# 5g) exogenous `fixed.x' covariates
if (length(ov.names.x) > 0) {
exo.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~~" &
lavpartable$lhs %in% ov.names.x &
lavpartable$rhs %in% ov.names.x)
if (!conditional.x) {
row.idx <- match(lavpartable$lhs[exo.idx], ov.names)
col.idx <- match(lavpartable$rhs[exo.idx], ov.names)
if (lavsamplestats@missing.flag && nlevels == 1L) {
start[exo.idx] <-
lavsamplestats@missing.h1[[g]]$sigma[cbind(row.idx, col.idx)]
# using slightly smaller starting values for free
# variance/covariances (fixed.x = FALSE);
# this somehow avoids false convergence in saturated models
nobs <- lavsamplestats@nobs[[g]]
this.idx <- which(seq_len(length(lavpartable$free)) %in% exo.idx &
lavpartable$free > 0L)
start[this.idx] <- start[this.idx] * (nobs - 1) / nobs
} else {
start[exo.idx] <- lavsamplestats@cov[[g]][cbind(row.idx, col.idx)]
}
} else {
# cov.x
row.idx <- match(lavpartable$lhs[exo.idx], ov.names.x)
col.idx <- match(lavpartable$rhs[exo.idx], ov.names.x)
start[exo.idx] <- lavsamplestats@cov.x[[g]][cbind(
row.idx,
col.idx
)]
# mean.x
exo.int.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~1" &
lavpartable$lhs %in% ov.names.x)
int.idx <- match(lavpartable$lhs[exo.int.idx], ov.names.x)
start[exo.int.idx] <- lavsamplestats@mean.x[[g]][int.idx]
}
}
# 6b. exogenous lv variances if single indicator -- new in 0.5-21
lv.x <- vnames(lavpartable, "lv.x", group = group.values[g])
# FIXME: also for multilevel?
lv.x <- unique(unlist(lv.x))
if (length(lv.x) > 0L) {
for (ll in lv.x) {
ind.idx <- which(lavpartable$op == "=~" &
lavpartable$lhs == ll &
lavpartable$group == group.values[g])
if (length(ind.idx) == 1L) {
single.ind <- lavpartable$rhs[ind.idx]
single.fvar.idx <- which(lavpartable$op == "~~" &
lavpartable$lhs == ll &
lavpartable$rhs == ll &
lavpartable$group == group.values[g])
single.var.idx <- which(lavpartable$op == "~~" &
lavpartable$lhs == single.ind &
lavpartable$rhs == single.ind &
lavpartable$group == group.values[g])
# user-defined residual variance
# fixme: we take the first, in case we have multiple matches
# (eg nlevels)
single.var <- lavpartable$ustart[single.var.idx[1]]
if (is.na(single.var)) {
single.var <- 1
}
ov.idx <- match(single.ind, ov.names)
if (conditional.x && nlevels == 1L) {
ov.var <- diag(lavsamplestats@res.cov[[g]])[ov.idx]
} else {
ov.var <- diag(lavsamplestats@cov[[g]])[ov.idx]
}
# take (1 - (rvar/ov.var) * ov.var
tmp <- (1 - (single.var / ov.var)) * ov.var
# just in case
if (is.na(tmp) || tmp < 0.05) {
tmp <- 0.05
}
start[single.fvar.idx] <- tmp
}
}
}
# 7g) regressions "~" # new in 0.6-10
if (length(lv.names) == 0L && nlevels == 1L && !conditional.x) {
# observed only
reg.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~")
if (length(reg.idx) > 0L) {
eqs.y <- unique(lavpartable$lhs[reg.idx])
ny <- length(eqs.y)
for (i in seq_len(ny)) {
y.name <- eqs.y[i]
start.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~" &
lavpartable$lhs == y.name)
x.names <- lavpartable$rhs[start.idx]
COV <- lavsamplestats@cov[[g]]
y.idx <- match(y.name, ov.names)
x.idx <- match(x.names, ov.names)
S.xx <- COV[x.idx, x.idx, drop = FALSE]
S.xy <- COV[x.idx, y.idx, drop = FALSE]
# regression coefficient(s)
beta.i <- try(solve(S.xx, S.xy), silent = TRUE)
if (inherits(beta.i, "try-error")) {
start[start.idx] <- beta.i <- rep(0, length(start.idx))
} else {
start[start.idx] <- drop(beta.i)
}
# residual variance
res.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~~" &
lavpartable$lhs == y.name &
lavpartable$rhs == y.name)
res.val <- COV[y.idx, y.idx] - drop(crossprod(beta.i, S.xy))
if (res.val > 0.001 * COV[y.idx, y.idx] &&
res.val < 0.999 * COV[y.idx, y.idx]) {
start[res.idx] <- res.val
} else {
# do nothing (keep what we have)
}
# intercept
int.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~1" &
lavpartable$lhs == y.name)
if (length(int.idx) > 0L) {
MEAN <- lavsamplestats@mean[[g]]
Ybar <- MEAN[y.idx]
Xbar <- MEAN[x.idx]
int.val <- Ybar - drop(crossprod(beta.i, Xbar))
