measurment invariance for categorical itens: "measurmentinvarianceCAT" vs measEQ.Syntax
João Marôco
modinv = 'Cp =~ SE1 + SE2 + SE3 + SE4 + SE5Em =~ SE6 + SE7 + SE8 + SE9 + SE10Co =~ SE11 + SE12 + SE13 + SE14 + SE15# fator de 2a ordem#eng =~ Cp + Em + Co'if use the old semTools' measurmentInvarianceCat:
grupo = "Country"measurementInvarianceCat(model = modinv, data = bd, group = grupo, method = "Satorra.Bentler.2010", missing ="listwise", ordered=it, strict = F, fit.measures = "default")where
it = c("SE1","SE2","SE3","SE4","SE5","SE6","SE7","SE8","SE9","SE10","SE11","SE12","SE13","SE14","SE15")
I get this results for the invariance of the first order constructs (note that my second order "eng" construct is commented with # in the modinv definition):
Measurement invariance models:
Model 1 : fit.configural
Model 2 : fit.loadings
Model 3 : fit.thresholds
Model 4 : fit.means
Scaled Chi Square Difference Test (method = "satorra.bentler.2010")
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
fit.configural 696 2171.1
fit.loadings 780 2627.3 247.68 84 < 2.2e-16 ***
fit.thresholds 1074 4414.4 1521.90 294 < 2.2e-16 ***
fit.means 1095 7890.1 269.26 21 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Fit measures:
cfi.scaled rmsea.scaled cfi.scaled.delta rmsea.scaled.delta
fit.configural 0.962 0.074 NA NA
fit.loadings 0.960 0.071 0.002 0.002
fit.thresholds 0.936 0.077 0.024 0.005
fit.means 0.900 0.095 0.036 0.018grupo="Country"test.seq <- c("thresholds","loadings","means")meq.list <- list()for (i in 0L:length(test.seq)) { if (i == 0L) { meq.label <- "configural" group.equal <- "" } else { meq.label <- test.seq[i] group.equal <- test.seq[1:i] } meq.list[[meq.label]] <- measEq.syntax(configural.model = modinv, data = bd, ordered = it, parameterization = "theta", #ID.fac = "ul", ID.cat = "Wu", group = grupo, group.equal = group.equal, return.fit = TRUE) }
compareFit(meq.list)I get an error that I can't understand:
Error in getMethod("summary", signature = "FitDiff") :
no method found for function 'summary' and signature FitDiff
Terrence Jorgensen
semTools_0.5-1.933 lavaan_0.6-5.1453 lavaan WARNING: some models have the same degrees of freedomjpma...@gmail.com
Dear Terrence,
Thanks for your reply.
My sessionInfo() returns:
semTools_0.5-1.934 lavaan_0.6-5.1453
and I still get the same error:
if (!is.null(dotdotdot$control$optim.force.converged)) {
dotdotdot$optim.force.converged <- dotdotdot$control$optim.force.converged
}
if (!is.null(dotdotdot$control$gradient)) {
dotdotdot$optim.gradient <- dotdotdot$control$gradient
}
if (!is.null(dotdotdot$gradient)) {
dotdotdot$optim.gradient <- dotdotdot$gradient
}
if (!is.null(dotdotdot$control$init_nelder_mead)) {
dotdotdot$optim.init_nelder_mead <- dotdotdot$control$init_nelder_mead
}
}
if (!is.null(slotParTable)) {
FLAT <- slotParTable
}
else if (is.character(model)) {
FLAT <- lavParseModelString(model)
}
else if (inherits(model, "formula")) {
tmp <- as.character(model)
if (tmp[1] == "~" && length(tmp) == 2L) {
warning("lavaan WARNING: model seems to be a formula; please enclose the model syntax between quotes")
model.bis <- paste("f =", paste(tmp, collapse = " "),
sep = "")
FLAT <- lavParseModelString(model.bis)
}
else if (tmp[1] == "~" && length(tmp) == 3L) {
warning("lavaan WARNING: model seems to be a formula; please enclose the model syntax between quotes")
model.bis <- paste(tmp[2], tmp[1], tmp[3])
FLAT <- lavParseModelString(model.bis)
}
else {
stop("lavaan ERROR: model seems to be a formula; please enclose the model syntax between quotes")
}
}
else if (inherits(model, "lavaan")) {
FLAT <- parTable(model)
}
else if (is.list(model)) {
if (!is.null(model$lhs) && !is.null(model$op) && !is.null(model$rhs) &&
!is.null(model$free)) {
FLAT <- model
if (!is.null(FLAT$block)) {
N <- length(FLAT$lhs)
if (length(FLAT$block) != N) {
FLAT$block <- FLAT$group
}
if (any(is.na(FLAT$block))) {
FLAT$block <- FLAT$group
}
}
else if (!is.null(FLAT$group)) {
FLAT$block <- FLAT$group
}
}
else {
bare.minimum <- c("lhs", "op", "rhs", "free")
missing.idx <- is.na(match(bare.minimum, names(model)))
missing.txt <- paste(bare.minimum[missing.idx], collapse = ", ")
stop("lavaan ERROR: model is a list, but not a parameterTable?",
"\n lavaan NOTE: ", "missing column(s) in parameter table: [",
missing.txt, "]")
}
}
else if (is.null(model)) {
stop("lavaan ERROR: model is NULL!")
}
if (any(FLAT$op == ":" & tolower(FLAT$lhs) == "group")) {
group.idx <- which(FLAT$op == ":" & tolower(FLAT$lhs) ==
"group")
FLAT$lhs[group.idx] <- "group"
tmp.group.values <- unique(FLAT$rhs[group.idx])
tmp.ngroups <- length(tmp.group.values)
tmp.lav <- lavaanify(FLAT, ngroups = tmp.ngroups, warn = FALSE)
ov.names <- ov.names.y <- ov.names.x <- vector("list",
length = tmp.ngroups)
for (g in seq_len(tmp.ngroups)) {
ov.names[[g]] <- unique(unlist(lav_partable_vnames(tmp.lav,
type = "ov", group = tmp.group.values[g])))
ov.names.y[[g]] <- unique(unlist(lav_partable_vnames(tmp.lav,
type = "ov.nox", group = tmp.group.values[g])))
ov.names.x[[g]] <- unique(unlist(lav_partable_vnames(tmp.lav,
type = "ov.x", group = tmp.group.values[g])))
}
}
else if (!is.null(FLAT$group)) {
ngroups <- lav_partable_ngroups(FLAT)
if (ngroups > 1L) {
group.values <- lav_partable_group_values(FLAT)
ov.names <- ov.names.y <- ov.names.x <- vector("list",
length = ngroups)
for (g in seq_len(ngroups)) {
ov.names[[g]] <- lav_partable_vnames(FLAT, type = "ov",
group = group.values[g])
ov.names.y[[g]] <- lav_partable_vnames(FLAT,
type = "ov.nox", group = group.values[g])
ov.names.x[[g]] <- lav_partable_vnames(FLAT,
type = "ov.x", group = group.values[g])
}
}
else {
ov.names <- lav_partable_vnames(FLAT, type = "ov")
ov.names.y <- lav_partable_vnames(FLAT, type = "ov.nox")
ov.names.x <- lav_partable_vnames(FLAT, type = "ov.x")
}
}
else {
ov.names <- lav_partable_vnames(FLAT, type = "ov")
ov.names.y <- lav_partable_vnames(FLAT, type = "ov.nox")
ov.names.x <- lav_partable_vnames(FLAT, type = "ov.x")
}
if (any(FLAT$op == ":" & tolower(FLAT$lhs) == "level")) {
if (!is.null(data) && is.null(cluster)) {
stop("lavaan ERROR: cluster argument is missing.")
}
group.idx <- which(FLAT$op == ":" & FLAT$lhs == "group")
tmp.group.values <- unique(FLAT$rhs[group.idx])
tmp.ngroups <- max(c(length(tmp.group.values), 1))
level.idx <- which(FLAT$op == ":" & tolower(FLAT$lhs) ==
"level")
FLAT$lhs[level.idx] <- "level"
tmp.level.values <- unique(FLAT$rhs[level.idx])
tmp.nlevels <- length(tmp.level.values)
if (tmp.nlevels < 2L) {
stop("lavaan ERROR: when data is clustered, you must specify a model\n",
" for each level in the model syntax (for now); see example(Demo.twolevel)")
}
tmp.lav <- lavaanify(FLAT, ngroups = tmp.ngroups, warn = FALSE)
if (max(tmp.lav$level) < 2L) {
stop("lavaan ERROR: at least one level has no model syntax; you must specify a model for each level in the model syntax (for now); see example(Demo.twolevel)")
}
ov.names.l <- vector("list", length = tmp.ngroups)
for (g in seq_len(tmp.ngroups)) {
ov.names.l[[g]] <- vector("list", length = tmp.nlevels)
for (l in seq_len(tmp.nlevels)) {
if (tmp.ngroups > 1L) {
ov.names.l[[g]][[l]] <- unique(unlist(lav_partable_vnames(tmp.lav,
type = "ov", group = tmp.group.values[g],
level = tmp.level.values[l])))
}
else {
ov.names.l[[g]][[l]] <- unique(unlist(lav_partable_vnames(tmp.lav,
type = "ov", level = tmp.level.values[l])))
}
}
}
}
else {
nlevels <- lav_partable_nlevels(FLAT)
if (nlevels > 1L) {
if (!is.null(data) && is.null(cluster)) {
stop("lavaan ERROR: cluster argument is missing.")
