Nothing
R/lav_object_inspect.R
In lavaan: Latent Variable Analysis
Defines functions
lav_object_inspect_mdist2
lav_object_inspect_loglik_casewise
lav_object_inspect_ranef
lav_object_inspect_icc
lav_object_inspect_npar
lav_object_inspect_coef
lav_object_inspect_delta_internal
lav_object_inspect_delta_rownames
lav_object_inspect_delta
lav_object_inspect_UfromUGamma
lav_object_inspect_UGamma
lav_object_inspect_vcov_def
lav_object_inspect_vcov
lav_object_inspect_firstorder
lav_object_inspect_h1_information
lav_object_inspect_information
lav_object_inspect_hessian
lav_object_inspect_gradient
lav_object_inspect_sampstat_gamma
lav_object_inspect_wls_v
lav_object_inspect_wls_obs
lav_object_inspect_wls_est
lav_object_inspect_empty_idx
lav_object_inspect_missing_patterns
lav_object_inspect_missing_coverage
lav_object_inspect_theta
lav_object_inspect_fs_determinacy
lav_object_inspect_vy
lav_object_inspect_th_idx
lav_object_inspect_th
lav_object_inspect_mean_ov
lav_object_inspect_cov_ov
lav_object_inspect_cov_all
lav_object_inspect_mean_lv
lav_object_inspect_cov_lv
lav_object_inspect_residuals
lav_object_inspect_implied
lav_object_inspect_rsquare
lav_object_inspect_cluster_info
lav_object_inspect_case_idx
lav_object_inspect_data
lav_object_inspect_sampstat
lav_object_inspect_modelmatrices
lav_object_inspect_boot
lav_object_inspect_start
lav_object_inspect_std_se
lav_object_inspect_se
lav_object_inspect_est
lavInspect.lavaan
lavTech.lavaan
inspect.lavaan
# inspect a fitted lavaan object
# backward compatibility -- wrapper around lavInspect
inspect.lavaan <- function(object, what = "free", ...) {
lavInspect.lavaan(object = object,
what = what,
add.labels = TRUE,
add.class = TRUE,
drop.list.single.group = TRUE)
}
# the `tech' version: no labels, full matrices, ... for further processing
lavTech.lavaan <- function(object,
what = "free",
add.labels = FALSE,
add.class = FALSE,
list.by.group = FALSE,
drop.list.single.group = FALSE) {
lavInspect.lavaan(object, what = what,
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
}
# the `user' version: with defaults for display only
lavInspect.lavaan <- function(object, # nolint
what = "free",
add.labels = TRUE,
add.class = TRUE,
list.by.group = TRUE,
drop.list.single.group = TRUE) {
# object must inherit from class lavaan
stopifnot(inherits(object, "lavaan"))
# store partable with pta in object to use cache in called functions
object@ParTable <- lav_partable_set_cache(object@ParTable, object@pta)
# old (<0.6) object?
if (!.hasSlot(object@Data, "block.label")) {
object@Data@block.label <- object@Data@group.label
}
# only a single argument
if (length(what) > 1) {
lav_msg_stop(gettextf("argument %s cannot have more than one element",
"what"))
}
# be case insensitive
what <- tolower(what)
#### model matrices, with different contents ####
if (what == "free") {
lav_object_inspect_modelmatrices(object, what = "free",
type = "free", add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "impute" ||
what == "imputed") { # just to ease the transition for semTools!
object@imputed
} else if (what == "partable" || what == "user") {
lav_object_inspect_modelmatrices(object, what = "free",
type = "partable", add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "se" ||
what == "std.err" ||
what == "standard.errors") {
lav_object_inspect_modelmatrices(object, what = "se",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "se.std" ||
what == "std.se") {
lav_object_inspect_modelmatrices(object, what = "std.se",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "start" || what == "starting.values") {
lav_object_inspect_modelmatrices(object, what = "start",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "est" || what == "estimates" ||
what == "x") {
lav_object_inspect_modelmatrices(object, what = "est",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "est.unrotated") {
lav_object_inspect_modelmatrices(object, what = "est.unrotated",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "dx.free") {
lav_object_inspect_modelmatrices(object, what = "dx.free",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "dx.all") {
lav_object_inspect_modelmatrices(object, what = "dx.all",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "std" || what == "std.all" ||
what == "est.std" || what == "std.est" ||
what == "standardized") {
lav_object_inspect_modelmatrices(object, what = "std.all",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "std.lv") {
lav_object_inspect_modelmatrices(object, what = "std.lv",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "std.nox") {
lav_object_inspect_modelmatrices(object, what = "std.nox",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
#### parameter table ####
} else if (what == "list") {
parTable(object)
#### bootstrap coef ####
} else if (what %in% c("boot", "bootstrap", "boot.coef", "coef.boot")) {
lav_object_inspect_boot(object, add.labels = add.labels,
add.class = add.class)
#### fit indices ####
} else if (what == "fit" ||
what == "fitmeasures" ||
what == "fit.measures" ||
what == "fit.indices") {
fitMeasures(object)
#### baseline model ####
} else if (what == "baseline.partable") {
out <- as.data.frame(object@baseline$partable, stringsAsFactors = FALSE)
if (add.class) {
class(out) <- c("lavaan.data.frame", "data.frame")
}
return(out)
} else if (what == "baseline.test") {
object@baseline$test
#### modification indices ####
} else if (what == "mi" ||
what == "modindices" ||
what == "modification.indices") {
modificationIndices(object)