if (is.finite(int.val)) {
start[int.idx] <- int.val
}
}
}
}
}
# # 8 latent variances (new in 0.6-2)
# lv.names.y <- vnames(lavpartable, "lv.y", group = group.values[g])
# lv.names.x <- vnames(lavpartable, "lv.x", group = group.values[g])
# # multilevel? take first level only
# if(is.list(lv.names.y)) {
# lv.names.y <- unlist(lv.names.y) # for now
# }
# if(is.list(lv.names.x)) {
# lv.names.x <- unlist(lv.names.x) # for now
# }
# lv.names.xy <- unique(c(lv.names.x, lv.names.y))
# if(length(lv.names.xy) > 0L) {
# free.var.idx <- which(lavpartable$op == "~~" &
# lavpartable$lhs %in% lv.names.xy &
# lavpartable$rhs == lavpartable$lhs &
# lavpartable$group == group.values[g])
# if(length(free.var.idx) > 0L) {
# this.lv.names <- lavpartable$lhs[free.var.idx]
# for(v in seq_len(length(free.var.idx))) {
# # single marker item?
# ind.idx <- which(lavpartable$op == "=~" &
# lavpartable$lhs %in% this.lv.names[v] &
# #lavpartable$rhs %in% ov.names.num &
# lavpartable$free == 0L &
# lavpartable$group == group.values[g])
# if(length(ind.idx) == 0) {
# next
# } else if(length(ind.idx) > 1L) {
# # FIXME! perhaps a random effect? do something clever
# next
# } else if(length(ind.idx) == 1L) {
# marker.ind <- lavpartable$rhs[ind.idx]
# ov.idx <- match(marker.ind, ov.names)
# if(conditional.x) {
# ov.var <- diag(lavsamplestats@res.cov[[g]])[ov.idx]
# } else {
# ov.var <- diag(lavsamplestats@cov[[g]])[ov.idx]
# }
#
# # exogenous? assume rel = 0.50
# lambda <- lavpartable$ustart[ind.idx]
# tmp <- (0.50 * ov.var)/lambda^2
# if(this.lv.names[v] %in% lv.names.y) {
# # endogenous, assume R2 = 0.2
# tmp <- 0.8 * tmp
# }
# # within variance?
# if(nlevels > 1L &&
# lavpartable$level[ free.var.idx[v] ] == 1L) {
# tmp <- tmp * 0.75
# }
# # between variance?
# if(nlevels > 1L &&
# lavpartable$level[ free.var.idx[v] ] > 1L) {
# tmp <- tmp * 0.25
# }
# # just in case
# if(is.na(tmp) || tmp < 0.05) {
# tmp <- 0.05
# }
# start[ free.var.idx[v] ] <- tmp
# }
# } # v
# } # free.var.idx
# } # lv var
# nlevels > 1L
if (nlevels > 1L) {
level.values <- lav_partable_level_values(lavpartable)
# Note: ov.names.x contains all levels within a group!
if (length(ov.names.x) > 0) {
for (l in 1:nlevels) {
# block number
block <- (g - 1L) * nlevels + l
this.block.x <- lav_partable_vnames(lavpartable, "ov.x",
block = block
)
this.block.ov <- lav_partable_vnames(lavpartable, "ov",
block = block
)
if (length(this.block.x) == 0L) {
next
}
# var/cov
exo.idx <- which(lavpartable$group == group.values[g] &
lavpartable$level == level.values[l] &
lavpartable$op == "~~" &
lavpartable$lhs %in% this.block.x &
lavpartable$rhs %in% this.block.x)
if (is.null(lavh1$implied$cov[[1]])) {
row.idx <- match(lavpartable$lhs[exo.idx], ov.names)
col.idx <- match(lavpartable$rhs[exo.idx], ov.names)
if (l == 1L) {
COV <- lavsamplestats@YLp[[g]][[2]]$S.PW.start
} else {
COV <- lavsamplestats@YLp[[g]][[l]]$Sigma.B
}
} else {
row.idx <- match(lavpartable$lhs[exo.idx], this.block.ov)
col.idx <- match(lavpartable$rhs[exo.idx], this.block.ov)
COV <- lavh1$implied$cov[[block]]
}
# make sure starting values for variances are positive
neg.idx <- which(diag(COV) < 0.001)
if (length(neg.idx) > 0L) {
diag(COV)[neg.idx] <- 0.001
}
start[exo.idx] <- COV[cbind(row.idx, col.idx)]
# intercepts
ov.int.idx <- which(lavpartable$group == group.values[g] &
lavpartable$level == level.values[l] &
lavpartable$op == "~1" &
lavpartable$lhs %in% this.block.x)
if (is.null(lavh1$implied$mean[[1]])) {
idx <- match(lavpartable$lhs[ov.int.idx], ov.names)
if (l == 1L) {
INT <- lavsamplestats@YLp[[g]][[2]]$Mu.W
} else {
INT <- lavsamplestats@YLp[[g]][[l]]$Mu.B.start
}
} else {
idx <- match(lavpartable$lhs[ov.int.idx], this.block.ov)
INT <- lavh1$implied$mean[[block]]
}
start[ov.int.idx] <- INT[idx]
# new in 0.6-12
# very special case: conditional.x with a combination of
# splitted-x and regular-x
# here, we must:
# 1) replace var/cov of splitted-x by *residual* varcov
# after regressing out regular-x
# 2) replace means of splitted-x by intercepts
# 3) fill splitted-x ~ regular-x regression coefficients
if (conditional.x) {
if (is.null(lavh1$implied$cov[[l]])) {
lav_msg_stop(gettext(
"lavh1 information is needed; please rerun with h1 = TRUE"))
}
blocks.within.group <- (g - 1L) * nlevels + seq_len(nlevels)
OTHER.BLOCK.NAMES <- lav_partable_vnames(lavpartable, "ov",
block = blocks.within.group[-block])
ov.names.x.block <- this.block.x
idx <- which(ov.names.x.block %in% OTHER.BLOCK.NAMES)
if (length(idx) > 0L) {
ov.names.x.block <- ov.names.x.block[-idx]
}
ov.names.x1 <- this.block.x[!this.block.x %in% ov.names.x.block]
ov.names.x2 <- ov.names.x.block
nx1 <- length(ov.names.x1) # splitted x
nx2 <- length(ov.names.x2) # regular x
if (nx1 > 0L && nx2 > 0L) {
# COV
c1.idx <- match(ov.names.x1, this.block.ov)
c2.idx <- match(ov.names.x2, this.block.ov)
COV.Y <- COV[c1.idx, c1.idx, drop = FALSE]
COV.X <- COV[c2.idx, c2.idx, drop = FALSE]
COV.YX <- COV[c1.idx, c2.idx, drop = FALSE]
COV.XY <- COV[c2.idx, c1.idx, drop = FALSE]
COV.XinvYX <- solve(COV.X, COV.XY)
RES.COV <- COV.Y - COV.YX %*% COV.XinvYX
res.cov.idx <- which(lavpartable$group == group.values[g] &
lavpartable$level == level.values[l] &
lavpartable$op == "~~" &
lavpartable$lhs %in% ov.names.x1 &
lavpartable$rhs %in% ov.names.x1)
row.idx <- match(lavpartable$lhs[res.cov.idx], ov.names.x1)
col.idx <- match(lavpartable$rhs[res.cov.idx], ov.names.x1)
start[res.cov.idx] <- RES.COV[cbind(row.idx, col.idx)]
# INT
INT.Y <- INT[c1.idx]
INT.X <- INT[c2.idx]
RES.INT <- INT.Y - t(COV.XinvYX) %*% INT.X
res.int.idx <- which(lavpartable$group == group.values[g] &
lavpartable$level == level.values[l] &
lavpartable$op == "~1" &
lavpartable$lhs %in% ov.names.x1)
idx <- match(lavpartable$lhs[res.int.idx], ov.names.x1)
start[res.int.idx] <- RES.INT[idx]
# REG
reg.idx <- which(lavpartable$group == group.values[g] &
lavpartable$level == level.values[l] &
lavpartable$op == "~" &
lavpartable$lhs %in% ov.names.x1 &
lavpartable$rhs %in% ov.names.x2)
row.idx <- match(lavpartable$lhs[reg.idx], ov.names.x1)
col.idx <- match(lavpartable$rhs[reg.idx], ov.names.x2)
start[reg.idx] <- t(COV.XinvYX)[cbind(row.idx, col.idx)]
} # special case
} # conditional.x
} # levels
} # fixed.x
} # nlevels > 1L
} # groups
# group weights
group.idx <- which(lavpartable$lhs == "group" &
lavpartable$op == "%")
if (length(group.idx) > 0L) {
ngroups <- length(group.idx)
# prop <- rep(1/ngroups, ngroups)
# use last group as reference
# start[group.idx] <- log(prop/prop[ngroups])
# poisson version
start[group.idx] <- log(rep(lavsamplestats@ntotal / ngroups, ngroups))
}
# growth models:
# - compute starting values for mean latent variables
# - compute starting values for variance latent variables
if (start.initial %in% c("lavaan", "mplus") &&
model.type == "growth") {
### DEBUG ONLY
# lv.var.idx <- which(lavpartable$op == "~~" &
# lavpartable$lhs %in% lv.names &
# lavpartable$lhs == lavpartable$rhs)
### DEBUG ONLY
# lv.int.idx <- which(lavpartable$op == "~1" &
# lavpartable$lhs %in% lv.names)
}
# adjust if outside bounds -- new in 0.6-6
if (!is.null(lavpartable$lower)) {
bad.idx <- which(start < lavpartable$lower)
if (length(bad.idx)) {
start[bad.idx] <- lavpartable$lower[bad.idx]
}
}
if (!is.null(lavpartable$upper)) {
bad.idx <- which(start > lavpartable$upper)
if (length(bad.idx)) {
start[bad.idx] <- lavpartable$upper[bad.idx]
}
}
# override if the model syntax contains explicit starting values (free only)
# user.idx <- which(!is.na(lavpartable$ustart) &
# lavpartable$user != 7L) # new in 0.6-7, if rotation and
# # and we change the order of lv's
user.idx <- which(!is.na(lavpartable$ustart) & lavpartable$free > 0L)
start[user.idx] <- lavpartable$ustart[user.idx]
# override if a user list with starting values is provided
# we only look at the 'est' column for now
if (!is.null(start.user)) {
if (is.null(lavpartable$group)) {
lavpartable$group <- rep(1L, length(lavpartable$lhs))
}
if (is.null(start.user$group)) {
start.user$group <- rep(1L, length(start.user$lhs))