}
ngroups <- lav_partable_ngroups(FLAT)
ov.names.l <- vector("list", length = ngroups)
for (g in 1:ngroups) {
ov.names.l[[g]] <- lavNames(FLAT, "ov", group = g)
}
}
else {
ov.names.l <- list()
}
}
if (!is.null(ordered)) {
if (is.logical(ordered) && ordered) {
ordered <- lavNames(FLAT, "ov")
}
else if (is.logical(ordered) && !ordered) {
ordered <- character(0L)
}
else if (!is.character(ordered)) {
stop("lavaan ERROR: ordered argument must be a character vector")
}
else {
if (!is.null(data)) {
missing.idx <- which(!ordered %in% names(data))
if (length(missing.idx) > 0L) {
warning("lavaan WARNING: ordered variable(s): ",
paste(ordered[missing.idx], collapse = " "),
"\n could not be found in the data and will be ignored")
}
}
}
}
ordered <- unique(c(ordered, lavNames(FLAT, "ov.ord")))
if (!is.null(slotOptions)) {
lavoptions <- slotOptions
if (length(dotdotdot) > 0L) {
dot.names <- names(dotdotdot)
op.idx <- which(dot.names %in% names(slotOptions))
warning("lavaan WARNING: the following argument(s) override(s) the options in slotOptions:\n\t\t",
paste(dot.names[op.idx], collapse = " "))
lavoptions[dot.names[op.idx]] <- dotdotdot[op.idx]
}
}
else {
opt <- lav_options_default()
ok.names <- names(opt)
dot.names <- names(dotdotdot)
wrong.idx <- which(!dot.names %in% ok.names)
if (length(wrong.idx) > 0L) {
idx <- wrong.idx[1L]
stop("lavaan ERROR: unknown argument `", dot.names[idx],
"'")
}
opt <- modifyList(opt, dotdotdot)
if (any(FLAT$op == "|")) {
opt$categorical <- TRUE
}
else if (!is.null(data) && length(ordered) > 0L) {
opt$categorical <- TRUE
}
else if (!is.null(sample.th)) {
opt$categorical <- TRUE
}
else if (is.data.frame(data) && lav_dataframe_check_ordered(frame = data,
ov.names = ov.names.y)) {
opt$categorical <- TRUE
}
else {
opt$categorical <- FALSE
}
if (length(cluster) > 0L) {
opt$clustered <- TRUE
}
else {
opt$clustered <- FALSE
}
if (length(ov.names.l) > 0L && length(ov.names.l[[1]]) >
1L) {
opt$multilevel <- TRUE
}
else {
opt$multilevel <- FALSE
}
if (!is.null(sampling.weights)) {
opt$estimator <- "MLR"
}
if (any(nchar(constraints) > 0L) && opt$estimator %in%
c("ML")) {
opt$information <- "observed"
}
if (any(FLAT$op == "~1") || !is.null(sample.mean)) {
opt$meanstructure <- TRUE
}
if (!is.null(group) && is.null(dotdotdot$meanstructure)) {
opt$meanstructure <- TRUE
}
if ((is.list(ov.names.x) && sum(sapply(ov.names.x, FUN = length)) ==
0L) || (is.character(ov.names.x) && length(ov.names.x) ==
0L)) {
if (is.logical(dotdotdot$conditional.x) && dotdotdot$conditional.x) {
warning("lavaan WARNING: no exogenous covariates; conditional.x will be set to FALSE")
}
opt$conditional.x <- FALSE
}
if ((is.list(ov.names.x) && sum(sapply(ov.names.x, FUN = length)) ==
0L) || (is.character(ov.names.x) && length(ov.names.x) ==
0L)) {
if (is.logical(dotdotdot$fixed.x) && dotdotdot$fixed.x) {
}
else {
opt$fixed.x <- FALSE
}
}
lavoptions <- lav_options_set(opt)
}
timing$Options <- (proc.time()[3] - start.time)
start.time <- proc.time()[3]
if (!lavoptions$fixed.x) {
ov.names.x <- character(0L)
}
if (!is.null(slotData)) {
lavdata <- slotData
}
else {
OV.NAMES <- if (lavoptions$conditional.x) {
ov.names.y
}
else {
ov.names
}
lavdata <- lavData(data = data, group = group, cluster = cluster,
ov.names = OV.NAMES, ov.names.x = ov.names.x, ov.names.l = ov.names.l,
ordered = ordered, sampling.weights = sampling.weights,
sample.cov = sample.cov, sample.mean = sample.mean,
sample.th = sample.th, sample.nobs = sample.nobs,
lavoptions = lavoptions)
}
if (lav...@data.type == "none") {
lavoptions$do.fit <- FALSE
lavoptions$start <- "simple"
lavoptions$se <- "none"
lavoptions$test <- "none"
}
else if (lav...@data.type == "moment") {
if (!is.null(dotdotdot$estimator)) {
if (dotdotdot$estimator %in% c("MLM", "MLMV", "MLR",
"MLR", "ULSM", "ULSMV", "ULSMVS") && is.null(NACOV)) {
stop("lavaan ERROR: estimator ", dotdotdot$estimator,
" requires full data or user-provided NACOV")
}
else if (dotdotdot$estimator %in% c("WLS", "WLSM",
"WLSMV", "WLSMVS", "DWLS") && is.null(WLS.V)) {
stop("lavaan ERROR: estimator ", dotdotdot$estimator,
" requires full data or user-provided WLS.V and NACOV")
}
}
if (lavoptions$se == "bootstrap") {
stop("lavaan ERROR: bootstrapping requires full data")
}
}
timing$Data <- (proc.time()[3] - start.time)
start.time <- proc.time()[3]
if (lavoptions$debug) {
print(str(lavdata))
}
if (!is.null(slotParTable)) {
lavpartable <- slotParTable
}
else if (is.character(model) || inherits(model, "formula")) {
if (lavoptions$debug) {
print(as.data.frame(FLAT))
}
if (lavoptions$fixed.x) {
tmp <- try(vnames(FLAT, type = "ov.x", ov.x.fatal = TRUE),
silent = TRUE)
if (inherits(tmp, "try-error")) {
warning("lavaan WARNING: syntax contains parameters involving exogenous covariates; switching to fixed.x = FALSE")
lavoptions$fixed.x <- FALSE
}
}
if (lavoptions$conditional.x) {
tmp <- vnames(FLAT, type = "ov.x", ov.x.fatal = TRUE)
}
lavpartable <- lavaanify(model = FLAT, constraints = constraints,
varTable = lavdata@ov, ngroups = lavdata@ngroups,
meanstructure = lavoptions$meanstructure, int.ov.free = lavoptions$int.ov.free,
int.lv.free = lavoptions$int.lv.free, orthogonal = lavoptions$orthogonal,
orthogonal.x = lavoptions$orthogonal.x, orthogonal.y = lavoptions$orthogonal.y,
orthogonal.efa = lavoptions$rotation.args$orthogonal,
conditional.x = lavoptions$conditional.x, fixed.x = lavoptions$fixed.x,
std.lv = lavoptions$std.lv, effect.coding = lavoptions$effect.coding,
parameterization = lavoptions$parameterization, auto.fix.first = lavoptions$auto.fix.first,
auto.fix.single = lavoptions$auto.fix.single, auto.var = lavoptions$auto.var,
auto.cov.lv.x = lavoptions$auto.cov.lv.x, auto.cov.y = lavoptions$auto.cov.y,
auto.th = lavoptions$auto.th, auto.delta = lavoptions$auto.delta,
auto.efa = lavoptions$auto.efa, group.equal = lavoptions$group.equal,
group.partial = lavoptions$group.partial, group.w.free = lavoptions$group.w.free,
debug = lavoptions$debug, warn = lavoptions$warn,
as.data.frame. = FALSE)
}
else if (inherits(model, "lavaan")) {
lavpartable <- parTable(model)
}
else if (is.list(model)) {
lavpartable <- as.list(FLAT)
lavpartable <- lav_partable_complete(lavpartable)
}
else {
stop("lavaan ERROR: model [type = ", class(model), "] is not of type character or list")
}
if (lavoptions$debug) {
print(as.data.frame(lavpartable))
}
junk <- lav_partable_check(lavpartable, categorical = lavoptions$categorical,
warn = TRUE)
if (lavoptions$optim.method == "em") {
zero.var.idx <- which(lavpartable$op == "~~" & lavpartable$lhs ==
lavpartable$rhs & lavpartable$free == 0L & lavpartable$ustart ==
0)
if (length(zero.var.idx) > 0L) {
lavpartable$ustart[zero.var.idx] <- lavoptions$em.zerovar.offset
}
}
lavpta <- lav_partable_attributes(lavpartable)
timing$ParTable <- (proc.time()[3] - start.time)
start.time <- proc.time()[3]
if (!is.null(slotSampleStats)) {
lavsamplestats <- slotSampleStats
}
else if (lav...@data.type == "full") {
lavsamplestats <- lav_samplestats_from_data(lavdata = lavdata,
missing = lavoptions$missing, rescale = (lavoptions$estimator %in%