#### sample statistics #####
} else if (what == "obs" ||
what == "observed" ||
what == "sampstat" ||
what == "sampstats" ||
what == "samplestats" ||
what == "samp" ||
what == "sample" ||
what == "samplestatistics") {
# new in 0.6-3: always use h1 = TRUE!!!
lav_object_inspect_sampstat(object, h1 = TRUE, std = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "obs.std" ||
what == "observed.std" ||
what == "sampstat.std" ||
what == "sampstats.std" ||
what == "samplestats.std" ||
what == "samp.std" ||
what == "sample.std" ||
what == "samplestatistics.std") {
lav_object_inspect_sampstat(object, h1 = TRUE, std = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "h1" || what == "missing.h1" || what == "sampstat.h1") {
lav_object_inspect_sampstat(object, h1 = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
#### wls.est - wls.obs - wls.v ####
} else if (what == "wls.est") {
lav_object_inspect_wls_est(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "wls.obs") {
lav_object_inspect_wls_obs(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "wls.v") {
lav_object_inspect_wls_v(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
#### data + missingness ####
} else if (what == "data") {
lav_object_inspect_data(object, add.labels = add.labels,
drop.list.single.group = drop.list.single.group)
} else if (what == "case.idx") {
lav_object_inspect_case_idx(object,
drop.list.single.group = drop.list.single.group)
} else if (what == "ngroups") {
object@Data@ngroups
} else if (what == "group") {
object@Data@group
} else if (what == "cluster") {
object@Data@cluster
} else if (what == "nlevels") {
object@Data@nlevels
} else if (what == "nclusters") {
lav_object_inspect_cluster_info(object, level = 2L,
what = "nclusters",
drop.list.single.group = drop.list.single.group)
} else if (what == "ncluster.size") {
lav_object_inspect_cluster_info(object, level = 2L,
what = "ncluster.size",
drop.list.single.group = drop.list.single.group)
} else if (what == "cluster.size") {
lav_object_inspect_cluster_info(object, level = 2L,
what = "cluster.size",
drop.list.single.group = drop.list.single.group)
} else if (what == "cluster.id") {
lav_object_inspect_cluster_info(object, level = 2L,
what = "cluster.id",
drop.list.single.group = drop.list.single.group)
} else if (what == "cluster.idx") {
lav_object_inspect_cluster_info(object, level = 2L,
what = "cluster.idx",
drop.list.single.group = drop.list.single.group)
} else if (what == "cluster.label") {
lav_object_inspect_cluster_info(object, level = 2L,
what = "cluster.label",
drop.list.single.group = drop.list.single.group)
} else if (what == "cluster.sizes") {
lav_object_inspect_cluster_info(object, level = 2L,
what = "cluster.sizes",
drop.list.single.group = drop.list.single.group)
} else if (what == "average.cluster.size") {
lav_object_inspect_cluster_info(object, level = 2L,
what = "average.cluster.size",
drop.list.single.group = drop.list.single.group)
} else if (what == "ordered") {
object@Data@ordered
} else if (what == "group.label") {
object@Data@group.label
} else if (what == "level.label") {
object@Data@level.label
} else if (what == "nobs") {
unlist(object@Data@nobs)
} else if (what == "norig") {
unlist(object@Data@norig)
} else if (what == "ntotal") {
sum(unlist(object@Data@nobs))
} else if (what == "coverage") {
lav_object_inspect_missing_coverage(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what %in% c("patterns", "pattern")) {
lav_object_inspect_missing_patterns(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "empty.idx") {
lav_object_inspect_empty_idx(object,
drop.list.single.group = drop.list.single.group)
#### rsquare ####
} else if (what == "rsquare" || what == "r-square" || what == "r2") {
lav_object_inspect_rsquare(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
#### model-implied sample statistics ####
} else if (what == "implied" || what == "fitted" ||
what == "expected" || what == "exp") {
lav_object_inspect_implied(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "resid" || what == "res" || what == "residual" ||
what == "residuals") {
lav_object_inspect_residuals(object, h1 = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "cov.lv" || what == "veta") {
lav_object_inspect_cov_lv(object,
correlation.metric = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "cor.lv") {
lav_object_inspect_cov_lv(object,
correlation.metric = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "mean.lv" || what == "eeta") {
lav_object_inspect_mean_lv(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "cov.all") {
lav_object_inspect_cov_all(object,
correlation.metric = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "cor.all") {
lav_object_inspect_cov_all(object,
correlation.metric = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "cov.ov" || what == "sigma" || what == "sigma.hat") {
lav_object_inspect_cov_ov(object,
correlation.metric = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "cor.ov") {
lav_object_inspect_cov_ov(object,
correlation.metric = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "mean.ov" || what == "mu" || what == "mu.hat") {
lav_object_inspect_mean_ov(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "th" || what == "thresholds") {
lav_object_inspect_th(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "th.idx") {
lav_object_inspect_th_idx(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "vy") {
lav_object_inspect_vy(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what %in% c("fs.reliability", "fs.rel", "fs.reliabilities")) {
lav_object_inspect_fs_determinacy(object, squared = TRUE,
fs.method = "regression",
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what %in% c("fs.determinacy", "fs.det", "fs.determin",
"fs.determinacies")) {
lav_object_inspect_fs_determinacy(object, squared = FALSE,
fs.method = "regression",
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what %in% c("fs.reliability.bartlett",
"fs.reliability.Bartlett",
"fs.rel.bartlett", "fs.rel.Bartlett",
"fs.reliabilities.bartlett",
"fs.reliabilities.Bartlett")) {
lav_object_inspect_fs_determinacy(object, squared = TRUE,
fs.method = "Bartlett",
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what %in% c("fs.determinacy.bartlett",
"fs.determinacy.Bartlett",
"fs.det.bartlett", "fs.det.Bartlett",
"fs.determin.bartlett", "fs.determin.Bartlett",
"fs.determinacies.bartlett",
"fs.determinacies.Bartlett")) {
lav_object_inspect_fs_determinacy(object, squared = FALSE,
fs.method = "Bartlett",
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
#### specific model matrices? ####
} else if (what == "theta" || what == "theta.cov") {
lav_object_inspect_theta(object, correlation.metric = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "theta.cor") {
lav_object_inspect_theta(object, correlation.metric = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
#### (squared) Mahalanobis distances ####
} else if (what == "mdist2.fs") {
lav_object_inspect_mdist2(object, type = "lv", squared = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "mdist2.resid") {
lav_object_inspect_mdist2(object, type = "resid", squared = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "mdist.fs") {
lav_object_inspect_mdist2(object, type = "lv", squared = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "mdist.resid") {
lav_object_inspect_mdist2(object, type = "resid", squared = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
#### convergence, meanstructure, categorical ####
} else if (what == "converged") {
object@optim$converged
} else if (what == "iterations" ||
what == "iter" ||
what == "niter") {
object@optim$iterations
} else if (what == "meanstructure") {
object@Model@meanstructure
} else if (what == "categorical") {
object@Model@categorical
} else if (what == "fixed.x") {
object@Model@fixed.x
} else if (what == "parameterization") {
object@Model@parameterization
} else if (what == "npar") {
lav_object_inspect_npar(object, type = "free")
} else if (what == "coef") {
# this breaks simsem and semTools -- 0.6-1
# lav_object_inspect_coef(object, type = "free",
# add.labels = add.labels, add.class = add.class)
lav_object_inspect_modelmatrices(object, what = "est",
type = "free", add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
#### NACOV samplestats ####
} else if (what == "gamma") {
lav_object_inspect_sampstat_gamma(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
#### gradient, Hessian, information, first.order, vcov ####
} else if (what == "gradient") {
lav_object_inspect_gradient(object,
add.labels = add.labels, add.class = add.class, logl = FALSE)
} else if (what == "gradient.logl") {
lav_object_inspect_gradient(object,
add.labels = add.labels, add.class = add.class, logl = TRUE)
} else if (what == "optim.gradient") {
lav_object_inspect_gradient(object,
add.labels = add.labels, add.class = add.class, optim = TRUE)
} else if (what == "hessian") {
lav_object_inspect_hessian(object,