}
# FIXME: avoid for loop!!!
for (i in seq_along(lavpartable$lhs)) {
# find corresponding parameters
lhs <- lavpartable$lhs[i]
op <- lavpartable$op[i]
rhs <- lavpartable$rhs[i]
grp <- lavpartable$group[i]
start.user.idx <- which(start.user$lhs == lhs &
start.user$op == op &
start.user$rhs == rhs &
start.user$group == grp)
if (length(start.user.idx) == 1L &&
is.finite(start.user$est[start.user.idx])) {
start[i] <- start.user$est[start.user.idx]
}
}
}
# override fixed values with ustart values
user.idx <- which(!is.na(lavpartable$ustart) & lavpartable$free == 0L)
start[user.idx] <- lavpartable$ustart[user.idx]
# final check: no NaN or other non-finite values
bad.idx <- which(!is.finite(start))
if (length(bad.idx) > 0L) {
cat("starting values:\n")
print(start)
lav_msg_warn(gettext(
"some starting values are non-finite; replacing them with 0.5;
please provide better starting values."))
start[bad.idx] <- 0.5
}
if (lav_debug()) {
cat("lavaan DEBUG: lavaanStart\n")
print(start)
}
start
}
# backwards compatibility
# StartingValues <- lav_start
# sanity check: (user-specified) variances smaller than covariances
lav_start_check_cov <- function(lavpartable = NULL, start = lavpartable$start) {
nblocks <- lav_partable_nblocks(lavpartable)
block.values <- lav_partable_block_values(lavpartable)
for (g in 1:nblocks) {
# collect all non-zero covariances
cov.idx <- which(lavpartable$op == "~~" &
lavpartable$block == block.values[g] &
lavpartable$lhs != lavpartable$rhs &
!lavpartable$exo &
start != 0)
# for each covariance, use corresponding variances to standardize;
# the end result should not exceed abs(1)
for (cc in seq_along(cov.idx)) {
this.cov.idx <- cov.idx[cc]
# find corresponding variances
var.lhs <- lavpartable$lhs[this.cov.idx]
var.rhs <- lavpartable$rhs[this.cov.idx]
var.lhs.idx <- which(lavpartable$op == "~~" &
lavpartable$block == block.values[g] &
lavpartable$lhs == var.lhs &
lavpartable$lhs == lavpartable$rhs)
var.rhs.idx <- which(lavpartable$op == "~~" &
lavpartable$block == block.values[g] &
lavpartable$lhs == var.rhs &
lavpartable$lhs == lavpartable$rhs)
var.lhs.value <- start[var.lhs.idx]
var.rhs.value <- start[var.rhs.idx]
block.txt <- ""
if (nblocks > 1L) {
block.txt <- paste(" [in block ", g, "]", sep = "")
}
# check for zero variances
if (var.lhs.value == 0 || var.rhs.value == 0) {
# this can only happen if it is user-specified
# cov.idx free? set it to zero
if (start[this.cov.idx] == 0) {
# nothing to do
} else if (lavpartable$free[this.cov.idx] > 0L) {
lav_msg_warn(gettextf(
"non-zero covariance element set to zero, due to fixed-to-zero
variances variables involved are: %s", var.lhs), var.rhs,
block.txt
)
start[this.cov.idx] <- 0
} else {
lav_msg_stop(gettextf(
"please provide better fixed values for (co)variances;
variables involved are: %s ", var.lhs), var.rhs, block.txt
)
}
next
}
# which one is the smallest? abs() in case of negative variances
if (abs(var.lhs.value) < abs(var.rhs.value)) {
var.min.idx <- var.lhs.idx
var.max.idx <- var.rhs.idx
} else {
var.min.idx <- var.rhs.idx
var.max.idx <- var.lhs.idx
}
# check
COR <- abs(start[this.cov.idx] / sqrt(var.lhs.value * var.rhs.value))
# NOTE: we treat this as an unconditional COR!
if (!is.finite(COR)) {
# force simple values
lav_msg_warn(gettextf(
"starting values imply NaN for a correlation value; variables
involved are: %s", var.lhs), var.rhs, block.txt
)
start[var.lhs.idx] <- 1
start[var.rhs.idx] <- 1
start[this.cov.idx] <- 0
} else if (COR > 1) {
txt <- gettextf(
"starting values imply a correlation larger than 1; variables
involved are: %1$s %2$s %3$s", var.lhs, var.rhs, block.txt)
# three ways to fix it: rescale cov12, var1 or var2
# we prefer a free parameter, and not user-specified
if (lavpartable$free[this.cov.idx] > 0L &&
is.na(lavpartable$ustart[this.cov.idx])) {
lav_msg_warn(gettext(txt))
start[this.cov.idx] <- start[this.cov.idx] / (COR * 1.1)
} else if (lavpartable$free[var.min.idx] > 0L &&
is.na(lavpartable$ustart[var.min.idx])) {
lav_msg_warn(gettext(txt))
start[var.min.idx] <- start[var.min.idx] * (COR * 1.1)^2
} else if (lavpartable$free[var.max.idx] > 0L &&
is.na(lavpartable$ustart[var.max.idx])) {
lav_msg_warn(gettext(txt))
start[var.max.idx] <- start[var.max.idx] * (COR * 1.1)^2
# not found? try just a free parameter
} else if (lavpartable$free[this.cov.idx] > 0L) {
lav_msg_warn(gettext(txt))
start[this.cov.idx] <- start[this.cov.idx] / (COR * 1.1)
} else if (lavpartable$free[var.min.idx] > 0L) {
lav_msg_warn(gettext(txt))
start[var.min.idx] <- start[var.min.idx] * (COR * 1.1)^2
} else if (lavpartable$free[var.max.idx] > 0L) {
lav_msg_warn(gettext(txt))
start[var.max.idx] <- start[var.max.idx] * (COR * 1.1)^2
# nothing? abort or warn (and fail later...): warn
} else {
lav_msg_warn(gettext(txt))
# lav_msg_stop(gettextf(
# "please provide better fixed values for (co)variances;
# variables involved are: %s ", var.lhs), var.rhs, block.txt)
}
} # COR > 1
} # cov.idx
}
start
}
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Defines functions lav_start_check_cov lav_start
# lav_start.R: provide starting values for model parameters
#
# YR 30/11/2010: initial version
# YR 08/06/2011: add fabin3 start values for factor loadings
# YR 14 Jan 2014: moved to lav_start.R
# fill in the 'ustart' column in a User data.frame with reasonable
# starting values, using the sample data
lav_start <- function(start.method = "default",
lavpartable = NULL,
lavsamplestats = NULL,
lavh1 = NULL, # fixme: only use lavh1?