c("ML", "REML", "NTRLS") && lavoptions$likelihood ==
"normal"), estimator = lavoptions$estimator,
mimic = lavoptions$mimic, meanstructure = lavoptions$meanstructure,
conditional.x = lavoptions$conditional.x, fixed.x = lavoptions$fixed.x,
group.w.free = lavoptions$group.w.free, missing.h1 = (lavoptions$missing !=
"listwise"), WLS.V = WLS.V, NACOV = NACOV, gamma.n.minus.one = lavoptions$gamma.n.minus.one,
se = lavoptions$se, information = lavoptions$information,
ridge = lavoptions$ridge, optim.method = lavoptions$optim.method.cor,
zero.add = lavoptions$zero.add, zero.keep.margins = lavoptions$zero.keep.margins,
zero.cell.warn = lavoptions$zero.cell.warn, debug = lavoptions$debug,
verbose = lavoptions$verbose)
}
else if (lav...@data.type == "moment") {
lavsamplestats <- lav_samplestats_from_moments(sample.cov = sample.cov,
sample.mean = sample.mean, sample.th = sample.th,
sample.nobs = sample.nobs, ov.names = ov.names, ov.names.x = ov.names.x,
estimator = lavoptions$estimator, mimic = lavoptions$mimic,
group.w.free = lavoptions$group.w.free, WLS.V = WLS.V,
NACOV = NACOV, ridge = lavoptions$ridge, rescale = lavoptions$sample.cov.rescale)
}
else {
lavsamplestats <- new("lavSampleStats", ngroups = lavdata@ngroups,
nobs = as.list(rep(0L, lavdata@ngroups)), th.idx = lavpta$th.idx,
missing.flag = FALSE)
}
timing$SampleStats <- (proc.time()[3] - start.time)
start.time <- proc.time()[3]
if (lavoptions$debug) {
print(str(lavsamplestats))
}
if (!is.null(sloth1)) {
lavh1 <- sloth1
}
else {
lavh1 <- list()
if (is.logical(lavoptions$h1) && lavoptions$h1) {
if (length(lavsamplestats@ntotal) > 0L) {
out <- lav_h1_implied_logl(lavdata = lavdata,
lavsamplestats = lavsamplestats, lavoptions = lavoptions)
h1.implied <- out$implied
h1.loglik <- out$logl$loglik
h1.loglik.group <- out$logl$loglik.group
lavh1 <- list(implied = h1.implied, loglik = h1.loglik,
loglik.group = h1.loglik.group)
}
else {
}
}
else {
if (!is.logical(lavoptions$h1)) {
stop("lavaan ERROR: argument `h1' must be logical (for now)")
}
}
}
timing$h1 <- (proc.time()[3] - start.time)
start.time <- proc.time()[3]
if (!is.null(slotModel)) {
lavmodel <- slotModel
timing$start <- (proc.time()[3] - start.time)
start.time <- proc.time()[3]
timing$Model <- (proc.time()[3] - start.time)
start.time <- proc.time()[3]
}
else {
if (!is.null(lavpartable$est) && lavoptions$start ==
"default") {
zero.idx <- which(lavpartable$free > 0L & lavpartable$op ==
"~~" & lavpartable$lhs == lavpartable$rhs & lavpartable$est ==
0)
if (length(zero.idx) > 0L || any(is.na(lavpartable$est))) {
lavpartable$start <- lav_start(start.method = lavoptions$start,
lavpartable = lavpartable, lavsamplestats = lavsamplestats,
model.type = lavoptions$model.type, mimic = lavoptions$mimic,
debug = lavoptions$debug)
}
else {
lavpartable$start <- lavpartable$est
}
}
else {
START <- lav_start(start.method = lavoptions$start,
lavpartable = lavpartable, lavsamplestats = lavsamplestats,
model.type = lavoptions$model.type, mimic = lavoptions$mimic,
debug = lavoptions$debug)
if (!is.null(lavoptions$check.start) && lavoptions$check.start) {
START <- lav_start_check_cov(lavpartable = lavpartable,
start = START)
}
lavpartable$start <- START
}
timing$start <- (proc.time()[3] - start.time)
start.time <- proc.time()[3]
lavmodel <- lav_model(lavpartable = lavpartable, lavoptions = lavoptions,
th.idx = lavsamp...@th.idx, cov.x = lavsamplestats@cov.x,
mean.x = lavsamplestats@mean.x)
timing$Model <- (proc.time()[3] - start.time)
start.time <- proc.time()[3]
if (lav...@data.type == "none" && lavmodel@categorical) {
lavmodel <- lav_model_set_parameters(lavmodel = lavmodel,
x = lav_model_get_parameters(lavmodel))
lavpartable$start <- lav_model_get_parameters(lavmodel,
type = "user")
if (!all(lavpartable$start == lavpartable$ustart)) {
if (lavmodel@parameterization == "delta") {
user.var.idx <- which(lavpartable$op == "~~" &
lavpartable$lhs == lavpartable$rhs & lavpartable$lhs %in%
unlist(lavpta$vnames$ov.ord) & lavpartable$user ==
1L)
if (length(user.var.idx)) {
warning("lavaan WARNING: ", "variance (theta) values for categorical variables are ignored",
"\n\t\t if parameterization = \"delta\"!")
}
}
else if (lavmodel@parameterization == "theta") {
user.delta.idx <- which(lavpartable$op == "~*~" &
lavpartable$lhs == lavpartable$rhs & lavpartable$lhs %in%
unlist(lavpta$vnames$ov.ord) & lavpartable$user ==
1L)
if (length(user.delta.idx)) {
warning("lavaan WARNING: ", "scaling (~*~) values for categorical variables are ignored",
"\n\t\t if parameterization = \"theta\"!")
}
}
}
}
}
if (!is.null(slotCache)) {
lavcache <- slotCache
}
else {
lavcache <- vector("list", length = lavdata@ngroups)
ov.types <- lavdata@ov$type
if (lavmodel@conditional.x && sum(lavmodel@nexo) > 0L) {
ov.x.idx <- unlist(lavpta$vidx$ov.x)
ov.types <- ov.types[-ov.x.idx]
}
if (lavoptions$estimator == "PML" && all(ov.types ==
"ordered")) {
TH <- computeTH(lavmodel)
BI <- lav_tables_pairwise_freq_cell(lavdata)
if (lavoptions$missing == "available.cases" || lavoptions$missing ==
"doubly.robust") {
UNI <- lav_tables_univariate_freq_cell(lavdata)
}
if (lavoptions$missing == "doubly.robust") {
if (is.null(lavoptions$control$pairwiseProbGivObs)) {
stop("lavaan ERROR: could not find `pairwiseProbGivObs' in control() list")
}
if (is.null(lavoptions$control$univariateProbGivObs)) {
stop("lavaan ERROR: could not find `univariateProbGivObs' in control() list")
}
}
for (g in 1:lavdata@ngroups) {
if (is.null(BI$group) || max(BI$group) == 1L) {
bifreq <- BI$obs.freq
binobs <- BI$nobs
}
else {
idx <- which(BI$group == g)
bifreq <- BI$obs.freq[idx]
binobs <- BI$nobs[idx]
}
LONG <- LongVecInd(no.x = ncol(lavdata@X[[g]]),
all.thres = TH[[g]], index.var.of.thres = lavm...@th.idx[[g]])
lavcache[[g]] <- list(bifreq = bifreq, nobs = binobs,
LONG = LONG)
if (lavoptions$missing == "available.cases" ||
lavoptions$missing == "doubly.robust") {
if (is.null(UNI$group) || max(UNI$group) ==
1L) {
unifreq <- UNI$obs.freq
uninobs <- UNI$nobs
}
else {
idx <- which(UNI$group == g)
unifreq <- UNI$obs.freq[idx]
uninobs <- UNI$nobs[idx]
}
lavcache[[g]]$unifreq <- unifreq
lavcache[[g]]$uninobs <- uninobs
uniweights.casewise <- rowSums(is.na(lavdata@X[[g]]))
lavcache[[g]]$uniweights.casewise <- uniweights.casewise
tmp.uniweights <- apply(lavdata@X[[g]], 2,
function(x) {
tapply(uniweights.casewise, as.factor(x),
sum, na.rm = TRUE)
})
if (is.matrix(tmp.uniweights)) {
lavcache[[g]]$uniweights <- c(tmp.uniweights)
}
if (is.list(tmp.uniweights)) {
lavcache[[g]]$uniweights <- unlist(tmp.uniweights)
}
}
if (lavoptions$missing == "doubly.robust") {
lavcache[[g]]$pairwiseProbGivObs <- lavoptions$control$pairwiseProbGivObs[[g]]
lavcache[[g]]$univariateProbGivObs <- lavoptions$control$univariateProbGivObs[[g]]
ind.vec <- as.data.frame(LONG[1:5])
ind.vec <- ind.vec[((ind.vec$index.thres.var1.of.pair !=
0) & (ind.vec$index.thres.var2.of.pair !=
0)), ]
idx.cat.y1 <- ind.vec$index.thres.var1.of.pair
idx.cat.y2 <- ind.vec$index.thres.var2.of.pair
idx.pairs <- ind.vec$index.pairs.extended
lavcache[[g]]$idx.pairs <- idx.pairs