add.labels = add.labels, add.class = add.class)
} else if (what == "information") {
lav_object_inspect_information(object, information = "default",
augmented = FALSE, inverted = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "information.expected") {
lav_object_inspect_information(object, information = "expected",
augmented = FALSE, inverted = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "information.observed") {
lav_object_inspect_information(object, information = "observed",
augmented = FALSE, inverted = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "information.first.order" ||
what == "information.firstorder" ||
what == "first.order") {
lav_object_inspect_information(object, information = "first.order",
augmented = FALSE, inverted = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "augmented.information") {
lav_object_inspect_information(object, information = "default",
augmented = TRUE, inverted = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "augmented.information.expected") {
lav_object_inspect_information(object, information = "expected",
augmented = TRUE, inverted = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "augmented.information.observed") {
lav_object_inspect_information(object, information = "observed",
augmented = TRUE, inverted = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "augmented.information.first.order" ||
what == "augmented.first.order") {
lav_object_inspect_information(object, information = "first.order",
augmented = TRUE, inverted = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "inverted.information") {
lav_object_inspect_information(object, information = "default",
augmented = TRUE, inverted = TRUE,
add.labels = add.labels, add.class = add.class)
} else if (what == "inverted.information.expected") {
lav_object_inspect_information(object, information = "expected",
augmented = TRUE, inverted = TRUE,
add.labels = add.labels, add.class = add.class)
} else if (what == "inverted.information.observed") {
lav_object_inspect_information(object, information = "observed",
augmented = TRUE, inverted = TRUE,
add.labels = add.labels, add.class = add.class)
} else if (what == "inverted.information.first.order" ||
what == "inverted.first.order") {
lav_object_inspect_information(object, information = "first.order",
augmented = TRUE, inverted = TRUE,
add.labels = add.labels, add.class = add.class)
} else if (what == "h1.information") {
lav_object_inspect_h1_information(object, information = "default",
h1.information = "default", inverted = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "h1.information.expected") {
lav_object_inspect_h1_information(object, information = "expected",
h1.information = "default", inverted = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "h1.information.observed") {
lav_object_inspect_h1_information(object, information = "observed",
h1.information = "default", inverted = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "h1.information.first.order" ||
what == "h1.information.firstorder" ||
what == "h1.first.order") {
lav_object_inspect_h1_information(object,
information = "first.order", h1.information = "default",
inverted = FALSE, add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "vcov") {
lav_object_inspect_vcov(object,
standardized = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.std.all" || what == "vcov.standardized" ||
what == "vcov.std") {
lav_object_inspect_vcov(object,
standardized = TRUE, type = "std.all",
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.std.lv") {
lav_object_inspect_vcov(object,
standardized = TRUE, type = "std.lv",
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.std.nox") {
lav_object_inspect_vcov(object,
standardized = TRUE, type = "std.nox",
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.def") {
lav_object_inspect_vcov_def(object, joint = FALSE,
standardized = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.def.std.all" || what == "vcov.def.standardized" ||
what == "vcov.def.std") {
lav_object_inspect_vcov_def(object, joint = FALSE,
standardized = TRUE, type = "std.all",
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.def.std.lv") {
lav_object_inspect_vcov_def(object, joint = FALSE,
standardized = TRUE, type = "std.lv",
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.def.std.nox") {
lav_object_inspect_vcov_def(object, joint = FALSE,
standardized = TRUE, type = "std.nox",
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.def.joint") {
lav_object_inspect_vcov_def(object, joint = TRUE,
standardized = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.def.joint.std.all" ||
what == "vcov.def.joint.standardized" ||
what == "vcov.def.joint.std") {
lav_object_inspect_vcov_def(object, joint = TRUE,
standardized = TRUE, type = "std.all",
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.def.joint.std.lv") {
lav_object_inspect_vcov_def(object, joint = TRUE,
standardized = TRUE, type = "std.lv",
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.def.joint.std.nox") {
lav_object_inspect_vcov_def(object, joint = TRUE,
standardized = TRUE, type = "std.nox",
add.labels = add.labels, add.class = add.class)
} else if (what == "ugamma" || what == "ug" || what == "u.gamma") {
lav_object_inspect_UGamma(object,
add.labels = add.labels, add.class = add.class)
} else if (what == "ufromugamma" || what == "u") {
lav_object_inspect_UfromUGamma(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
### jacobians ####
} else if (what == "delta") {
lav_object_inspect_delta(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "delta.rownames") {
lav_object_inspect_delta_rownames(object,
drop.list.single.group = drop.list.single.group)
### casewise loglikehoods ###
} else if (what == "loglik.casewise") {
lav_object_inspect_loglik_casewise(object, log. = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "lik.casewise") {
lav_object_inspect_loglik_casewise(object, log. = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
# multilevel #
} else if (what == "icc") {
lav_object_inspect_icc(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "ranef") {
lav_object_inspect_ranef(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
# post-checking
} else if (what == "post.check" || what == "post") {
lav_object_post_check(object)
# options
} else if (what == "options" || what == "lavoptions") {
object@Options
# version
} else if (what == "version") {
object@version
# call
} else if (what == "call") {
as.list(object@call)
# timing
} else if (what == "timing") {
object@timing
# optim
} else if (what == "optim") {
object@optim
# test
} else if (what == "test") {
object@test
# zero cell tables
} else if (what == "zero.cell.tables") {
lav_object_inspect_zero_cell_tables(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
#### not found ####
} else {
lav_msg_stop(gettextf(
"%1$s argument unknown: %2$s",
"what", lav_msg_view(what)
))
}
}
# helper functions (mostly to deal with older 'object' that may have
# been saved somewhere)
lav_object_inspect_est <- function(object, unrotated = FALSE) {
if (inherits(object, "lavaan")) {
# from 0.5-19, they are in the partable
if (!is.null(object@ParTable$est)) {
if (unrotated) {
return.value <- object@ParTable$est.unrotated
} else {
return.value <- object@ParTable$est # if this changes, tag @TDJorgensen in commit message
}
} else if (.hasSlot(object, "Fit")) {
# in < 0.5-19, we should look in @Fit@est
return.value <- object@Fit@est
} else {
partable <- parTable(object)
return.value <- rep(as.numeric(NA), length(partable$lhs))
}
} else {
# try generic coef()
return.value <- coef(object, type = "user")
if (is.matrix(return.value)) {
# lavaanList?
return.value <- rowMeans(return.value)
}
}
return.value
}
lav_object_inspect_se <- function(object) {
# from 0.5-19, they are in the partable
if (!is.null(object@ParTable$se)) {
return.value <- object@ParTable$se
} else if (.hasSlot(object, "Fit")) {
# in < 0.5-19, we should look in @Fit@se
return.value <- object@Fit@se
} else {
partable <- parTable(object)
return.value <- rep(as.numeric(NA), length(partable$lhs))
}
return.value
}
lav_object_inspect_std_se <- function(object) {
if (!is.null(object@ParTable$se.std)) {
return.value <- object@ParTable$se.std
} else {
tmp.std <- standardizedSolution(object)
return.value <- tmp.std$se
}
return.value
}
lav_object_inspect_start <- function(object) {
# from 0.5-19, they are in the partable
if (!is.null(object@ParTable$start)) {
return.value <- object@ParTable$start
} else {
# in < 0.5-19, we should look in @Fit@start
return.value <- object@Fit@start
}
return.value
}
lav_object_inspect_boot <- function(object, add.labels = FALSE,
add.class = FALSE) {
if (object@Options$se != "bootstrap" &&
!any(c("bootstrap", "bollen.stine") %in% object@Options$test)) {
lav_msg_stop(gettext("bootstrap was not used."))
}
# from 0.5-19. they are in a separate slot
tmp <- try(slot(object, "boot"), silent = TRUE)