model.type = "sem",
mimic = "lavaan",
reflect = FALSE, # rotation only
samplestats.flag = TRUE,
order.lv.by = "none" # rotation only
) {
# check arguments
stopifnot(is.list(lavpartable))
# categorical?
categorical <- any(lavpartable$op == "|")
# correlation structure?
correlation <- any(lavpartable$op == "~*~")
# conditional.x?
conditional.x <- any(lavpartable$exo == 1L &
lavpartable$op %in% c("~", "<~"))
# ord.names <- unique(lavpartable$lhs[ lavpartable$op == "|" ])
# nlevels?
nlevels <- lav_partable_nlevels(lavpartable)
# reflect/order.lv.by
if (is.null(reflect)) {
reflect <- FALSE
}
if (is.null(order.lv.by)) {
order.lv.by <- "index"
}
# check start.method
if (mimic == "lavaan") {
start.initial <- "lavaan"
} else if (mimic == "Mplus") {
start.initial <- "mplus"
} else {
# FIXME: use LISREL/EQS/AMOS/.... schemes
start.initial <- "lavaan"
}
# start.method
start.user <- NULL
if (is.character(start.method)) {
start.method.lc <- tolower(start.method)
if (start.method.lc != "simple" && !samplestats.flag) {
start.method.lc <- start.method <- "simple"
}
if (start.method.lc == "default") {
# nothing to do
} else if (start.method == "simple") {
start <- numeric(length(lavpartable$ustart))
# if(categorical || correlation) {
start[which(lavpartable$op == "=~")] <- 0.7
# } else {
# start[ which(lavpartable$op == "=~") ] <- 1.0
# }
start[which(lavpartable$op == "~*~")] <- 1.0
ov.names.ord <- vnames(lavpartable, "ov.ord")
var.idx <- which(lavpartable$op == "~~" &
lavpartable$lhs == lavpartable$rhs &
!(lavpartable$lhs %in% ov.names.ord))
start[var.idx] <- 1.0
user.idx <- which(!is.na(lavpartable$ustart))
start[user.idx] <- lavpartable$ustart[user.idx]
return(start) # assuming fixed.x = FALSE!
} else if (start.method == "est") {
return(lavpartable$est)
} else if (start.method.lc %in% c("simple", "lavaan", "mplus")) {
start.initial <- start.method.lc
} else {
lav_msg_stop(gettext("unknown value for start argument"))
}
} else if (is.list(start.method)) {
start.user <- start.method
} else if (is.numeric(start.method)) {
nx.free <- sum(lavpartable$free > 0L)
if (length(start.method) != nx.free) {
lav_msg_stop(gettextf(
"start argument contains %1$s elements; but parameter table
expects %2$s free parameters.", length(start.method), nx.free))
}
lavpartable$ustart[lavpartable$free > 0L] <- start.method
} else if (inherits(start.method, "lavaan")) {
start.user <- parTable(start.method)
}
# check model list elements, if provided
if (!is.null(start.user)) {
if (is.null(start.user$lhs) ||
is.null(start.user$op) ||
is.null(start.user$rhs)) {
lav_msg_stop(gettext(
"problem with start argument: model list does not contain
all elements: lhs/op/rhs"))
}
if (!is.null(start.user$est)) {
# excellent, we got an est column; nothing to do
} else if (!is.null(start.user$start)) {
# no est column, but we use the start column
start.user$est <- start.user$start
} else if (!is.null(start.user$ustart)) {
# no ideal, but better than nothing
start.user$est <- start.user$ustart
} else {
lav_msg_stop(gettext(
"problem with start argument: could not find est/start column
in model list"))
}
}
# global settings
# 0. everyting is zero
start <- numeric(length(lavpartable$ustart))
# 1. =~ factor loadings:
if (categorical || correlation) {
# if std.lv=TRUE, 0.8 is too large
start[which(lavpartable$op == "=~")] <- 0.7
} else {
start[which(lavpartable$op == "=~")] <- 1.0
}
# 2. (residual) lv variances for latent variables
lv.names <- vnames(lavpartable, "lv") # all groups
lv.var.idx <- which(lavpartable$op == "~~" &
lavpartable$lhs %in% lv.names &
lavpartable$lhs == lavpartable$rhs)
start[lv.var.idx] <- 0.05
# start[lv.var.idx] <- 0.5 # new in 0.6-2? (for optim.parscale = "stand")
# 3. latent response scales (if any)
delta.idx <- which(lavpartable$op == "~*~")
start[delta.idx] <- 1.0
# group-specific settings
ngroups <- lav_partable_ngroups(lavpartable)
# for now, if no group column, add one (again), until we rewrite
# this function to handle block/group hybrid settings
if (is.null(lavpartable$group) && ngroups == 1L) {
lavpartable$group <- rep(1L, length(lavpartable$lhs))
lavpartable$group[lavpartable$block == 0L] <- 0L
}
for (g in 1:ngroups) {
# group values (not necessarily 1,2,... anymore)
group.values <- lav_partable_group_values(lavpartable)
# info from user model for this group
if (conditional.x) {
ov.names <- vnames(lavpartable, "ov.nox", group = group.values[g])
} else {
ov.names <- vnames(lavpartable, "ov", group = group.values[g])
}
if (categorical) {
ov.names.num <- vnames(lavpartable, "ov.num", group = group.values[g])
ov.names.ord <- vnames(lavpartable, "ov.ord", group = group.values[g])
} else {
ov.names.num <- ov.names
}
lv.names <- vnames(lavpartable, "lv", group = group.values[g])
lv.names.efa <- vnames(lavpartable, "lv.efa", group = group.values[g])
ov.names.x <- vnames(lavpartable, "ov.x", group = group.values[g])
# just for the nlevels >1 case
ov.names <- unique(unlist(ov.names))
ov.names.num <- unique(unlist(ov.names.num))
lv.names <- unique(unlist(lv.names))
lv.names.efa <- unique(unlist(lv.names.efa))
ov.names.x <- unique(unlist(ov.names.x))
# residual ov variances (including exo/ind, to be overriden)
ov.var.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~~" &
lavpartable$lhs %in% ov.names.num &
lavpartable$lhs == lavpartable$rhs)
sample.var.idx <- match(lavpartable$lhs[ov.var.idx], ov.names)
if (model.type == "unrestricted") {
# this does not work if conditional.x = TRUE...
if (!is.null(lavsamplestats@missing.h1[[g]])) {
start[ov.var.idx] <-
diag(lavsamplestats@missing.h1[[g]]$sigma)[sample.var.idx]
} else {
start[ov.var.idx] <-
diag(lavsamplestats@cov[[g]])[sample.var.idx]
}
} else {
if (start.initial == "mplus") {
if (conditional.x && nlevels == 1L) {
start[ov.var.idx] <-
(1.0 - 0.50) * lavsamplestats@res.var[[1L]][sample.var.idx]
} else {
start[ov.var.idx] <-
(1.0 - 0.50) * lavsamplestats@var[[1L]][sample.var.idx]
}
} else {
if (conditional.x && nlevels == 1L) {
start[ov.var.idx] <-
(1.0 - 0.50) * diag(lavsamplestats@res.cov[[g]])[sample.var.idx]
} else {
start[ov.var.idx] <-
(1.0 - 0.50) * diag(lavsamplestats@cov[[g]])[sample.var.idx]
}
}
}
# 1-fac measurement models: loadings, psi, theta
if (start.initial %in% c("lavaan", "mplus") &&
model.type %in% c("sem", "cfa")) {
# fabin3 estimator (2sls) of Hagglund (1982) per factor
for (f in lv.names) {
# not for efa factors
if (f %in% lv.names.efa) {
next
}
lambda.idx <- which(lavpartable$lhs == f &
lavpartable$op == "=~" &
lavpartable$group == group.values[g])