idx.cat.y1.split <- split(idx.cat.y1, idx.pairs)
idx.cat.y2.split <- split(idx.cat.y2, idx.pairs)
lavcache[[g]]$idx.cat.y1.split <- idx.cat.y1.split
lavcache[[g]]$idx.cat.y2.split <- idx.cat.y2.split
nlev <- lavdata@ov$nlev
nvar <- length(nlev)
idx.var.matrix <- matrix(1:nvar, nrow = nvar,
ncol = nvar)
idx.diag <- diag(matrix(1:(nvar * nvar), nrow = nvar,
ncol = nvar))
idx.Y1Gy2.matrix <- rbind(t(idx.var.matrix)[-idx.diag],
idx.var.matrix[-idx.diag])
no.pairs.Y1Gy2 <- ncol(idx.Y1Gy2.matrix)
idx.cat.Y1 <- unlist(lapply(1:no.pairs.Y1Gy2,
function(x) {
rep(1:nlev[idx.Y1Gy2.matrix[1, x]], times = nlev[idx.Y1Gy2.matrix[2,
x]])
}))
idx.cat.Gy2 <- unlist(lapply(1:no.pairs.Y1Gy2,
function(x) {
rep(1:nlev[idx.Y1Gy2.matrix[2, x]], each = nlev[idx.Y1Gy2.matrix[1,
x]])
}))
dim.pairs <- unlist(lapply(1:no.pairs.Y1Gy2,
function(x) {
nlev[idx.Y1Gy2.matrix[1, x]] * nlev[idx.Y1Gy2.matrix[2,
x]]
}))
idx.Y1 <- unlist(mapply(rep, idx.Y1Gy2.matrix[1,
], each = dim.pairs))
idx.Gy2 <- unlist(mapply(rep, idx.Y1Gy2.matrix[2,
], each = dim.pairs))
lavcache[[g]]$idx.Y1 <- idx.Y1
lavcache[[g]]$idx.Gy2 <- idx.Gy2
lavcache[[g]]$idx.cat.Y1 <- idx.cat.Y1
lavcache[[g]]$idx.cat.Gy2 <- idx.cat.Gy2
lavcache[[g]]$id.uniPrGivObs <- sort(c(unique(lavm...@th.idx[[g]]),
lavm...@th.idx[[g]]))
}
}
}
if (lav...@data.type == "full" && !is.null(lavdata@Rp[[1L]])) {
for (g in 1:lavdata@ngroups) {
lavcache[[g]]$pat <- lavdata@Rp[[g]]$pat
}
}
}
if (lavoptions$estimator == "MML") {
for (g in 1:lavdata@ngroups) {
nfac <- lavpta$nfac[[g]]
lavcache[[g]]$GH <- lav_integration_gauss_hermite(n = lavoptions$integration.ngh,
dnorm = TRUE, mean = 0, sd = 1, ndim = nfac)
}
}
timing$cache <- (proc.time()[3] - start.time)
start.time <- proc.time()[3]
x <- NULL
if (lavoptions$do.fit && lavoptions$estimator != "none" &&
lavm...@nx.free > 0L) {
if (lavoptions$optim.method == "em") {
stopifnot(lavdata@nlevels > 1L)
x <- lav_mvnorm_cluster_em_h0(lavsamplestats = lavsamplestats,
lavdata = lavdata, lavimplied = NULL, lavpartable = lavpartable,
lavmodel = lavmodel, lavoptions = lavoptions,
verbose = lavoptions$verbose, fx.tol = lavoptions$em.fx.tol,
dx.tol = lavoptions$em.dx.tol, max.iter = lavoptions$em.iter.max)
}
else {
x <- lav_model_estimate(lavmodel = lavmodel, lavpartable = lavpartable,
lavsamplestats = lavsamplestats, lavdata = lavdata,
lavoptions = lavoptions, lavcache = lavcache)
}
if (!is.null(attr(x, "con.jac")))
lavm...@con.jac <- attr(x, "con.jac")
if (!is.null(attr(x, "con.lambda")))
lavm...@con.lambda <- attr(x, "con.lambda")
if ((.hasSlot(lavmodel, "nefa")) && (lavmodel@nefa >
0L) && (lavoptions$rotation != "none")) {
x.unrotated <- as.numeric(x)
tmp <- lav_model_set_parameters(lavmodel, x = as.numeric(x))
lavpartable$est.unrotated <- lav_model_get_parameters(lavmodel = tmp,
type = "user", extra = TRUE)
if (lavoptions$verbose) {
cat("Rotating solution using rotation method =",
lavoptions$rotation, "... ")
}
lavmodel <- lav_model_efa_rotate(lavmodel = lavmodel,
x.orig = as.numeric(x), lavoptions = lavoptions)
if (lavoptions$verbose) {
cat("done.\n")
}
}
else {
lavmodel <- lav_model_set_parameters(lavmodel, x = as.numeric(x))
}
lavpartable$est <- lav_model_get_parameters(lavmodel = lavmodel,
type = "user", extra = TRUE)
if (!attr(x, "converged") && lavoptions$warn) {
warning("lavaan WARNING: the optimizer warns that a solution has NOT been found!")
}
}
else {
x <- numeric(0L)
attr(x, "iterations") <- 0L
attr(x, "converged") <- FALSE
attr(x, "control") <- lavoptions$control
attr(x, "fx") <- lav_model_objective(lavmodel = lavmodel,
lavsamplestats = lavsamplestats, lavdata = lavdata,
lavcache = lavcache)
lavpartable$est <- lavpartable$start
}
if (lavoptions$optim.force.converged) {
attr(x, "converged") <- TRUE
}
lavoptim <- list()
x2 <- x
attributes(x2) <- NULL
lavoptim$x <- x2
lavoptim$npar <- length(x)
lavoptim$iterations <- attr(x, "iterations")
lavoptim$converged <- attr(x, "converged")
lavoptim$parscale <- attr(x, "parscale")
fx.copy <- fx <- attr(x, "fx")
attributes(fx) <- NULL
lavoptim$fx <- fx
lavoptim$fx.group <- attr(fx.copy, "fx.group")
if (!is.null(attr(fx.copy, "logl.group"))) {
lavoptim$logl.group <- attr(fx.copy, "logl.group")
lavoptim$logl <- sum(lavoptim$logl.group)
}
else {
lavoptim$logl.group <- as.numeric(NA)
lavoptim$logl <- as.numeric(NA)
}
lavoptim$control <- attr(x, "control")
timing$optim <- (proc.time()[3] - start.time)
start.time <- proc.time()[3]
lavimplied <- list()
if (lavoptions$implied) {
lavimplied <- lav_model_implied(lavmodel)
}
lavloglik <- list()
if (lavoptions$loglik) {
lavloglik <- lav_model_loglik(lavdata = lavdata, lavsamplestats = lavsamplestats,
lavimplied = lavimplied, lavmodel = lavmodel, lavoptions = lavoptions)
}
timing$implied <- (proc.time()[3] - start.time)
start.time <- proc.time()[3]
VCOV <- NULL
if (lavoptions$se != "none" && lavoptions$se != "external" &&
lavoptions$se != "twostep" && lavm...@nx.free > 0L &&
attr(x, "converged")) {
if ((.hasSlot(lavmodel, "nefa")) && (lavmodel@nefa >
0L) && (lavoptions$rotation != "none") && lavoptions$rotation.se ==
"delta") {
lavmodel2 <- lav_model_set_parameters(lavmodel, x = x.unrotated)
}
else {
lavmodel2 <- lavmodel
}
if (lavoptions$verbose) {
cat("Computing VCOV for se =", lavoptions$se, "...")
}
VCOV <- lav_model_vcov(lavmodel = lavmodel2, lavsamplestats = lavsamplestats,
lavoptions = lavoptions, lavdata = lavdata, lavpartable = lavpartable,
lavcache = lavcache, lavimplied = lavimplied, lavh1 = lavh1)
if (lavoptions$verbose) {
cat(" done.\n")
}
if ((.hasSlot(lavmodel, "nefa")) && (lavmodel@nefa >
0L) && (lavoptions$rotation != "none") && (lavoptions$se !=
"bootstrap") && (lavoptions$rotation.se == "delta")) {
if (lavoptions$verbose) {
cat("Using delta method to compute VCOV of rotated parameters:")
}
JAC <- numDeriv::jacobian(func = lav_model_efa_rotate_x,
x = x.unrotated, lavmodel = lavmodel, init.rot = lavoptions$rotation.args$jac.init.rot,
lavoptions = lavoptions, type = "free", extra = FALSE,
method.args = list(eps = 0.001), method = "simple")
VCOV <- JAC %*% VCOV %*% t(JAC)
if (lavoptions$verbose) {
cat(" done.\n")
}
}
}
if (!is.null(attr(VCOV, "BOOT.COEF"))) {
lavboot <- list()
lavboot$coef <- attr(VCOV, "BOOT.COEF")
}
else {
lavboot <- list()
}
tmp.attr <- attributes(VCOV)
VCOV1 <- VCOV
attributes(VCOV1) <- tmp.attr["dim"]
lavvcov <- list(se = lavoptions$se, information = lavoptions$information,
vcov = VCOV1)
if (lavoptions$se != "external" && lavoptions$se != "twostep") {
lavpartable$se <- lav_model_vcov_se(lavmodel = lavmodel,
lavpartable = lavpartable, VCOV = VCOV, BOOT = lavboot$coef)
}
else {
if (is.null(lavpartable$se)) {
lavpartable$se <- lav_model_vcov_se(lavmodel = lavmodel,
lavpartable = lavpartable, VCOV = NULL, BOOT = NULL)
warning("lavaan WARNING: se = \"external\" but parameter table does not contain a `se' column")
}
}
timing$vcov <- (proc.time()[3] - start.time)
start.time <- proc.time()[3]
TEST <- NULL
if (lavoptions$test != "none" && attr(x, "converged")) {
if (lavoptions$verbose) {
cat("Computing TEST for test =", lavoptions$test,
"...")