if (inherits(tmp, "try-error")) {
# older version of object?
est <- lav_object_inspect_est(object)
tmp.boot <- attr(est, "tmp.boot.COEF")
} else {
# 0.5-19 way
tmp.boot <- object@boot$coef
}
# add coef names
if (add.labels) {
colnames(tmp.boot) <- names(coef(object))
}
# add class
if (add.class) {
class(tmp.boot) <- c("lavaan.matrix", "matrix")
}
tmp.boot
}
lav_object_inspect_modelmatrices <- function(object, what = "free", # nolint
type = "free", add.labels = FALSE, add.class = FALSE,
list.by.group = FALSE,
drop.list.single.group = FALSE) {
glist <- object@Model@GLIST
current.verbose <- lav_verbose()
if (what == "dx.free") {
if (lav_verbose(FALSE)) on.exit(lav_verbose(current.verbose), TRUE)
tmp.dx <- lav_model_gradient(
lavmodel = object@Model,
GLIST = NULL,
lavsamplestats = object@SampleStats,
lavdata = object@Data,
lavcache = object@Cache,
type = "free",
group.weight = TRUE,
ceq.simple = TRUE,
Delta = NULL)
} else if (what == "dx.all") {
if (lav_verbose(FALSE)) on.exit(lav_verbose(current.verbose), TRUE)
glist <- lav_model_gradient(lavmodel = object@Model,
GLIST = NULL,
lavsamplestats = object@SampleStats,
lavdata = object@Data,
lavcache = object@Cache,
type = "allofthem",
group.weight = TRUE,
ceq.simple = FALSE,
Delta = NULL)
names(glist) <- names(object@Model@GLIST)
} else if (what == "std.all") {
tmp.std <- lav_standardize_all(object)
} else if (what == "std.lv") {
tmp.std <- lav_standardize_lv(object)
} else if (what == "std.nox") {
tmp.std <- lav_standardize_all_nox(object)
} else if (what == "se") {
tmp.se <- lav_object_inspect_se(object)
} else if (what == "std.se") {
tmp.se <- lav_object_inspect_std_se(object)
} else if (what == "start") {
tmp.start <- lav_object_inspect_start(object)
} else if (what == "est") {
tmp.est <- lav_object_inspect_est(object)
} else if (what == "est.unrotated") {
if (!is.null(object@Options$rotation) &&
object@Options$rotation == "none") {
tmp.est <- lav_object_inspect_est(object, unrotated = FALSE)
} else {
tmp.est <- lav_object_inspect_est(object, unrotated = TRUE)
}
}
for (mm in seq_along(glist)) {
if (add.labels) {
dimnames(glist[[mm]]) <- object@Model@dimNames[[mm]]
}
if (what == "free") {
# fill in free parameter counts
if (type == "free") {
m.el.idx <- object@Model@m.free.idx[[mm]]
x.el.idx <- object@Model@x.free.idx[[mm]]
# } else if(type == "unco") {
# m.el.idx <- object@Model@m.unco.idx[[mm]]
# x.el.idx <- object@Model@x.unco.idx[[mm]]
} else if (type == "partable") {
m.el.idx <- object@Model@m.user.idx[[mm]]
x.el.idx <- object@Model@x.user.idx[[mm]]
} else {
lav_msg_stop(gettextf(
"%1$s argument unknown: %2$s",
"type", lav_msg_view(type)
))
}
# erase everything
glist[[mm]][, ] <- 0.0
glist[[mm]][m.el.idx] <- x.el.idx
} else if (what == "se" || what == "std.se") {
# fill in standard errors
m.user.idx <- object@Model@m.user.idx[[mm]]
x.user.idx <- object@Model@x.user.idx[[mm]]
# erase everything
glist[[mm]][, ] <- 0.0
glist[[mm]][m.user.idx] <- tmp.se[x.user.idx]
} else if (what == "start") {
# fill in starting values
m.user.idx <- object@Model@m.user.idx[[mm]]
x.user.idx <- object@Model@x.user.idx[[mm]]
glist[[mm]][m.user.idx] <- tmp.start[x.user.idx]
} else if (what %in% c("est", "est.unrotated")) {
# fill in estimated parameter values
m.user.idx <- object@Model@m.user.idx[[mm]]
x.user.idx <- object@Model@x.user.idx[[mm]]
glist[[mm]][m.user.idx] <- tmp.est[x.user.idx]
} else if (what == "dx.free") {
# fill in derivatives free parameters
m.el.idx <- object@Model@m.free.idx[[mm]]
x.el.idx <- object@Model@x.free.idx[[mm]]
# erase everything
glist[[mm]][, ] <- 0.0
glist[[mm]][m.el.idx] <- tmp.dx[x.el.idx]
} else if (what %in% c("std.all", "std.lv", "std.nox")) {
m.user.idx <- object@Model@m.user.idx[[mm]]
x.user.idx <- object@Model@x.user.idx[[mm]]
glist[[mm]][m.user.idx] <- tmp.std[x.user.idx]
}
# class
if (add.class) {
if (object@Model@isSymmetric[mm]) {
class(glist[[mm]]) <- c("lavaan.matrix.symmetric", "matrix")
} else {
class(glist[[mm]]) <- c("lavaan.matrix", "matrix")
}
}
}
# try to reflect `equality constraints'
con.flag <- FALSE
if (what == "free" && object@Model@eq.constraints) {
# extract constraints from parameter table
partable <- parTable(object)
tmp.con <- partable[partable$op %in% c("==", "<", ">"),
c("lhs", "op", "rhs")]
rownames(tmp.con) <- NULL
# replace 'labels' by parameter numbers
tmp.id <- lav_partable_constraints_label_id(partable)
tmp.label <- names(tmp.id)
for (con in seq_len(nrow(tmp.con))) {
# lhs
lhs.labels <- all.vars(as.formula(paste("~", tmp.con[con, "lhs"])))
if (length(lhs.labels) > 0L) {
# par id
lhs.freeid <- tmp.id[match(lhs.labels, tmp.label)]
# substitute
tmp <- tmp.con[con, "lhs"]
for (pat in seq_along(lhs.labels)) {
tmp <- sub(lhs.labels[pat], lhs.freeid[pat], tmp)
}
tmp.con[con, "lhs"] <- tmp
}
# rhs
rhs.labels <- all.vars(as.formula(paste("~", tmp.con[con, "rhs"])))
if (length(rhs.labels) > 0L) {
# par id
rhs.freeid <- tmp.id[match(rhs.labels, tmp.label)]
# substitute
tmp <- tmp.con[con, "rhs"]
for (pat in seq_along(rhs.labels)) {
tmp <- sub(rhs.labels[pat], rhs.freeid[pat], tmp)
}
tmp.con[con, "rhs"] <- tmp
}
} # con
# add this info at the top
# glist <- c(constraints = list(tmp.con), glist)
# no, not a good idea, it does not work with list.by.group
# add it as a 'header' attribute?
attr(tmp.con, "header") <- "Note: model contains equality constraints:"
con.flag <- TRUE
}
# should we group them per block?
if (list.by.group) {
lavmodel <- object@Model
nmat <- lavmodel@nmat
return.value <- vector("list", length = lavmodel@nblocks)
for (b in seq_len(lavmodel@nblocks)) {
# which mm belong to this block?
mm.in.group <- 1:nmat[b] + cumsum(c(0, nmat))[b]
return.value[[b]] <- glist[mm.in.group]
}
if (lavmodel@nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (lavmodel@nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
} else {
return.value <- glist
}
# header
if (con.flag) {
attr(return.value, "header") <- tmp.con
}
# lavaan.list
if (add.class) {
class(return.value) <- c("lavaan.list", "list")
}
return.value
}
# - fixme, should we export this function?
# - since 0.5-21, conditional.x = TRUE returns residual sample statistics
# for ML, we have both joint and residual cov/var/...; but for
# categorical = TRUE, we only have residual cov/var...; so, we
# only return residual in both cases, whenever residual
# - since 0.6-3, we always extract the values from the @h1 slot (if present)
# if meanstructure = FALSE, do NOT include $mean elements any longer
lav_object_inspect_sampstat <- function(object, h1 = TRUE, # nolint
std = FALSE,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# old (<0.6) object?
if (!.hasSlot(object@Data, "block.label")) {
object@Data@block.label <- object@Data@group.label
}
nblocks <- object@Model@nblocks
ov.names <- object@pta$vnames$ov
ov.names.res <- object@pta$vnames$ov.nox
ov.names.x <- object@pta$vnames$ov.x
# slots
lavsamplestats <- object@SampleStats
lavmodel <- object@Model
# if nlevels, override h1 to be TRUE, and set conditional.x = FALSE
if (object@Data@nlevels > 1L) {
h1 <- TRUE
conditional.x <- FALSE # for now (0.6-12)