# standardized?
std.lv <- FALSE
var.f.idx <- which(lavpartable$lhs == f &
lavpartable$op == "~~" &
lavpartable$group == group.values[g] &
lavpartable$rhs == f)
if (length(var.f.idx) > 0L &&
all(lavpartable$free[var.f.idx] == 0) &&
all(lavpartable$ustart[var.f.idx] == 1)) {
std.lv <- TRUE
}
# no second order
if (any(lavpartable$rhs[lambda.idx] %in% lv.names)) next
# get observed indicators for this latent variable
ov.idx <- match(lavpartable$rhs[lambda.idx], ov.names)
if (length(ov.idx) > 0L && !any(is.na(ov.idx))) {
if (lavsamplestats@missing.flag && nlevels == 1L) {
COV <- lavsamplestats@missing.h1[[g]]$sigma[ov.idx,
ov.idx,
drop = FALSE
]
} else {
if (conditional.x && nlevels == 1L) {
COV <- lavsamplestats@res.cov[[g]][ov.idx,
ov.idx,
drop = FALSE
]
} else {
COV <- lavsamplestats@cov[[g]][ov.idx,
ov.idx,
drop = FALSE
]
}
}
# fabin for 1-factor
fabin <- lav_cfa_1fac_fabin(COV,
std.lv = std.lv,
lambda.only = TRUE,
method = "fabin3"
)
# factor loadings
tmp <- fabin$lambda
tmp[!is.finite(tmp)] <- 1.0 # just in case (eg 0/0)
# check for negative triad if nvar=3L (new in 0.6-8)
if (!is.null(fabin$neg.triad) && fabin$neg.triad) {
if (std.lv) {
tmp <- rep(0.7, length(tmp))
} else {
tmp <- rep(1.0, length(tmp))
}
}
start[lambda.idx] <- tmp
# factor variance
# if(!std.lv) {
# start[var.f.idx] <- fabin$psi
# # if residual var, make smaller
# y.idx <- which(lavpartable$lhs == f &
# lavpartable$group == group.values[g] &
# lavpartable$op == "~")
# if(length(y.idx) > 0L) {
# # how much explained variance do we expect?
# # we take 0.50
# start[var.f.idx] <- 0.5 * start[var.f.idx]
# }
# # no negative variances (we get these if we have an
# # inconsistent triad (eg, covariance signs are +,+,-)
# if(start[var.f.idx] < 0) {
# start[var.f.idx] <- 0.05
# }
# }
# NOTE: fabin (sometimes) gives residual variances
# that are larger than the original variances...
# residual variances -- order?
# res.idx <- which(lavpartable$lhs %in% ov.names[ov.idx] &
# lavpartable$op == "~~" &
# lavpartable$group == group.values[g] &
# lavpartable$rhs == lavpartable$lhs)
# start[res.idx] <- fabin$theta
# negative variances?
# neg.idx <- which(start[res.idx] < 0)
# if(length(neg.idx) > 0L) {
# start[res.idx][neg.idx] <- 0.05
# }
}
} # fabin3
# efa?
nefa <- lav_partable_nefa(lavpartable)
if (nefa > 0L) {
efa.values <- lav_partable_efa_values(lavpartable)
for (set in seq_len(nefa)) {
# determine ov idx for this set
ov.efa <-
unique(lavpartable$rhs[lavpartable$op == "=~" &
lavpartable$block == g &
lavpartable$efa == efa.values[set]])
lv.efa <-
unique(lavpartable$lhs[lavpartable$op == "=~" &
lavpartable$block == g &
lavpartable$efa == efa.values[set]])
lambda.idx <- which(lavpartable$lhs %in% lv.efa &
lavpartable$op == "=~" &
lavpartable$group == group.values[g])
theta.idx <- which(lavpartable$lhs %in% ov.efa &
lavpartable$op == "~~" &
lavpartable$lhs == lavpartable$rhs &
lavpartable$group == group.values[g])
# get observed indicators for these EFA lv variables
ov.idx <- match(
unique(lavpartable$rhs[lambda.idx]),
ov.names
)
if (length(ov.idx) > 0L && !any(is.na(ov.idx))) {
if (lavsamplestats@missing.flag && nlevels == 1L) {
COV <- lavsamplestats@missing.h1[[g]]$sigma[ov.idx,
ov.idx,
drop = FALSE
]
} else {
if (conditional.x) {
COV <- lavsamplestats@res.cov[[g]][ov.idx,
ov.idx,
drop = FALSE
]
} else {
COV <- lavsamplestats@cov[[g]][ov.idx,
ov.idx,
drop = FALSE
]
}
}
# EFA solution with zero upper-right corner
EFA <- lav_efa_extraction(
S = COV,
nfactors = length(lv.efa),
method = "ML",
order.lv.by = order.lv.by,
# order.lv.by = "none",
# reflect = reflect,
reflect = FALSE,
corner = TRUE
)
# factor loadings
tmp <- as.numeric(EFA$LAMBDA)
tmp[!is.finite(tmp)] <- 1.0 # just in case (eg 0/0)
start[lambda.idx] <- tmp
# residual variances
tmp <- diag(EFA$THETA)
tmp[!is.finite(tmp)] <- 1.0 # just in case
start[theta.idx] <- tmp
}
} # set
} # efa
} # factor loadings
if (model.type == "unrestricted") {
# fill in 'covariances' from lavsamplestats
cov.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~~" &
lavpartable$lhs != lavpartable$rhs)
lhs.idx <- match(lavpartable$lhs[cov.idx], ov.names)
rhs.idx <- match(lavpartable$rhs[cov.idx], ov.names)
if (!is.null(lavsamplestats@missing.h1[[g]])) {
start[cov.idx] <- lavsamplestats@missing.h1[[g]]$sigma[
cbind(lhs.idx, rhs.idx)
]
} else {
start[cov.idx] <- lavsamplestats@cov[[g]][
cbind(lhs.idx, rhs.idx)
]
}
}
# variances of ordinal variables - set to 1.0
if (categorical) {
ov.var.ord.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~~" &
lavpartable$lhs %in% ov.names.ord &
lavpartable$lhs == lavpartable$rhs)
start[ov.var.ord.idx] <- 1.0
}
# 3g) intercepts/means
ov.int.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~1" &
lavpartable$lhs %in% ov.names)
sample.int.idx <- match(lavpartable$lhs[ov.int.idx], ov.names)
if (lavsamplestats@missing.flag && nlevels == 1L) {
start[ov.int.idx] <- lavsamplestats@missing.h1[[g]]$mu[sample.int.idx]
} else {
if (conditional.x && nlevels == 1L) {