}
TEST <- lav_model_test(lavmodel = lavmodel, lavpartable = lavpartable,
lavsamplestats = lavsamplestats, lavimplied = lavimplied,
lavh1 = lavh1, lavoptions = lavoptions, x = x, VCOV = VCOV,
lavdata = lavdata, lavcache = lavcache, lavloglik = lavloglik)
if (lavoptions$verbose) {
cat(" done.\n")
}
}
else {
TEST <- list(list(test = "none", stat = NA, stat.group = rep(NA,
lavdata@ngroups), df = NA, refdistr = "unknown",
pvalue = NA))
}
lavtest <- TEST
timing$test <- (proc.time()[3] - start.time)
start.time <- proc.time()[3]
lavfit <- lav_model_fit(lavpartable = lavpartable, lavmodel = lavmodel,
x = x, VCOV = VCOV, TEST = TEST)
timing$total <- (proc.time()[3] - start.time0)
lavbaseline <- list()
if (is.logical(lavoptions$baseline) && lavoptions$baseline) {
}
lavaan <- new("lavaan", version = as.character(packageVersion("lavaan")),
call = mc, timing = timing, Options = lavoptions, ParTable = lavpartable,
pta = lavpta, Data = lavdata, SampleStats = lavsamplestats,
Model = lavmodel, Cache = lavcache, Fit = lavfit, boot = lavboot,
optim = lavoptim, implied = lavimplied, loglik = lavloglik,
vcov = lavvcov, test = lavtest, h1 = lavh1, baseline = list(),
external = list())
if (!is.null(lavoptions$check.post) && lavoptions$check.post &&
lavTech(lavaan, "converged")) {
lavInspect(lavaan, "post.check")
}
hasExplicitConstraints <- FALSE
if (is.character(constraints) && any(nchar(constraints) >
0L)) {
hasExplicitConstraints <- TRUE
}
hasNonLinearEqConstraints <- FALSE
if (length(lavm...@ceq.nonlinear.idx) > 0L) {
hasNonLinearEqConstraints <- TRUE
}
hasIneqConstraints <- FALSE
if (length(lavm...@cin.linear.idx) > 0L || length(lavm...@cin.nonlinear.idx) >
0L) {
hasIneqConstraints <- TRUE
}
if (!is.null(lavoptions$check.gradient) && lavoptions$check.gradient &&
lavTech(lavaan, "converged") && !hasExplicitConstraints &&
!hasNonLinearEqConstraints && !hasIneqConstraints) {
grad <- lavInspect(lavaan, "optim.gradient")
large.idx <- which(abs(grad) > 0.001)
if (length(large.idx) > 0L) {
warning("lavaan WARNING: not all elements of the gradient are (near) zero;\n",
" the optimizer may not have found a local solution;\n",
" use lavInspect(fit, \"optim.gradient\") to investigate")
}
}
lavaan
}
<bytecode: 0x69f76f8>
<environment: namespace:lavaan>
> lavaan()
Error in lavaan() : lavaan ERROR: model is NULL!
> sessioninfo()
Error in sessioninfo() : could not find function "sessioninfo"
> sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
Random number generation:
RNG: Mersenne-Twister
Normal: Inversion
Sample: Rounding
locale:
[1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 LC_COLLATE=C.UTF-8
[5] LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 LC_PAPER=C.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] semPlot_1.1.1 haven_2.1.0 lavaanPlot_0.5.1 forcats_0.4.0 stringr_1.4.0
[6] dplyr_0.8.1 purrr_0.3.2 readr_1.3.1 tidyr_0.8.3 tibble_2.1.3
[11] ggplot2_3.2.0 tidyverse_1.2.1 semTools_0.5-1.934 lavaan_0.6-5.1453 skimr_1.0.7
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.1.4 Hmisc_4.2-0 BDgraph_2.59 plyr_1.8.4
[6] igraph_1.2.4.1 lazyeval_0.2.2 splines_3.6.0 usethis_1.5.0 digest_0.6.18
[11] htmltools_0.3.6 matrixcalc_1.0-3 viridis_0.5.1 magrittr_1.5 Rsolnp_1.16
[16] checkmate_1.9.3 memoise_1.1.0 lisrelToR_0.1.4 cluster_2.1.0 openxlsx_4.1.0.1
[21] remotes_2.0.2 modelr_0.1.4 prettyunits_1.0.2 jpeg_0.1-8 sem_3.1-9
[26] colorspace_1.4-1 rvest_0.3.4 xfun_0.8 callr_3.2.0 crayon_1.3.4
[31] jsonlite_1.6 lme4_1.1-21 regsem_1.3.9 brew_1.0-6 survival_2.44-1.1
[36] glue_1.3.1 gtable_0.3.0 mi_1.0 pkgbuild_1.0.3 Rook_1.1-1
[41] ggm_2.3 abind_1.4-5 scales_1.0.0 Rcpp_1.0.1 viridisLite_0.3.0
[46] xtable_1.8-3 htmlTable_1.13.1 foreign_0.8-71 Formula_1.2-3 stats4_3.6.0
[51] truncnorm_1.0-8 htmlwidgets_1.3 httr_1.4.0 DiagrammeR_1.0.1 RColorBrewer_1.1-2
[56] acepack_1.4.1 pkgconfig_2.0.2 XML_3.98-1.20 nnet_7.3-12 kutils_1.69
[61] tidyselect_0.2.5 rlang_0.4.0 reshape2_1.4.3 munsell_0.5.0 cellranger_1.1.0
[66] tools_3.6.0 visNetwork_2.0.7 downloader_0.4 cli_1.1.0 generics_0.0.2
[71] devtools_2.0.2 broom_0.5.2 fdrtool_1.2.15 yaml_2.2.0 arm_1.10-1
[76] processx_3.3.1 knitr_1.22 fs_1.3.1 zip_2.0.2 glasso_1.10
[81] pbapply_1.4-0 nlme_3.1-140 whisker_0.3-2 xml2_1.2.0 compiler_3.6.0
[86] rstudioapi_0.10 curl_3.3 rgexf_0.15.3 png_0.1-7 testthat_2.1.1
[91] huge_1.3.2 pbivnorm_0.6.0 stringi_1.3.1 highr_0.8 ps_1.3.0
[96] qgraph_1.6.3 rockchalk_1.8.144 desc_1.2.0 lattice_0.20-38 Matrix_1.2-17
[101] psych_1.8.12 nloptr_1.2.1 pillar_1.4.1 OpenMx_2.13.2 data.table_1.12.2
[106] corpcor_1.6.9 R6_2.4.0 latticeExtra_0.6-28 gridExtra_2.3 sessioninfo_1.1.1
[111] gtools_3.8.1 boot_1.3-22 MASS_7.3-51.4 assertthat_0.2.1 pkgload_1.0.2
[116] rjson_0.2.20 rprojroot_1.3-2 withr_2.1.2 mnormt_1.5-5 parallel_3.6.0
[121] hms_0.4.2 influenceR_0.1.0 grid_3.6.0 rpart_4.1-15 coda_0.19-2
[126] minqa_1.2.4 carData_3.0-2 d3Network_0.5.2.1 lubridate_1.7.4 base64enc_0.1-3
> ## compare several invariance models
>
> modinv = '
+ Cp =~ SE1 + SE2 + SE3 + SE4 + SE5
+ Em =~ SE6 + SE7 + SE8 + SE9 + SE10
+ Co =~ SE11 + SE12 + SE13 + SE14 + SE15
+ # fator de 2a ordem
+ #eng =~ Cp + Em + Co
+ '
>
> #install.packages("lavaan", repos = "http://www.da.ugent.be", type = "source")
> #devtools::install_github("simsem/semTools/semTools")
> #bd1=subset(bd, Country %in% c("ZH", "PT"))
>
> grupo="Country"
> test.seq <- c("thresholds","loadings","intercepts", "regressions", "means","residuals")
> meq.list <- list()
> for (i in 0L:length(test.seq)) {
+ if (i == 0L) {
+ meq.label <- "configural"
+ group.equal <- ""
+ } else {
+ meq.label <- test.seq[i]
+ group.equal <- test.seq[1:i]
+ }
+ meq.list[[meq.label]] <- measEq.syntax(configural.model = modinv,
+ data = bd,
+ ordered = it,
+ parameterization = "theta",
+ ID.fac = "ul",
+ ID.cat = "Wu",
+ group = grupo,
+ group.equal = group.equal,
+ return.fit = TRUE)
+ }
Warning messages:
1: In lavaan::lavaan(model = "## LOADINGS:\n\nCp =~ c(1, 1, 1, 1, 1, 1, 1, 1)*SE1 + c(lambda.1_1.g1, lambda.1_1.g2, lambda.1_1.g3, lambda.1_1.g4, lambda.1_1.g5, lambda.1_1.g6, lambda.1_1.g7, lambda.1_1.g8)*SE1\nCp =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE2 + c(lambda.2_1.g1, lambda.2_1.g2, lambda.2_1.g3, lambda.2_1.g4, lambda.2_1.g5, lambda.2_1.g6, lambda.2_1.g7, lambda.2_1.g8)*SE2\nCp =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE3 + c(lambda.3_1.g1, lambda.3_1.g2, lambda.3_1.g3, lambda.3_1.g4, lambda.3_1.g5, lambda.3_1.g6, lambda.3_1.g7, lambda.3_1.g8)*SE3\nCp =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE4 + c(lambda.4_1.g1, lambda.4_1.g2, lambda.4_1.g3, lambda.4_1.g4, lambda.4_1.g5, lambda.4_1.g6, lambda.4_1.g7, lambda.4_1.g8)*SE4\nCp =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE5 + c(lambda.5_1.g1, lambda.5_1.g2, lambda.5_1.g3, lambda.5_1.g4, lambda.5_1.g5, lambda.5_1.g6, lambda.5_1.g7, lambda.5_1.g8)*SE5\nEm =~ c(1, 1, 1, 1, 1, 1, 1, 1)*SE6 + c(lambda.6_2.g1, lambda.6_2.g2, lambda.6_2.g3, lambda.6_2.g4, lambda.6_2.g5, lambda.6_2.g6, lambda.6_2.g7, lambda.6_2.g8)*SE6\nEm =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE7 + c(lambda.7_2.g1, lambda.7_2.g2, lambda.7_2.g3, lambda.7_2.g4, lambda.7_2.g5, lambda.7_2.g6, lambda.7_2.g7, lambda.7_2.g8)*SE7\nEm =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE8 + c(lambda.8_2.g1, lambda.8_2.g2, lambda.8_2.g3, lambda.8_2.g4, lambda.8_2.g5, lambda.8_2.g6, lambda.8_2.g7, lambda.8_2.g8)*SE8\nEm =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE9 + c(lambda.9_2.g1, lambda.9_2.g2, lambda.9_2.g3, lambda.9_2.g4, lambda.9_2.g5, lambda.9_2.g6, lambda.9_2.g7, lambda.9_2.g8)*SE9\nEm =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE10 + c(lambda.10_2.g1, lambda.10_2.g2, lambda.10_2.g3, lambda.10_2.g4, lambda.10_2.g5, lambda.10_2.g6, lambda.10_2.g7, lambda.10_2.g8)*SE10\nCo =~ c(1, 1, 1, 1, 1, 1, 1, 1)*SE11 + c(lambda.11_3.g1, lambda.11_3.g2, lambda.11_3.g3, lambda.11_3.g4, lambda.11_3.g5, lambda.11_3.g6, lambda.11_3.g7, lambda.11_3.g8)*SE11\nCo =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE12 + c(lambda.12_3.g1, lambda.12_3.g2, lambda.12_3.g3, lambda.12_3.g4, lambda.12_3.g5, lambda.12_3.g6, lambda.12_3.g7, lambda.12_3.g8)*SE12\nCo =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE13 + c(lambda.13_3.g1, lambda.13_3.g2, lambda.13_3.g3, lambda.13_3.g4, lambda.13_3.g5, lambda.13_3.g6, lambda.13_3.g7, lambda.13_3.g8)*SE13\nCo =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE14 + c(lambda.14_3.g1, lambda.14_3.g2, lambda.14_3.g3, lambda.14_3.g4, lambda.14_3.g5, lambda.14_3.g6, lambda.14_3.g7, lambda.14_3.g8)*SE14\nCo =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE15 + c(lambda.15_3.g1, lambda.15_3.g2, lambda.15_3.g3, lambda.15_3.g4, lambda.15_3.g5, lambda.15_3.g6, lambda.15_3.g7, lambda.15_3.g8)*SE15\n\n## THRESHOLDS:\n\nSE1 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE1.thr1.g1, SE1.thr1.g2, SE1.thr1.g3, SE1.thr1.g4, SE1.thr1.g5, SE1.thr1.g6, SE1.thr1.g7, SE1.thr1.g8)*t1\nSE1 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE1.thr2.g1, SE1.thr2.g2, SE1.thr2.g3, SE1.thr2.g4, SE1.thr2.g5, SE1.thr2.g6, SE1.thr2.g7, SE1.thr2.g8)*t2\nSE1 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE1.thr3.g1, SE1.thr3.g2, SE1.thr3.g3, SE1.thr3.g4, SE1.thr3.g5, SE1.thr3.g6, SE1.thr3.g7, SE1.thr3.g8)*t3\nSE1 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE1.thr4.g1, SE1.thr4.g2, SE1.thr4.g3, SE1.thr4.g4, SE1.thr4.g5, SE1.thr4.g6, SE1.thr4.g7, SE1.thr4.g8)*t4\nSE2 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE2.thr1.g1, SE2.thr1.g2, SE2.thr1.g3, SE2.thr1.g4, SE2.thr1.g5, SE2.thr1.g6, SE2.thr1.g7, SE2.thr1.g8)*t1\nSE2 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE2.thr2.g1, SE2.thr2.g2, SE2.thr2.g3, SE2.thr2.g4, SE2.thr2.g5, SE2.thr2.g6, SE2.thr2.g7, SE2.thr2.g8)*t2\nSE2 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE2.thr3.g1, SE2.thr3.g2, SE2.thr3.g3, SE2.thr3.g4, SE2.thr3.g5, SE2.thr3.g6, SE2.thr3.g7, SE2.thr3.g8)*t3\nSE2 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE2.thr4.g1, SE2.thr4.g2, SE2.thr4.g3, SE2.thr4.g4, SE2.thr4.g5, SE2.thr4.g6, SE2.thr4.g7, SE2.thr4.g8)*t4\nSE3 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE3.thr1.g1, SE3.thr1.g2, SE3.thr1.g3, SE3.thr1.g4, SE3.thr1.g5, SE3.thr1.g6, SE3.thr1.g7, SE3.thr1.g8)*t1\nSE3 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE3.thr2.g1, SE3.thr2.g2, SE3.thr2.g3, SE3.thr2.g4, SE3.thr2.g5, SE3.thr2.g6, SE3.thr2.g7, SE3.thr2.g8)*t2\nSE3 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE3.thr3.g1, SE3.thr3.g2, SE3.thr3.g3, SE3.thr3.g4, SE3.thr3.g5, SE3.thr3.g6, SE3.thr3.g7, SE3.thr3.g8)*t3\nSE3 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE3.thr4.g1, SE3.thr4.g2, SE3.thr4.g3, SE3.thr4.g4, SE3.thr4.g5, SE3.thr4.g6, SE3.thr4.g7, SE3.thr4.g8)*t4\nSE4 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE4.thr1.g1, SE4.thr1.g2, SE4.thr1.g3, SE4.thr1.g4, SE4.thr1.g5, SE4.thr1.g6, SE4.thr1.g7, SE4.thr1.g8)*t1\nSE4 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE4.thr2.g1, SE4.thr2.g2, SE4.thr2.g3, SE4.thr2.g4, SE4.thr2.g5, SE4.thr2.g6, SE4.thr2.g7, SE4.thr2.g8)*t2\nSE4 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE4.thr3.g1, SE4.thr3.g2, SE4.thr3.g3, SE4.thr3.g4, SE4.thr3.g5, SE4.thr3.g6, SE4.thr3.g7, SE4.thr3.g8)*t3\nSE4 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE4.thr4.g1, SE4.thr4.g2, SE4.thr4.g3, SE4.thr4.g4, SE4.thr4.g5, SE4.thr4.g6, SE4.thr4.g7, SE4.thr4.g8)*t4\nSE5 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE5.thr1.g1, SE5.thr1.g2, SE5.thr1.g3, SE5.thr1.g4, SE5.thr1.g5, SE5.thr1.g6, SE5.thr1.g7, SE5.thr1.g8)*t1\nSE5 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE5.thr2.g1, SE5.thr2.g2, SE5.thr2.g3, SE5.thr2.g4, SE5.thr2.g5, SE5.thr2.g6, SE5.thr2.g7, SE5.thr2.g8)*t2\nSE5 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE5.thr3.g1, SE5.thr3.g2, SE5.thr3.g3, SE5.thr3.g4, SE5.thr3.g5, SE5.thr3.g6, SE5.thr3.g7, SE5.thr3.g8)*t3\nSE5 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE5.thr4.g1, SE5.thr4.g2, SE5.thr4.g3, SE5.thr4.g4, SE5.thr4.g5, SE5.thr4.g6, SE5.thr4.g7, SE5.thr4.g8)*t4\nSE6 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE6.thr1.g1, SE6.thr1.g2, SE6.thr1.g3, SE6.thr1.g4, SE6.thr1.g5, SE6.thr1.g6, SE6.thr1.g7, SE6.thr1.g8)*t1\nSE6 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE6.thr2.g1, SE6.thr2.g2, SE6.thr2.g3, SE6.thr2.g4, SE6.thr2.g5, SE6.thr2.g6, SE6.thr2.g7, SE6.thr2.g8)*t2\nSE6 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE6.thr3.g1, SE6.thr3.g2, SE6.thr3.g3, SE6.thr3.g4, SE6.thr3.g5, SE6.thr3.g6, SE6.thr3.g7, SE6.thr3.g8)*t3\nSE6 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE6.thr4.g1, SE6.thr4.g2, SE6.thr4.g3, SE6.thr4.g4, SE6.thr4.g5, SE6.thr4.g6, SE6.thr4.g7, SE6.thr4.g8)*t4\nSE7 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE7.thr1.g1, SE7.thr1.g2, SE7.thr1.g3, SE7.thr1.g4, SE7.thr1.g5, SE7.thr1.g6, SE7.thr1.g7, SE7.thr1.g8)*t1\nSE7 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE7.thr2.g1, SE7.thr2.g2, SE7.thr2.g3, SE7.thr2.g4, SE7.thr2.g5, SE7.thr2.g6, SE7.thr2.g7, SE7.thr2.g8)*t2\nSE7 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE7.thr3.g1, SE7.thr3.g2, SE7.thr3.g3, SE7.thr3.g4, SE7.thr3.g5, SE7.thr3.g6, SE7.thr3.g7, SE7.thr3.g8)*t3\nSE7 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE7.thr4.g1, SE7.thr4.g2, SE7.thr4.g3, SE7.thr4.g4, SE7.thr4.g5, SE7.thr4.g6, SE7.thr4.g7, SE7.thr4.g8)*t4\nSE8 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE8.thr1.g1, SE8.thr1.g2, SE8.thr1.g3, SE8.thr1.g4, SE8.thr1.g5, SE8.thr1.g6, SE8.thr1.g7, SE8.thr1.g8)*t1\nSE8 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE8.thr2.g1, SE8.thr2.g2, SE8.thr2.g3, SE8.thr2.g4, SE8.thr2.g5, SE8.thr2.g6, SE8.thr2.g7, SE8.thr2.g8)*t2\nSE8 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE8.thr3.g1, SE8.thr3.g2, SE8.thr3.g3, SE8.thr3.g4, SE8.thr3.g5, SE8.thr3.g6, SE8.thr3.g7, SE8.thr3.g8)*t3\nSE8 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE8.thr4.g1, SE8.thr4.g2, SE8.thr4.g3, SE8.thr4.g4, SE8.thr4.g5, SE8.thr4.g6, SE8.thr4.g7, SE8.thr4.g8)*t4\nSE9 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE9.thr1.g1, SE9.thr1.g2, SE9.thr1.g3, SE9.thr1.g4, SE9.thr1.g5, SE9.thr1.g6, SE9.thr1.g7, SE9.thr1.g8)*t1\nSE9 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE9.thr2.g1, SE9.thr2.g2, SE9.thr2.g3, SE9.thr2.g4, SE9.thr2.g5, SE9.thr2.g6, SE9.thr2.g7, SE9.thr2.g8)*t2\nSE9 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE9.thr3.g1, SE9.thr3.g2, SE9.thr3.g3, SE9.thr3.g4, SE9.thr3.g5, SE9.thr3.g6, SE9.thr3.g7, SE9.thr3.g8)*t3\nSE9 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE9.thr4.g1, SE9.thr4.g2,
lavaan WARNING: the optimizer warns that a solution has NOT been found!