} else {
conditional.x <- lavmodel@conditional.x
}
# check if we have a non-empty @h1 slot
if (!.hasSlot(object, "h1")) {
h1 <- FALSE
} else if (length(object@h1) == 0L) {
h1 <- FALSE
} else {
h1.implied <- object@h1$implied
}
# if h1 = FALSE and nlevels > 1L, nothing to show...
if (!h1 && object@Data@nlevels > 1L) {
lav_msg_stop(gettext(
"sample statistics not available; refit with option h1 = TRUE"))
}
return.value <- vector("list", length = nblocks)
for (b in seq_len(nblocks)) {
if (!conditional.x) {
# covariance matrix
if (h1) {
return.value[[b]]$cov <- h1.implied$cov[[b]]
} else {
return.value[[b]]$cov <- lavsamplestats@cov[[b]]
}
if (std) {
diag.orig <- diag(return.value[[b]]$cov)
return.value[[b]]$cov <- cov2cor(return.value[[b]]$cov)
}
if (add.labels && !is.null(return.value[[b]]$cov)) {
rownames(return.value[[b]]$cov) <- colnames(return.value[[b]]$cov) <-
ov.names[[b]]
}
if (add.class) {
class(return.value[[b]]$cov) <- c("lavaan.matrix.symmetric", "matrix")
}
# mean vector
if (lavmodel@meanstructure) {
if (h1) {
return.value[[b]]$mean <- as.numeric(h1.implied$mean[[b]])
} else {
return.value[[b]]$mean <- as.numeric(lavsamplestats@mean[[b]])
}
if (std) {
diag.orig[diag.orig < .Machine$double.eps] <- NA
return.value[[b]]$mean <- return.value[[b]]$mean / sqrt(diag.orig)
}
if (add.labels) {
names(return.value[[b]]$mean) <- ov.names[[b]]
}
if (add.class) {
class(return.value[[b]]$mean) <- c("lavaan.vector", "numeric")
}
}
# thresholds
if (lavmodel@categorical) {
if (h1) {
return.value[[b]]$th <- as.numeric(h1.implied$th[[b]])
} else {
return.value[[b]]$th <- as.numeric(lavsamplestats@th[[b]])
}
if (length(lavmodel@num.idx[[b]]) > 0L) {
num.idx <- which(lavmodel@th.idx[[b]] == 0)
return.value[[b]]$th <- return.value[[b]]$th[-num.idx]
}
# FIXME: what to do if std = TRUE (depends on delta/theta)
if (add.labels) {
names(return.value[[b]]$th) <- object@pta$vnames$th[[b]]
}
if (add.class) {
class(return.value[[b]]$th) <- c("lavaan.vector", "numeric")
}
}
# !conditional.x
} else {
# if conditional.x = TRUE
# residual covariance matrix
if (h1) {
return.value[[b]]$res.cov <- h1.implied$res.cov[[b]]
} else {
return.value[[b]]$res.cov <- lavsamplestats@res.cov[[b]]
}
if (std) {
diag.orig <- diag(return.value[[b]]$res.cov)
return.value[[b]]$res.cov <- cov2cor(return.value[[b]]$res.cov)
}
if (add.labels) {
rownames(return.value[[b]]$res.cov) <-
colnames(return.value[[b]]$res.cov) <-
ov.names.res[[b]]
}
if (add.class) {
class(return.value[[b]]$res.cov) <-
c("lavaan.matrix.symmetric", "matrix")
}
# intercepts
if (lavmodel@meanstructure) {
if (h1) {
return.value[[b]]$res.int <- as.numeric(h1.implied$res.int[[b]])
} else {
return.value[[b]]$res.int <- as.numeric(lavsamplestats@res.int[[b]])
}
if (std) {
diag.orig[diag.orig < .Machine$double.eps] <- NA
return.value[[b]]$res.int <- return.value[[b]]$res.int /
sqrt(diag.orig)
}
if (add.labels) {
names(return.value[[b]]$res.int) <- ov.names.res[[b]]
}
if (add.class) {
class(return.value[[b]]$res.int) <- c("lavaan.vector", "numeric")
}
}
# thresholds
if (lavmodel@categorical) {
if (h1) {
return.value[[b]]$res.th <- as.numeric(h1.implied$res.th[[b]])
} else {
return.value[[b]]$res.th <- as.numeric(lavsamplestats@res.th[[b]])
}
if (length(lavmodel@num.idx[[b]]) > 0L) {
num.idx <- which(lavmodel@th.idx[[b]] == 0)
return.value[[b]]$res.th <- return.value[[b]]$res.th[-num.idx]
}
# FIXME: if std: what to do?
if (add.labels) {
names(return.value[[b]]$res.th) <- object@pta$vnames$th[[b]]
}
if (add.class) {
class(return.value[[b]]$res.th) <- c("lavaan.vector", "numeric")
}
}
# slopes
if (lavmodel@nexo[b] > 0L) {
if (h1) {
return.value[[b]]$res.slopes <- h1.implied$res.slopes[[b]]
} else {
return.value[[b]]$res.slopes <- lavsamplestats@res.slopes[[b]]
}
# FIXME: if std: what to do? (here: b.z = b * s.x /s.y)
if (std) {
tmp.y <- matrix(sqrt(diag.orig),
nrow(return.value[[b]]$res.slopes),
ncol(return.value[[b]]$res.slopes))
tmp.x <- matrix(sqrt(diag(lavsamplestats@cov.x[[b]])),
nrow(return.value[[b]]$res.slopes),
ncol(return.value[[b]]$res.slopes), byrow = TRUE)
return.value[[b]]$res.slopes <- return.value[[b]]$res.slopes /
tmp.y * tmp.x
}
if (add.labels) {
rownames(return.value[[b]]$res.slopes) <- ov.names.res[[b]]
colnames(return.value[[b]]$res.slopes) <- ov.names.x[[b]]
}
if (add.class) {
class(return.value[[b]]$res.slopes) <- c("lavaan.matrix", "matrix")
}
}
# cov.x
if (lavmodel@nexo[b] > 0L) {
return.value[[b]]$cov.x <- lavsamplestats@cov.x[[b]]
if (std) {
diag.orig <- diag(return.value[[b]]$cov.x)
return.value[[b]]$cov.x <- cov2cor(return.value[[b]]$cov.x)
}
if (add.labels) {
rownames(return.value[[b]]$cov.x) <- ov.names.x[[b]]
colnames(return.value[[b]]$cov.x) <- ov.names.x[[b]]
}
if (add.class) {
class(return.value[[b]]$cov.x) <-
c("lavaan.matrix.symmetric", "matrix")
}
}
# mean.x
if (lavmodel@nexo[b] > 0L) {
return.value[[b]]$mean.x <- as.numeric(object@SampleStats@mean.x[[b]])
if (std) {
diag.orig[diag.orig < .Machine$double.eps] <- NA
return.value[[b]]$mean.x <- return.value[[b]]$mean.x / sqrt(diag.orig)
}
if (add.labels) {
names(return.value[[b]]$mean.x) <- ov.names.x[[b]]
}
if (add.class) {
class(return.value[[b]]$mean.x) <- c("lavaan.vector", "numeric")
}
}
} # conditional.x
# stochastic weights
if (lavmodel@group.w.free) {
# to be consistent with the 'implied' values,
# transform so group.w is the 'log(group.freq)'
return.value[[b]]$group.w <-
log(lavsamplestats@group.w[[b]] * lavsamplestats@ntotal)
if (add.labels) {
names(return.value[[b]]$group.w) <- "w"
}
if (add.class) {
class(return.value[[b]]$group.w) <- c("lavaan.vector", "numeric")
}
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_data <- function(object, add.labels = FALSE,