start[ov.int.idx] <- lavsamplestats@res.int[[g]][sample.int.idx]
} else {
start[ov.int.idx] <- lavsamplestats@mean[[g]][sample.int.idx]
}
}
# TODo: if marker.int.zero = TRUE, set lv means to marker means,
# and the non-marker means to
# lavsamplestats@mean[[g]] - LAMBDA %*% ALPHA
# where ALPHA = means of the markers
# 4g) thresholds
th.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "|")
if (length(th.idx) > 0L) {
th.names.lavpartable <- paste(lavpartable$lhs[th.idx], "|",
lavpartable$rhs[th.idx],
sep = ""
)
th.names.sample <-
lavsamplestats@th.names[[g]][lavsamplestats@th.idx[[g]] > 0L]
# th.names.sample should identical to
# vnames(lavpartable, "th", group = group.values[g])
if (conditional.x && nlevels == 1L) {
th.values <-
lavsamplestats@res.th[[g]][lavsamplestats@th.idx[[g]] > 0L]
} else {
th.values <-
lavsamplestats@th[[g]][lavsamplestats@th.idx[[g]] > 0L]
}
start[th.idx] <- th.values[match(
th.names.lavpartable,
th.names.sample
)]
}
# 5g) exogenous `fixed.x' covariates
if (length(ov.names.x) > 0) {
exo.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~~" &
lavpartable$lhs %in% ov.names.x &
lavpartable$rhs %in% ov.names.x)
if (!conditional.x) {
row.idx <- match(lavpartable$lhs[exo.idx], ov.names)
col.idx <- match(lavpartable$rhs[exo.idx], ov.names)
if (lavsamplestats@missing.flag && nlevels == 1L) {
start[exo.idx] <-
lavsamplestats@missing.h1[[g]]$sigma[cbind(row.idx, col.idx)]
# using slightly smaller starting values for free
# variance/covariances (fixed.x = FALSE);
# this somehow avoids false convergence in saturated models
nobs <- lavsamplestats@nobs[[g]]
this.idx <- which(seq_len(length(lavpartable$free)) %in% exo.idx &
lavpartable$free > 0L)
start[this.idx] <- start[this.idx] * (nobs - 1) / nobs
} else {
start[exo.idx] <- lavsamplestats@cov[[g]][cbind(row.idx, col.idx)]
}
} else {
# cov.x
row.idx <- match(lavpartable$lhs[exo.idx], ov.names.x)
col.idx <- match(lavpartable$rhs[exo.idx], ov.names.x)
start[exo.idx] <- lavsamplestats@cov.x[[g]][cbind(
row.idx,
col.idx
)]
# mean.x
exo.int.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~1" &
lavpartable$lhs %in% ov.names.x)
int.idx <- match(lavpartable$lhs[exo.int.idx], ov.names.x)
start[exo.int.idx] <- lavsamplestats@mean.x[[g]][int.idx]
}
}
# 6b. exogenous lv variances if single indicator -- new in 0.5-21
lv.x <- vnames(lavpartable, "lv.x", group = group.values[g])
# FIXME: also for multilevel?
lv.x <- unique(unlist(lv.x))
if (length(lv.x) > 0L) {
for (ll in lv.x) {
ind.idx <- which(lavpartable$op == "=~" &
lavpartable$lhs == ll &
lavpartable$group == group.values[g])
if (length(ind.idx) == 1L) {
single.ind <- lavpartable$rhs[ind.idx]
single.fvar.idx <- which(lavpartable$op == "~~" &
lavpartable$lhs == ll &
lavpartable$rhs == ll &
lavpartable$group == group.values[g])
single.var.idx <- which(lavpartable$op == "~~" &
lavpartable$lhs == single.ind &
lavpartable$rhs == single.ind &
lavpartable$group == group.values[g])
# user-defined residual variance
# fixme: we take the first, in case we have multiple matches
# (eg nlevels)
single.var <- lavpartable$ustart[single.var.idx[1]]
if (is.na(single.var)) {
single.var <- 1
}
ov.idx <- match(single.ind, ov.names)
if (conditional.x && nlevels == 1L) {
ov.var <- diag(lavsamplestats@res.cov[[g]])[ov.idx]
} else {
ov.var <- diag(lavsamplestats@cov[[g]])[ov.idx]
}
# take (1 - (rvar/ov.var) * ov.var
tmp <- (1 - (single.var / ov.var)) * ov.var
# just in case
if (is.na(tmp) || tmp < 0.05) {
tmp <- 0.05
}
start[single.fvar.idx] <- tmp
}
}
}
# 7g) regressions "~" # new in 0.6-10
if (length(lv.names) == 0L && nlevels == 1L && !conditional.x) {
# observed only
reg.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~")
if (length(reg.idx) > 0L) {
eqs.y <- unique(lavpartable$lhs[reg.idx])
ny <- length(eqs.y)
for (i in seq_len(ny)) {
y.name <- eqs.y[i]
start.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~" &
lavpartable$lhs == y.name)
x.names <- lavpartable$rhs[start.idx]
COV <- lavsamplestats@cov[[g]]
y.idx <- match(y.name, ov.names)
x.idx <- match(x.names, ov.names)
S.xx <- COV[x.idx, x.idx, drop = FALSE]
S.xy <- COV[x.idx, y.idx, drop = FALSE]
# regression coefficient(s)
beta.i <- try(solve(S.xx, S.xy), silent = TRUE)
if (inherits(beta.i, "try-error")) {
start[start.idx] <- beta.i <- rep(0, length(start.idx))
} else {
start[start.idx] <- drop(beta.i)
}
# residual variance
res.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~~" &
lavpartable$lhs == y.name &
lavpartable$rhs == y.name)
res.val <- COV[y.idx, y.idx] - drop(crossprod(beta.i, S.xy))
if (res.val > 0.001 * COV[y.idx, y.idx] &&
res.val < 0.999 * COV[y.idx, y.idx]) {
start[res.idx] <- res.val
} else {
# do nothing (keep what we have)
}
# intercept
int.idx <- which(lavpartable$group == group.values[g] &
lavpartable$op == "~1" &
lavpartable$lhs == y.name)
if (length(int.idx) > 0L) {
MEAN <- lavsamplestats@mean[[g]]
Ybar <- MEAN[y.idx]
Xbar <- MEAN[x.idx]
int.val <- Ybar - drop(crossprod(beta.i, Xbar))