2: In lavaan::lavaan(model = "## LOADINGS:\n\nCp =~ c(1, 1, 1, 1, 1, 1, 1, 1)*SE1 + c(lambda.1_1.g1, lambda.1_1.g2, lambda.1_1.g3, lambda.1_1.g4, lambda.1_1.g5, lambda.1_1.g6, lambda.1_1.g7, lambda.1_1.g8)*SE1\nCp =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE2 + c(lambda.2_1.g1, lambda.2_1.g2, lambda.2_1.g3, lambda.2_1.g4, lambda.2_1.g5, lambda.2_1.g6, lambda.2_1.g7, lambda.2_1.g8)*SE2\nCp =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE3 + c(lambda.3_1.g1, lambda.3_1.g2, lambda.3_1.g3, lambda.3_1.g4, lambda.3_1.g5, lambda.3_1.g6, lambda.3_1.g7, lambda.3_1.g8)*SE3\nCp =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE4 + c(lambda.4_1.g1, lambda.4_1.g2, lambda.4_1.g3, lambda.4_1.g4, lambda.4_1.g5, lambda.4_1.g6, lambda.4_1.g7, lambda.4_1.g8)*SE4\nCp =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE5 + c(lambda.5_1.g1, lambda.5_1.g2, lambda.5_1.g3, lambda.5_1.g4, lambda.5_1.g5, lambda.5_1.g6, lambda.5_1.g7, lambda.5_1.g8)*SE5\nEm =~ c(1, 1, 1, 1, 1, 1, 1, 1)*SE6 + c(lambda.6_2.g1, lambda.6_2.g2, lambda.6_2.g3, lambda.6_2.g4, lambda.6_2.g5, lambda.6_2.g6, lambda.6_2.g7, lambda.6_2.g8)*SE6\nEm =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE7 + c(lambda.7_2.g1, lambda.7_2.g2, lambda.7_2.g3, lambda.7_2.g4, lambda.7_2.g5, lambda.7_2.g6, lambda.7_2.g7, lambda.7_2.g8)*SE7\nEm =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE8 + c(lambda.8_2.g1, lambda.8_2.g2, lambda.8_2.g3, lambda.8_2.g4, lambda.8_2.g5, lambda.8_2.g6, lambda.8_2.g7, lambda.8_2.g8)*SE8\nEm =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE9 + c(lambda.9_2.g1, lambda.9_2.g2, lambda.9_2.g3, lambda.9_2.g4, lambda.9_2.g5, lambda.9_2.g6, lambda.9_2.g7, lambda.9_2.g8)*SE9\nEm =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE10 + c(lambda.10_2.g1, lambda.10_2.g2, lambda.10_2.g3, lambda.10_2.g4, lambda.10_2.g5, lambda.10_2.g6, lambda.10_2.g7, lambda.10_2.g8)*SE10\nCo =~ c(1, 1, 1, 1, 1, 1, 1, 1)*SE11 + c(lambda.11_3.g1, lambda.11_3.g2, lambda.11_3.g3, lambda.11_3.g4, lambda.11_3.g5, lambda.11_3.g6, lambda.11_3.g7, lambda.11_3.g8)*SE11\nCo =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE12 + c(lambda.12_3.g1, lambda.12_3.g2, lambda.12_3.g3, lambda.12_3.g4, lambda.12_3.g5, lambda.12_3.g6, lambda.12_3.g7, lambda.12_3.g8)*SE12\nCo =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE13 + c(lambda.13_3.g1, lambda.13_3.g2, lambda.13_3.g3, lambda.13_3.g4, lambda.13_3.g5, lambda.13_3.g6, lambda.13_3.g7, lambda.13_3.g8)*SE13\nCo =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE14 + c(lambda.14_3.g1, lambda.14_3.g2, lambda.14_3.g3, lambda.14_3.g4, lambda.14_3.g5, lambda.14_3.g6, lambda.14_3.g7, lambda.14_3.g8)*SE14\nCo =~ c(NA, NA, NA, NA, NA, NA, NA, NA)*SE15 + c(lambda.15_3.g1, lambda.15_3.g2, lambda.15_3.g3, lambda.15_3.g4, lambda.15_3.g5, lambda.15_3.g6, lambda.15_3.g7, lambda.15_3.g8)*SE15\n\n## THRESHOLDS:\n\nSE1 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE1.thr1, SE1.thr1, SE1.thr1, SE1.thr1, SE1.thr1, SE1.thr1, SE1.thr1, SE1.thr1)*t1\nSE1 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE1.thr2, SE1.thr2, SE1.thr2, SE1.thr2, SE1.thr2, SE1.thr2, SE1.thr2, SE1.thr2)*t2\nSE1 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE1.thr3, SE1.thr3, SE1.thr3, SE1.thr3, SE1.thr3, SE1.thr3, SE1.thr3, SE1.thr3)*t3\nSE1 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE1.thr4, SE1.thr4, SE1.thr4, SE1.thr4, SE1.thr4, SE1.thr4, SE1.thr4, SE1.thr4)*t4\nSE2 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE2.thr1, SE2.thr1, SE2.thr1, SE2.thr1, SE2.thr1, SE2.thr1, SE2.thr1, SE2.thr1)*t1\nSE2 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE2.thr2, SE2.thr2, SE2.thr2, SE2.thr2, SE2.thr2, SE2.thr2, SE2.thr2, SE2.thr2)*t2\nSE2 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE2.thr3, SE2.thr3, SE2.thr3, SE2.thr3, SE2.thr3, SE2.thr3, SE2.thr3, SE2.thr3)*t3\nSE2 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE2.thr4, SE2.thr4, SE2.thr4, SE2.thr4, SE2.thr4, SE2.thr4, SE2.thr4, SE2.thr4)*t4\nSE3 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE3.thr1, SE3.thr1, SE3.thr1, SE3.thr1, SE3.thr1, SE3.thr1, SE3.thr1, SE3.thr1)*t1\nSE3 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE3.thr2, SE3.thr2, SE3.thr2, SE3.thr2, SE3.thr2, SE3.thr2, SE3.thr2, SE3.thr2)*t2\nSE3 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE3.thr3, SE3.thr3, SE3.thr3, SE3.thr3, SE3.thr3, SE3.thr3, SE3.thr3, SE3.thr3)*t3\nSE3 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE3.thr4, SE3.thr4, SE3.thr4, SE3.thr4, SE3.thr4, SE3.thr4, SE3.thr4, SE3.thr4)*t4\nSE4 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE4.thr1, SE4.thr1, SE4.thr1, SE4.thr1, SE4.thr1, SE4.thr1, SE4.thr1, SE4.thr1)*t1\nSE4 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE4.thr2, SE4.thr2, SE4.thr2, SE4.thr2, SE4.thr2, SE4.thr2, SE4.thr2, SE4.thr2)*t2\nSE4 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE4.thr3, SE4.thr3, SE4.thr3, SE4.thr3, SE4.thr3, SE4.thr3, SE4.thr3, SE4.thr3)*t3\nSE4 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE4.thr4, SE4.thr4, SE4.thr4, SE4.thr4, SE4.thr4, SE4.thr4, SE4.thr4, SE4.thr4)*t4\nSE5 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE5.thr1, SE5.thr1, SE5.thr1, SE5.thr1, SE5.thr1, SE5.thr1, SE5.thr1, SE5.thr1)*t1\nSE5 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE5.thr2, SE5.thr2, SE5.thr2, SE5.thr2, SE5.thr2, SE5.thr2, SE5.thr2, SE5.thr2)*t2\nSE5 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE5.thr3, SE5.thr3, SE5.thr3, SE5.thr3, SE5.thr3, SE5.thr3, SE5.thr3, SE5.thr3)*t3\nSE5 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE5.thr4, SE5.thr4, SE5.thr4, SE5.thr4, SE5.thr4, SE5.thr4, SE5.thr4, SE5.thr4)*t4\nSE6 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE6.thr1, SE6.thr1, SE6.thr1, SE6.thr1, SE6.thr1, SE6.thr1, SE6.thr1, SE6.thr1)*t1\nSE6 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE6.thr2, SE6.thr2, SE6.thr2, SE6.thr2, SE6.thr2, SE6.thr2, SE6.thr2, SE6.thr2)*t2\nSE6 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE6.thr3, SE6.thr3, SE6.thr3, SE6.thr3, SE6.thr3, SE6.thr3, SE6.thr3, SE6.thr3)*t3\nSE6 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE6.thr4, SE6.thr4, SE6.thr4, SE6.thr4, SE6.thr4, SE6.thr4, SE6.thr4, SE6.thr4)*t4\nSE7 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE7.thr1, SE7.thr1, SE7.thr1, SE7.thr1, SE7.thr1, SE7.thr1, SE7.thr1, SE7.thr1)*t1\nSE7 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE7.thr2, SE7.thr2, SE7.thr2, SE7.thr2, SE7.thr2, SE7.thr2, SE7.thr2, SE7.thr2)*t2\nSE7 