drop.list.single.group = FALSE) {
n.g <- object@Data@ngroups
if (object@Model@conditional.x) {
return.value <- vector("list", length = n.g)
for (g in 1:n.g) {
return.value[[g]] <- cbind(object@Data@X[[g]],
object@Data@eXo[[g]])
}
} else {
return.value <- object@Data@X
}
if (add.labels) {
for (g in 1:n.g) {
if (object@Model@conditional.x) {
colnames(return.value[[g]]) <- c(object@Data@ov.names[[g]],
object@Data@ov.names.x[[g]])
} else {
colnames(return.value[[g]]) <- object@Data@ov.names[[g]]
}
}
}
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
lav_object_inspect_case_idx <- function(object,
drop.list.single.group = FALSE) {
n.g <- object@Data@ngroups
return.value <- object@Data@case.idx
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
# lav_object_inspect_case_idx <- function(object, level = 1L,
# drop.list.single.group = FALSE) {
# #FIXME: if lavaan ever allows 3-level or cross-classifed models,
# # "level=" should be a character indicating the clustering variable
#
# n.g <- object@Data@ngroups
# nlevels <- object@Data@nlevels
# if (nlevels == 1L) level <- 1L
# # if what="cluster.idx" for single-level model
#
# if (level == 2L) {
# # level-2 (cluster) IDs
# return.value <- lapply(object@Data@Lp, function(gg)
# gg$cluster.id[[2]][ gg$cluster.idx[[2]] ])
# #FIXME: update if lavaan ever accepts 3-level or
# cross-classified models
#
# } else return.value <- object@Data@case.idx # level-1 (casewise) IDs
#
# if(n.g == 1L && drop.list.single.group) {
# return.value <- return.value[[1]]
# } else {
# if(length(object@Data@group.label) > 0L) {
# names(return.value) <- unlist(object@Data@group.label)
# }
# }
# return.value
# }
#
# cluster info
lav_object_inspect_cluster_info <- function( # nolint
object,
what = "cluster.size",
level = 2L,
drop.list.single.group = FALSE) {
n.g <- object@Data@ngroups
nlevels <- object@Data@nlevels
# just in case we have no clusters
if (nlevels == 1L) {
if (what %in% c("nclusters", "ncluster.size", "cluster.id")) {
return.value <- as.list(rep(1L, n.g))
} else if (what %in% c("cluster.size", "cluster.sizes")) {
return.value <- object@Data@nobs
} else if (what %in% c("cluster.idx", "cluster.label")) {
# everybody belongs to cluster 1
return.value <- lapply(seq_len(n.g),
function(gg) rep(1L, object@Data@nobs[[gg]]))
}
}
# if we do have clusters
if (nlevels > 1L) {
return.value <- vector("list", length = n.g)
for (g in seq_len(n.g)) {
tmp.lp <- object@Data@Lp[[g]]
if (what == "nclusters") {
return.value[[g]] <- tmp.lp$nclusters[[level]]
} else if (what == "ncluster.size") {
return.value[[g]] <- tmp.lp$ncluster.size[[level]]
} else if (what == "cluster.size") {
return.value[[g]] <- tmp.lp$cluster.size[[level]]
} else if (what == "cluster.id") {
return.value[[g]] <- tmp.lp$cluster.id[[level]]
} else if (what == "cluster.idx") {
return.value[[g]] <- tmp.lp$cluster.idx[[level]]
} else if (what == "cluster.label") {
return.value[[g]] <-
tmp.lp$cluster.id[[level]][tmp.lp$cluster.idx[[level]]]
} else if (what == "cluster.sizes") {
return.value[[g]] <- tmp.lp$cluster.sizes[[level]]
} else if (what == "average.cluster.size") {
nn.g <- object@Data@nobs[[g]]
cluster.size <- tmp.lp$cluster.size[[level]]
nclusters <- tmp.lp$nclusters[[level]]
return.value[[g]] <- (nn.g^2 - sum(cluster.size^2)) /
(nn.g * (nclusters - 1L))
}
} # g
} # nlevels > 1L
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
# count the number of clusters, or obtain tmp.n within each cluster
# lav_object_inspect_ncluster <- function(object, sizes = FALSE, #level = 2L,
# drop.list.single.group = FALSE) {
# n.g <- object@Data@ngroups
# nlevels <- object@Data@nlevels
#
# if (nlevels == 1L) {
# # single-level model, return sample size(s) or count 1 cluster per group
# return.value <- if (sizes) unlist(object@Data@nobs) else rep(1L, n.g)
#
# } else if (sizes) {
# # for each group, a vector of cluster sizes
# return.value <- lapply(object@Data@Lp, function(gg) gg$cluster.size[[2]])
# #FIXME: update if lavaan ever accepts 3-level or cross-classified models
#
# if (n.g == 1L && drop.list.single.group)
# return.value <- return.value[[1]]
#
# } else {
# # number of clusters in each group
# return.value <- sapply(object@Data@Lp, function(gg) gg$nclusters[[2]])
# #FIXME: update if lavaan ever accepts 3-level or cross-classified models
# }
#
# # assign group names, if applicable
# if (n.g > 1L) names(return.value) <- unlist(object@Data@group.label)
# return.value
# }
lav_object_inspect_rsquare <- function(object, est.std.all = NULL,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
nblocks <- object@Model@nblocks
return.value <- vector("list", length = nblocks)
if (is.null(est.std.all)) {
est.std.all <- lav_standardize_all(object)
}
partable <- object@ParTable
partable$rsquare <- 1.0 - est.std.all
# no values > 1.0
partable$rsquare[partable$rsquare > 1.0] <- as.numeric(NA)
for (b in seq_len(nblocks)) {
ind.names <- partable$rhs[which(partable$op == "=~" &
partable$block == b)]
eqs.y.names <- partable$lhs[which(partable$op == "~" &
partable$block == b)]
y.names <- unique(c(ind.names, eqs.y.names))
idx <- which(partable$op == "~~" & partable$lhs %in% y.names &
partable$rhs == partable$lhs & partable$block == b)
tmp <- partable$rsquare[idx]
if (add.labels && length(tmp) > 0L) {
names(tmp) <- partable$lhs[idx]
}
if (add.class) {
class(tmp) <- c("lavaan.vector", "numeric")
}
return.value[[b]] <- tmp
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
# model implied sample stats
lav_object_inspect_implied <- function(object, # nolint
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# old (<0.6) object?