if (is.finite(int.val)) {
start[int.idx] <- int.val
}
}
}
}
}
# # 8 latent variances (new in 0.6-2)
# lv.names.y <- vnames(lavpartable, "lv.y", group = group.values[g])
# lv.names.x <- vnames(lavpartable, "lv.x", group = group.values[g])
# # multilevel? take first level only
# if(is.list(lv.names.y)) {
# lv.names.y <- unlist(lv.names.y) # for now
# }
# if(is.list(lv.names.x)) {
# lv.names.x <- unlist(lv.names.x) # for now
# }
# lv.names.xy <- unique(c(lv.names.x, lv.names.y))
# if(length(lv.names.xy) > 0L) {
# free.var.idx <- which(lavpartable$op == "~~" &
# lavpartable$lhs %in% lv.names.xy &
# lavpartable$rhs == lavpartable$lhs &
# lavpartable$group == group.values[g])
# if(length(free.var.idx) > 0L) {
# this.lv.names <- lavpartable$lhs[free.var.idx]
# for(v in seq_len(length(free.var.idx))) {
# # single marker item?
# ind.idx <- which(lavpartable$op == "=~" &
# lavpartable$lhs %in% this.lv.names[v] &
# #lavpartable$rhs %in% ov.names.num &
# lavpartable$free == 0L &
# lavpartable$group == group.values[g])
# if(length(ind.idx) == 0) {
# next
# } else if(length(ind.idx) > 1L) {
# # FIXME! perhaps a random effect? do something clever
# next
# } else if(length(ind.idx) == 1L) {
# marker.ind <- lavpartable$rhs[ind.idx]
# ov.idx <- match(marker.ind, ov.names)
# if(conditional.x) {
# ov.var <- diag(lavsamplestats@res.cov[[g]])[ov.idx]
# } else {
# ov.var <- diag(lavsamplestats@cov[[g]])[ov.idx]
# }
#
# # exogenous? assume rel = 0.50
# lambda <- lavpartable$ustart[ind.idx]
# tmp <- (0.50 * ov.var)/lambda^2
# if(this.lv.names[v] %in% lv.names.y) {
# # endogenous, assume R2 = 0.2
# tmp <- 0.8 * tmp
# }
# # within variance?
# if(nlevels > 1L &&
# lavpartable$level[ free.var.idx[v] ] == 1L) {
# tmp <- tmp * 0.75
# }
# # between variance?
# if(nlevels > 1L &&
# lavpartable$level[ free.var.idx[v] ] > 1L) {
# tmp <- tmp * 0.25
# }
# # just in case
# if(is.na(tmp) || tmp < 0.05) {
# tmp <- 0.05
# }
# start[ free.var.idx[v] ] <- tmp
# }
# } # v
# } # free.var.idx
# } # lv var
# nlevels > 1L
if (nlevels > 1L) {
level.values <- lav_partable_level_values(lavpartable)
# Note: ov.names.x contains all levels within a group!
if (length(ov.names.x) > 0) {
for (l in 1:nlevels) {
# block number
block <- (g - 1L) * nlevels + l
this.block.x <- lav_partable_vnames(lavpartable, "ov.x",
block = block
)
this.block.ov <- lav_partable_vnames(lavpartable, "ov",
block = block
)
if (length(this.block.x) == 0L) {
next
}
# var/cov
exo.idx <- which(lavpartable$group == group.values[g] &
lavpartable$level == level.values[l] &
lavpartable$op == "~~" &
lavpartable$lhs %in% this.block.x &
lavpartable$rhs %in% this.block.x)
if (is.null(lavh1$implied$cov[[1]])) {
row.idx <- match(lavpartable$lhs[exo.idx], ov.names)
col.idx <- match(lavpartable$rhs[exo.idx], ov.names)
if (l == 1L) {
COV <- lavsamplestats@YLp[[g]][[2]]$S.PW.start
} else {
COV <- lavsamplestats@YLp[[g]][[l]]$Sigma.B
}
} else {
row.idx <- match(lavpartable$lhs[exo.idx], this.block.ov)
col.idx <- match(lavpartable$rhs[exo.idx], this.block.ov)
COV <- lavh1$implied$cov[[block]]
}
# make sure starting values for variances are positive
neg.idx <- which(diag(COV) < 0.001)
if (length(neg.idx) > 0L) {
diag(COV)[neg.idx] <- 0.001
}
start[exo.idx] <- COV[cbind(row.idx, col.idx)]
# intercepts
ov.int.idx <- which(lavpartable$group == group.values[g] &
lavpartable$level == level.values[l] &
lavpartable$op == "~1" &
lavpartable$lhs %in% this.block.x)
if (is.null(lavh1$implied$mean[[1]])) {
idx <- match(lavpartable$lhs[ov.int.idx], ov.names)
if (l == 1L) {
INT <- lavsamplestats@YLp[[g]][[2]]$Mu.W
} else {
INT <- lavsamplestats@YLp[[g]][[l]]$Mu.B.start
}
} else {
idx <- match(lavpartable$lhs[ov.int.idx], this.block.ov)
INT <- lavh1$implied$mean[[block]]
}
start[ov.int.idx] <- INT[idx]
# new in 0.6-12
# very special case: conditional.x with a combination of
# splitted-x and regular-x
# here, we must:
# 1) replace var/cov of splitted-x by *residual* varcov
# after regressing out regular-x
# 2) replace means of splitted-x by intercepts
# 3) fill splitted-x ~ regular-x regression coefficients
if (conditional.x) {
if (is.null(lavh1$implied$cov[[l]])) {
lav_msg_stop(gettext(
"lavh1 information is needed; please rerun with h1 = TRUE"))
}
blocks.within.group <- (g - 1L) * nlevels + seq_len(nlevels)
OTHER.BLOCK.NAMES <- lav_partable_vnames(lavpartable, "ov",
block = blocks.within.group[-block])
ov.names.x.block <- this.block.x
idx <- which(ov.names.x.block %in% OTHER.BLOCK.NAMES)
if (length(idx) > 0L) {
ov.names.x.block <- ov.names.x.block[-idx]
}
ov.names.x1 <- this.block.x[!this.block.x %in% ov.names.x.block]
ov.names.x2 <- ov.names.x.block
nx1 <- length(ov.names.x1) # splitted x
nx2 <- length(ov.names.x2) # regular x
if (nx1 > 0L && nx2 > 0L) {
# COV
c1.idx <- match(ov.names.x1, this.block.ov)
c2.idx <- match(ov.names.x2, this.block.ov)
COV.Y <- COV[c1.idx, c1.idx, drop = FALSE]
COV.X <- COV[c2.idx, c2.idx, drop = FALSE]
COV.YX <- COV[c1.idx, c2.idx, drop = FALSE]
COV.XY <- COV[c2.idx, c1.idx, drop = FALSE]
COV.XinvYX <- solve(COV.X, COV.XY)
RES.COV <- COV.Y - COV.YX %*% COV.XinvYX
res.cov.idx <- which(lavpartable$group == group.values[g] &
lavpartable$level == level.values[l] &
lavpartable$op == "~~" &
lavpartable$lhs %in% ov.names.x1 &
lavpartable$rhs %in% ov.names.x1)
row.idx <- match(lavpartable$lhs[res.cov.idx], ov.names.x1)
col.idx <- match(lavpartable$rhs[res.cov.idx], ov.names.x1)
start[res.cov.idx] <- RES.COV[cbind(row.idx, col.idx)]
# INT
INT.Y <- INT[c1.idx]
INT.X <- INT[c2.idx]
RES.INT <- INT.Y - t(COV.XinvYX) %*% INT.X
res.int.idx <- which(lavpartable$group == group.values[g] &
lavpartable$level == level.values[l] &
lavpartable$op == "~1" &
lavpartable$lhs %in% ov.names.x1)
idx <- match(lavpartable$lhs[res.int.idx], ov.names.x1)
start[res.int.idx] <- RES.INT[idx]
# REG
reg.idx <- which(lavpartable$group == group.values[g] &
lavpartable$level == level.values[l] &
lavpartable$op == "~" &
lavpartable$lhs %in% ov.names.x1 &
lavpartable$rhs %in% ov.names.x2)
row.idx <- match(lavpartable$lhs[reg.idx], ov.names.x1)
col.idx <- match(lavpartable$rhs[reg.idx], ov.names.x2)
start[reg.idx] <- t(COV.XinvYX)[cbind(row.idx, col.idx)]
} # special case
} # conditional.x
} # levels
} # fixed.x
} # nlevels > 1L
} # groups
# group weights
group.idx <- which(lavpartable$lhs == "group" &
lavpartable$op == "%")
if (length(group.idx) > 0L) {
ngroups <- length(group.idx)
# prop <- rep(1/ngroups, ngroups)
# use last group as reference
# start[group.idx] <- log(prop/prop[ngroups])
# poisson version
start[group.idx] <- log(rep(lavsamplestats@ntotal / ngroups, ngroups))
}
# growth models:
# - compute starting values for mean latent variables
# - compute starting values for variance latent variables
if (start.initial %in% c("lavaan", "mplus") &&
model.type == "growth") {
### DEBUG ONLY
# lv.var.idx <- which(lavpartable$op == "~~" &
# lavpartable$lhs %in% lv.names &
# lavpartable$lhs == lavpartable$rhs)
### DEBUG ONLY
# lv.int.idx <- which(lavpartable$op == "~1" &
# lavpartable$lhs %in% lv.names)
}
# adjust if outside bounds -- new in 0.6-6
if (!is.null(lavpartable$lower)) {
bad.idx <- which(start < lavpartable$lower)
if (length(bad.idx)) {
start[bad.idx] <- lavpartable$lower[bad.idx]
}
}
if (!is.null(lavpartable$upper)) {
bad.idx <- which(start > lavpartable$upper)
if (length(bad.idx)) {
start[bad.idx] <- lavpartable$upper[bad.idx]
}
}
# override if the model syntax contains explicit starting values (free only)
# user.idx <- which(!is.na(lavpartable$ustart) &
# lavpartable$user != 7L) # new in 0.6-7, if rotation and
# # and we change the order of lv's
user.idx <- which(!is.na(lavpartable$ustart) & lavpartable$free > 0L)
start[user.idx] <- lavpartable$ustart[user.idx]
# override if a user list with starting values is provided
# we only look at the 'est' column for now
if (!is.null(start.user)) {
if (is.null(lavpartable$group)) {
lavpartable$group <- rep(1L, length(lavpartable$lhs))
}
if (is.null(start.user$group)) {
start.user$group <- rep(1L, length(start.user$lhs))