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE7.thr3, SE7.thr3, SE7.thr3, SE7.thr3, SE7.thr3, SE7.thr3, SE7.thr3, SE7.thr3)*t3\nSE7 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE7.thr4, SE7.thr4, SE7.thr4, SE7.thr4, SE7.thr4, SE7.thr4, SE7.thr4, SE7.thr4)*t4\nSE8 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE8.thr1, SE8.thr1, SE8.thr1, SE8.thr1, SE8.thr1, SE8.thr1, SE8.thr1, SE8.thr1)*t1\nSE8 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE8.thr2, SE8.thr2, SE8.thr2, SE8.thr2, SE8.thr2, SE8.thr2, SE8.thr2, SE8.thr2)*t2\nSE8 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE8.thr3, SE8.thr3, SE8.thr3, SE8.thr3, SE8.thr3, SE8.thr3, SE8.thr3, SE8.thr3)*t3\nSE8 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE8.thr4, SE8.thr4, SE8.thr4, SE8.thr4, SE8.thr4, SE8.thr4, SE8.thr4, SE8.thr4)*t4\nSE9 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE9.thr1, SE9.thr1, SE9.thr1, SE9.thr1, SE9.thr1, SE9.thr1, SE9.thr1, SE9.thr1)*t1\nSE9 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE9.thr2, SE9.thr2, SE9.thr2, SE9.thr2, SE9.thr2, SE9.thr2, SE9.thr2, SE9.thr2)*t2\nSE9 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE9.thr3, SE9.thr3, SE9.thr3, SE9.thr3, SE9.thr3, SE9.thr3, SE9.thr3, SE9.thr3)*t3\nSE9 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE9.thr4, SE9.thr4, SE9.thr4, SE9.thr4, SE9.thr4, SE9.thr4, SE9.thr4, SE9.thr4)*t4\nSE10 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE10.thr1, SE10.thr1, SE10.thr1, SE10.thr1, SE10.thr1, SE10.thr1, SE10.thr1, SE10.thr1)*t1\nSE10 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE10.thr2, SE10.thr2, SE10.thr2, SE10.thr2, SE10.thr2, SE10.thr2, SE10.thr2, SE10.thr2)*t2\nSE10 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t3 + c(SE10.thr3, SE10.thr3, SE10.thr3, SE10.thr3, SE10.thr3, SE10.thr3, SE10.thr3, SE10.thr3)*t3\nSE10 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t4 + c(SE10.thr4, SE10.thr4, SE10.thr4, SE10.thr4, SE10.thr4, SE10.thr4, SE10.thr4, SE10.thr4)*t4\nSE11 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t1 + c(SE11.thr1, SE11.thr1, SE11.thr1, SE11.thr1, SE11.thr1, SE11.thr1, SE11.thr1, SE11.thr1)*t1\nSE11 | c(NA, NA, NA, NA, NA, NA, NA, NA)*t2 + c(SE11.thr2, SE11.thr2, SE11.thr2,
lavaan WARNING: the optimizer warns that a solution has NOT been found!
3: In lav_model_vcov(lavmodel = lavmodel2, lavsamplestats = lavsamplestats, :
lavaan WARNING:
The variance-covariance matrix of the estimated parameters (vcov)
does not appear to be positive definite! The smallest eigenvalue
(= 1.079082e-13) is close to zero. This may be a symptom that the
model is not identified.
And
> compareFit(meq.list)
Error in lav_fit_measures(object = object, fit.measures = fit.measures, :
lavaan ERROR: fit measures not available if model did not converge
I do get results if I remove the “ordered = it,” option. But in that case, I am using ML estimation and not WLSMV on the categorical polychoric matrix, correct?
For the ML estimation (without the “ordered=it,” option, the thresholds and configural model are the same (since there are no thresholds) and I do get the warning that there are models with same df (the configural and thresholds models):
################### Nested Model Comparison #########################
Chi Square Difference Test
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
meq.list.configural 696 119612 121933 1904.5
meq.list.thresholds 696 119612 121933 1904.5 0.00 0
meq.list.loadings 780 119667 121481 2127.9 223.46 84 1.325e-14 ***
meq.list.intercepts 864 120431 121737 3059.6 931.64 84 < 2.2e-16 ***
meq.list.regressions 878 120440 121661 3096.7 37.13 14 0.0007048 ***
meq.list.means 899 120886 121980 3584.7 487.98 21 < 2.2e-16 ***
meq.list.residuals 1004 121447 121906 4355.4 770.73 105 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
####################### Model Fit Indices ###########################
chisq df pvalue cfi tli aic bic rmsea srmr
meq.list.configural 1904.485† 696 .000 .920† .904 119611.822† 121933.328 .067 .056†
meq.list.thresholds 1904.485† 696 .000 .920† .904 119611.822† 121933.328 .067 .056†
meq.list.loadings 2127.943 780 .000 .911 .904† 119667.281 121480.957† .067† .068
meq.list.intercepts 3059.580 864 .000 .855 .859 120430.917 121736.764 .081 .078
meq.list.regressions 3096.710 878 .000 .854 .860 120440.048 121661.257 .080 .081
meq.list.means 3584.692 899 .000 .823 .834 120886.029 121980.281 .088 .104
meq.list.residuals 4355.418 1004 .000 .779 .815 121446.755 121906.220 .093 .109
################## Differences in Fit Indices #######################
df cfi tli aic bic rmsea srmr
meq.list.thresholds - meq.list.configural 0 0.000 0.000 0.000 0.000 0.000 0.000
meq.list.loadings - meq.list.thresholds 84 -0.009 0.000 55.459 -452.371 0.000 0.013
meq.list.intercepts - meq.list.loadings 84 -0.056 -0.045 763.636 255.807 0.014 0.009
meq.list.regressions - meq.list.intercepts 14 -0.002 0.001 9.131 -75.507 0.000 0.004
meq.list.means - meq.list.regressions 21 -0.031 -0.026 445.981 319.024 0.007 0.023
meq.list.residuals - meq.list.means 105 -0.044 -0.019 560.726 -74.061 0.005 0.006
☹☹☹
João Marôco
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Terrence Jorgensen
and I still get the same error:
Error in lav_fit_measures(object = object, fit.measures = fit.measures, :
lavaan ERROR: fit measures not available if model did not converge
I do get results if I remove the “ordered = it,” option. But in that case, I am using ML estimation and not WLSMV on the categorical polychoric matrix, correct?
jpma...@gmail.com
Thanks Terrence,.
Yes, I did that (I left the 2nd order comment by mistake). The thresholds model does not converge.
I boiled the problem down to a few items in one of the countries were there are empty response categories. I think will proceed with MLR. The response format is 5-point likert, the loadings are a little smaller but still acceptable (hope the referees won't bother with the polychorics mambo - jambo :-)
One a diiferent note. Thanks you (and Yves) so much for putiing so much work and time answering the lavaan and semtools users questions. Really appreciate it.
Best,
João Marôco
[Sent from my not that smart smartphone, with more errors than usual]
-------- Original Message --------
From: Terrence Jorgensen <tjorge...@gmail.com>
Sent: Saturday, July 6, 2019 10:37 AM
To: lavaan <lav...@googlegroups.com>
Subject: Re: measurment invariance for categorical itens: "measurmentinvarianceCAT" vs measEQ.Syntax
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