if (!.hasSlot(object@Data, "block.label")) {
object@Data@block.label <- object@Data@group.label
}
nblocks <- object@Model@nblocks
ov.names <- object@pta$vnames$ov
ov.names.res <- object@pta$vnames$ov.nox
ov.names.x <- object@pta$vnames$ov.x
# slots
lavimplied <- object@implied
lavmodel <- object@Model
# if nlevels, always set conditional.x = FALSE
if (object@Data@nlevels > 1L) {
lavimplied <- lav_model_implied_cond2uncond(lavimplied)
conditional.x <- FALSE # for now (0.6-12)
} else {
conditional.x <- lavmodel@conditional.x
}
return.value <- vector("list", length = nblocks)
for (b in seq_len(nblocks)) {
if (!conditional.x) {
# covariance matrix
return.value[[b]]$cov <- lavimplied$cov[[b]]
if (add.labels && !is.null(return.value[[b]]$cov)) {
rownames(return.value[[b]]$cov) <- colnames(return.value[[b]]$cov) <-
ov.names[[b]]
}
if (add.class) {
class(return.value[[b]]$cov) <- c("lavaan.matrix.symmetric", "matrix")
}
# mean vector
if (lavmodel@meanstructure) {
return.value[[b]]$mean <- as.numeric(lavimplied$mean[[b]])
if (add.labels) {
names(return.value[[b]]$mean) <- ov.names[[b]]
}
if (add.class) {
class(return.value[[b]]$mean) <- c("lavaan.vector", "numeric")
}
}
# thresholds
if (lavmodel@categorical) {
return.value[[b]]$th <- as.numeric(lavimplied$th[[b]])
if (length(object@Model@num.idx[[b]]) > 0L) {
num.idx <- which(object@Model@th.idx[[b]] == 0)
return.value[[b]]$th <- return.value[[b]]$th[-num.idx]
}
if (add.labels) {
names(return.value[[b]]$th) <- object@pta$vnames$th[[b]]
}
if (add.class) {
class(return.value[[b]]$th) <- c("lavaan.vector", "numeric")
}
}
# !conditional.x
} else {
# if conditional.x = TRUE
# residual covariance matrix
return.value[[b]]$res.cov <- lavimplied$res.cov[[b]]
if (add.labels) {
rownames(return.value[[b]]$res.cov) <-
colnames(return.value[[b]]$res.cov) <-
ov.names.res[[b]]
}
if (add.class) {
class(return.value[[b]]$res.cov) <-
c("lavaan.matrix.symmetric", "matrix")
}
# intercepts
if (lavmodel@meanstructure) {
return.value[[b]]$res.int <- as.numeric(lavimplied$res.int[[b]])
if (add.labels) {
names(return.value[[b]]$res.int) <- ov.names.res[[b]]
}
if (add.class) {
class(return.value[[b]]$res.int) <- c("lavaan.vector", "numeric")
}
}
# thresholds
if (lavmodel@categorical) {
return.value[[b]]$res.th <- as.numeric(lavimplied$res.th[[b]])
if (length(object@Model@num.idx[[b]]) > 0L) {
num.idx <- which(object@Model@th.idx[[b]] == 0)
return.value[[b]]$res.th <- return.value[[b]]$res.th[-num.idx]
}
if (add.labels) {
names(return.value[[b]]$res.th) <- object@pta$vnames$th[[b]]
}
if (add.class) {
class(return.value[[b]]$res.th) <- c("lavaan.vector", "numeric")
}
}
# slopes
if (lavmodel@nexo[b] > 0L) {
return.value[[b]]$res.slopes <- lavimplied$res.slopes[[b]]
if (add.labels) {
rownames(return.value[[b]]$res.slopes) <- ov.names.res[[b]]
colnames(return.value[[b]]$res.slopes) <- ov.names.x[[b]]
}
if (add.class) {
class(return.value[[b]]$res.slopes) <- c("lavaan.matrix", "matrix")
}
}
# cov.x
if (lavmodel@nexo[b] > 0L) {
return.value[[b]]$cov.x <- lavimplied$cov.x[[b]]
if (add.labels) {
rownames(return.value[[b]]$cov.x) <- ov.names.x[[b]]
colnames(return.value[[b]]$cov.x) <- ov.names.x[[b]]
}
if (add.class) {
class(return.value[[b]]$cov.x) <-
c("lavaan.matrix.symmetric", "matrix")
}
}
# mean.x
if (lavmodel@nexo[b] > 0L) {
return.value[[b]]$mean.x <- as.numeric(lavimplied$mean.x[[b]])
if (add.labels) {
names(return.value[[b]]$mean.x) <- ov.names.x[[b]]
}
if (add.class) {
class(return.value[[b]]$mean.x) <- c("lavaan.vector", "numeric")
}
}
} # conditional.x
# stochastic weights
if (lavmodel@group.w.free) {
return.value[[b]]$group.w <- lavimplied$group.w[[b]]
if (add.labels) {
names(return.value[[b]]$group.w) <- "w" # somewhat redundant
}
if (add.class) {
class(return.value[[b]]$group.w) <- c("lavaan.vector", "numeric")
}
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
# residuals: _inspect_sampstat - _inspect_implied
lav_object_inspect_residuals <- function(object, h1 = TRUE,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
lav_residuals(object, type = "raw", h1 = h1,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
}
lav_object_inspect_cov_lv <- function(object, correlation.metric = FALSE,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# compute lv covar
return.value <- computeVETA(lavmodel = object@Model, remove.dummy.lv = TRUE)
# nblocks
nblocks <- length(return.value)
# cor + labels + class
for (b in seq_len(nblocks)) {
if (correlation.metric && nrow(return.value[[b]]) > 1L) {
# note: cov2cor fails if matrix is empty!
return.value[[b]] <- cov2cor(return.value[[b]])
}
if (add.labels) {
colnames(return.value[[b]]) <- rownames(return.value[[b]]) <-
object@pta$vnames$lv[[b]]
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.matrix.symmetric", "matrix")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_mean_lv <- function(object,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# compute lv means
return.value <- computeEETA(lavmodel = object@Model,
lavsamplestats = object@SampleStats,
remove.dummy.lv = TRUE)
# nblocks
nblocks <- length(return.value)
# ensure numeric
return.value <- lapply(return.value, as.numeric)
# labels + class
for (b in seq_len(nblocks)) {
if (add.labels && length(return.value[[b]]) > 0L) {
names(return.value[[b]]) <- object@pta$vnames$lv[[b]]
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.vector", "numeric")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_cov_all <- function(object, correlation.metric = FALSE,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# compute extended model implied covariance matrix (both ov and lv)
return.value <- computeCOV(lavmodel = object@Model,
remove.dummy.lv = TRUE)
# nblocks
nblocks <- length(return.value)
# cor + labels + class
for (b in seq_len(nblocks)) {
if (correlation.metric && nrow(return.value[[b]]) > 1L) {
# note: cov2cor fails if matrix is empty!
return.value[[b]] <- cov2cor(return.value[[b]])
}
if (add.labels) {
tmp.names <- c(object@pta$vnames$ov.model[[b]],
object@pta$vnames$lv[[b]])
colnames(return.value[[b]]) <- rownames(return.value[[b]]) <- tmp.names
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.matrix.symmetric", "matrix")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_cov_ov <- function(object, correlation.metric = FALSE,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# get model-implied covariance matrix observed
if (object@Model@conditional.x) {
return.value <- object@implied$res.cov
} else {
return.value <- object@implied$cov
}
# nblocks
nblocks <- length(return.value)
# cor + labels + class
for (b in seq_len(nblocks)) {
if (correlation.metric && nrow(return.value[[b]]) > 1L) {
# note: cov2cor fails if matrix is empty!
return.value[[b]] <- cov2cor(return.value[[b]])
}
if (add.labels) {
colnames(return.value[[b]]) <- rownames(return.value[[b]]) <-
object@pta$vnames$ov.model[[b]]
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.matrix.symmetric", "matrix")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_mean_ov <- function(object,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# compute ov means
if (object@Model@conditional.x) {
return.value <- object@implied$res.int
} else {
return.value <- object@implied$mean
}
# nblocks
nblocks <- length(return.value)
# make numeric
return.value <- lapply(return.value, as.numeric)
# labels + class
for (b in seq_len(nblocks)) {
if (add.labels && length(return.value[[b]]) > 0L) {
names(return.value[[b]]) <- object@pta$vnames$ov.model[[b]]
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.vector", "numeric")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_th <- function(object,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# thresholds
if (object@Model@conditional.x) {
return.value <- object@implied$res.th
} else {
return.value <- object@implied$th
}
# nblocks
nblocks <- length(return.value)
# make numeric
return.value <- lapply(return.value, as.numeric)
# labels + class
for (b in seq_len(nblocks)) {
if (length(object@Model@num.idx[[b]]) > 0L) {
num.idx <- which(object@Model@th.idx[[b]] == 0)
return.value[[b]] <- return.value[[b]][-num.idx]
}
if (add.labels && length(return.value[[b]]) > 0L) {
names(return.value[[b]]) <- object@pta$vnames$th[[b]]
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.vector", "numeric")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_th_idx <- function(object,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# thresholds idx
return.value <- object@SampleStats@th.idx
# nblocks
nblocks <- length(return.value)
# labels + class
for (b in seq_len(nblocks)) {
if (add.labels && length(return.value[[b]]) > 0L) {
names(return.value[[b]]) <- object@SampleStats@th.names[[b]]
}
if (add.class && !is.null(return.value[[b]])) {
class(return.value[[b]]) <- c("lavaan.vector", "numeric")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_vy <- function(object,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# 'unconditional' model-implied variances
# - same as diag(Sigma.hat) if all Y are continuous)
# - 1.0 (or delta^2) if categorical
# - if also Gamma, cov.x is used (only if categorical)
return.value <- computeVY(lavmodel = object@Model, GLIST = NULL,
diagonal.only = TRUE)
# nblocks
nblocks <- length(return.value)
# labels + class
for (b in seq_len(nblocks)) {
if (add.labels && length(return.value[[b]]) > 0L) {
if (object@Model@categorical) {
names(return.value[[b]]) <- object@pta$vnames$ov.nox[[b]]
} else {
names(return.value[[b]]) <- object@pta$vnames$ov[[b]]
}
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.vector", "numeric")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_fs_determinacy <- function(object,
squared = TRUE, fs.method = "regression",
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
FS <- lavPredict(object, type = "lv", method = fs.method, rel = TRUE)
return.value <- attr(FS, "rel")