}
# FIXME: avoid for loop!!!
for (i in seq_along(lavpartable$lhs)) {
# find corresponding parameters
lhs <- lavpartable$lhs[i]
op <- lavpartable$op[i]
rhs <- lavpartable$rhs[i]
grp <- lavpartable$group[i]
start.user.idx <- which(start.user$lhs == lhs &
start.user$op == op &
start.user$rhs == rhs &
start.user$group == grp)
if (length(start.user.idx) == 1L &&
is.finite(start.user$est[start.user.idx])) {
start[i] <- start.user$est[start.user.idx]
}
}
}
# override fixed values with ustart values
user.idx <- which(!is.na(lavpartable$ustart) & lavpartable$free == 0L)
start[user.idx] <- lavpartable$ustart[user.idx]
# final check: no NaN or other non-finite values
bad.idx <- which(!is.finite(start))
if (length(bad.idx) > 0L) {
cat("starting values:\n")
print(start)
lav_msg_warn(gettext(
"some starting values are non-finite; replacing them with 0.5;
please provide better starting values."))
start[bad.idx] <- 0.5
}
if (lav_debug()) {
cat("lavaan DEBUG: lavaanStart\n")
print(start)
}
start
}
# backwards compatibility
# StartingValues <- lav_start
# sanity check: (user-specified) variances smaller than covariances
lav_start_check_cov <- function(lavpartable = NULL, start = lavpartable$start) {
nblocks <- lav_partable_nblocks(lavpartable)
block.values <- lav_partable_block_values(lavpartable)
for (g in 1:nblocks) {
# collect all non-zero covariances
cov.idx <- which(lavpartable$op == "~~" &
lavpartable$block == block.values[g] &
lavpartable$lhs != lavpartable$rhs &
!lavpartable$exo &
start != 0)
# for each covariance, use corresponding variances to standardize;
# the end result should not exceed abs(1)
for (cc in seq_along(cov.idx)) {
this.cov.idx <- cov.idx[cc]
# find corresponding variances
var.lhs <- lavpartable$lhs[this.cov.idx]
var.rhs <- lavpartable$rhs[this.cov.idx]
var.lhs.idx <- which(lavpartable$op == "~~" &
lavpartable$block == block.values[g] &
lavpartable$lhs == var.lhs &
lavpartable$lhs == lavpartable$rhs)
var.rhs.idx <- which(lavpartable$op == "~~" &
lavpartable$block == block.values[g] &
lavpartable$lhs == var.rhs &
lavpartable$lhs == lavpartable$rhs)
var.lhs.value <- start[var.lhs.idx]
var.rhs.value <- start[var.rhs.idx]
block.txt <- ""
if (nblocks > 1L) {
block.txt <- paste(" [in block ", g, "]", sep = "")
}
# check for zero variances
if (var.lhs.value == 0 || var.rhs.value == 0) {
# this can only happen if it is user-specified
# cov.idx free? set it to zero
if (start[this.cov.idx] == 0) {
# nothing to do
} else if (lavpartable$free[this.cov.idx] > 0L) {
lav_msg_warn(gettextf(
"non-zero covariance element set to zero, due to fixed-to-zero
variances variables involved are: %s", var.lhs), var.rhs,
block.txt
)
start[this.cov.idx] <- 0
} else {
lav_msg_stop(gettextf(
"please provide better fixed values for (co)variances;
variables involved are: %s ", var.lhs), var.rhs, block.txt
)
}
next
}
# which one is the smallest? abs() in case of negative variances
if (abs(var.lhs.value) < abs(var.rhs.value)) {
var.min.idx <- var.lhs.idx
var.max.idx <- var.rhs.idx
} else {
var.min.idx <- var.rhs.idx
var.max.idx <- var.lhs.idx
}
# check
COR <- abs(start[this.cov.idx] / sqrt(var.lhs.value * var.rhs.value))
# NOTE: we treat this as an unconditional COR!
if (!is.finite(COR)) {
# force simple values
lav_msg_warn(gettextf(
"starting values imply NaN for a correlation value; variables
involved are: %s", var.lhs), var.rhs, block.txt
)
start[var.lhs.idx] <- 1
start[var.rhs.idx] <- 1
start[this.cov.idx] <- 0
} else if (COR > 1) {
txt <- gettextf(
"starting values imply a correlation larger than 1; variables
involved are: %1$s %2$s %3$s", var.lhs, var.rhs, block.txt)
# three ways to fix it: rescale cov12, var1 or var2
# we prefer a free parameter, and not user-specified
if (lavpartable$free[this.cov.idx] > 0L &&
is.na(lavpartable$ustart[this.cov.idx])) {
lav_msg_warn(gettext(txt))
start[this.cov.idx] <- start[this.cov.idx] / (COR * 1.1)
} else if (lavpartable$free[var.min.idx] > 0L &&
is.na(lavpartable$ustart[var.min.idx])) {
lav_msg_warn(gettext(txt))
start[var.min.idx] <- start[var.min.idx] * (COR * 1.1)^2
} else if (lavpartable$free[var.max.idx] > 0L &&
is.na(lavpartable$ustart[var.max.idx])) {
lav_msg_warn(gettext(txt))
start[var.max.idx] <- start[var.max.idx] * (COR * 1.1)^2
# not found? try just a free parameter
} else if (lavpartable$free[this.cov.idx] > 0L) {
lav_msg_warn(gettext(txt))
start[this.cov.idx] <- start[this.cov.idx] / (COR * 1.1)
} else if (lavpartable$free[var.min.idx] > 0L) {
lav_msg_warn(gettext(txt))
start[var.min.idx] <- start[var.min.idx] * (COR * 1.1)^2
} else if (lavpartable$free[var.max.idx] > 0L) {
lav_msg_warn(gettext(txt))
start[var.max.idx] <- start[var.max.idx] * (COR * 1.1)^2
# nothing? abort or warn (and fail later...): warn
} else {
lav_msg_warn(gettext(txt))
# lav_msg_stop(gettextf(
# "please provide better fixed values for (co)variances;
# variables involved are: %s ", var.lhs), var.rhs, block.txt)
}
} # COR > 1
} # cov.idx
}
start
}
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