# determinacies or reliabilities?
if (!squared) {
return.value <- lapply(return.value, sqrt)
}
# nblocks
nblocks <- length(return.value)
# ensure numeric
return.value <- lapply(return.value, as.numeric)
# labels + class
for (b in seq_len(nblocks)) {
if (add.labels && length(return.value[[b]]) > 0L) {
names(return.value[[b]]) <- object@pta$vnames$lv[[b]]
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.vector", "numeric")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_theta <- function(object, correlation.metric = FALSE,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# get residual covariances
return.value <- computeTHETA(lavmodel = object@Model)
# nblocks
nblocks <- length(return.value)
# labels + class
for (b in seq_len(nblocks)) {
if (correlation.metric && nrow(return.value[[b]]) > 0L) {
if (all(return.value[[b]] == 0)) {
return.value[[b]] <- return.value[[b]]
} else {
return.value[[b]] <- cov2cor(return.value[[b]])
}
}
if (add.labels && length(return.value[[b]]) > 0L) {
colnames(return.value[[b]]) <- rownames(return.value[[b]]) <-
object@pta$vnames$ov.model[[b]]
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.matrix.symmetric", "matrix")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_missing_coverage <- function(object, # nolint
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
n.g <- object@Data@ngroups
return.value <- vector("list", n.g)
for (g in 1:n.g) {
if (!is.null(object@Data@Mp[[g]])) {
return.value[[g]] <- object@Data@Mp[[g]]$coverage
} else {
nvar <- length(object@Data@ov.names[[g]])
return.value[[g]] <- matrix(1.0, nvar, nvar)
}
if (add.labels && length(return.value[[g]]) > 0L) {
colnames(return.value[[g]]) <- rownames(return.value[[g]]) <-
object@pta$vnames$ov.model[[g]]
}
if (add.class) {
class(return.value[[g]]) <- c("lavaan.matrix.symmetric", "matrix")
}
}
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
lav_object_inspect_missing_patterns <- function(object, # nolint
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
n.g <- object@Data@ngroups
return.value <- vector("list", n.g)
for (g in 1:n.g) {
if (!is.null(object@Data@Mp[[g]])) {
return.value[[g]] <- object@Data@Mp[[g]]$pat
} else {
nvar <- length(object@Data@ov.names[[g]])
return.value[[g]] <- matrix(TRUE, 1L, nvar)
rownames(return.value[[g]]) <- object@Data@nobs[[g]]
}
if (add.labels && length(return.value[[g]]) > 0L) {
colnames(return.value[[g]]) <- object@pta$vnames$ov.model[[g]]
}
if (add.class) {
class(return.value[[g]]) <- c("lavaan.matrix", "matrix")
}
}
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
lav_object_inspect_empty_idx <- function(object,
drop.list.single.group = FALSE) {
n.g <- object@Data@ngroups
# get empty idx
return.value <- vector("list", n.g)
for (g in 1:n.g) {
if (!is.null(object@Data@Mp[[g]])) {
return.value[[g]] <- object@Data@Mp[[g]]$empty.idx
} else {
return.value[[g]] <- integer(0L)
}
}
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
lav_object_inspect_wls_est <- function(object,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# old (<0.6) object?
if (!.hasSlot(object@Data, "block.label")) {
object@Data@block.label <- object@Data@group.label
}
return.value <- lav_model_wls_est(object@Model)
if (add.labels) {
tmp.names <- lav_object_inspect_delta_rownames(object,
drop.list.single.group = FALSE)
}
# nblocks
nblocks <- length(return.value)
for (b in seq_len(nblocks)) {
if (add.labels && length(return.value[[b]]) > 0L &&
object@Data@nlevels == 1L) {
names(return.value[[b]]) <- tmp.names[[b]]
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.vector", "numeric")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_wls_obs <- function(object,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# old (<0.6) object?
if (!.hasSlot(object@Data, "block.label")) {
object@Data@block.label <- object@Data@group.label
}
return.value <- object@SampleStats@WLS.obs ### FIXME: should be in @h1??
if (add.labels) {
tmp.names <- lav_object_inspect_delta_rownames(object,
drop.list.single.group = FALSE)
}
# nblocks
nblocks <- length(return.value)
for (b in seq_len(nblocks)) {
if (add.labels && length(return.value[[b]]) > 0L &&
object@Data@nlevels == 1L) {
names(return.value[[b]]) <- tmp.names[[b]]
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.vector", "numeric")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_wls_v <- function(object,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# old (<0.6) object?
if (!.hasSlot(object@Data, "block.label")) {
object@Data@block.label <- object@Data@group.label
}
# return.value <- lav_model_wls_v(lavmodel = object@Model,
# lavsamplestats = object@SampleStats,
# structured = TRUE,
# lavdata = object@Data)
# WLS.V == (traditionally) h1 expected information
return.value <- lav_model_h1_information_expected(lavobject = object)
# this affects fit measures gfi, agfi, pgfi
# nblocks
nblocks <- length(return.value)
# if estimator == "DWLS" or "ULS", we only stored the diagonal
# hence, we create a full matrix here
if (object@Options$estimator %in% c("DWLS", "ULS")) {
return.value <- lapply(return.value,
function(x) {
nr <- NROW(x)
diag(x, nrow = nr, ncol = nr)
})
}
if (add.labels) {
tmp.names <- lav_object_inspect_delta_rownames(object,
drop.list.single.group = FALSE)
}
# label + class
for (b in seq_len(nblocks)) {
# labels
if (add.labels && nrow(return.value[[b]]) > 0L &&
object@Data@nlevels == 1L) {
colnames(return.value[[b]]) <-
rownames(return.value[[b]]) <- tmp.names[[b]]
}
# class
if (add.class) {
class(return.value[[b]]) <- c("lavaan.matrix", "matrix")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_sampstat_gamma <- function(object, # nolint
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
if (!is.null(object@SampleStats@NACOV[[1]])) {
return.value <- object@SampleStats@NACOV
} else {
return.value <- lav_object_gamma(object)
}
if (add.labels) {
tmp.names <- lav_object_inspect_delta_rownames(object,
drop.list.single.group = FALSE)
}
# nblocks
nblocks <- length(return.value)
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
# labels
if (add.labels) {
colnames(return.value) <- rownames(return.value) <- tmp.names[[1]]
}
# class
if (add.class) {
class(return.value) <- c("lavaan.matrix.symmetric", "matrix")
}
} else {
if (object@Data@nlevels == 1L && length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
# labels
if (add.labels) {
for (g in seq_len(object@Data@ngroups)) {
colnames(return.value[[g]]) <-
rownames(return.value[[g]]) <- tmp.names[[g]]
}
}
# class
if (add.class) {
for (g in seq_len(object@Data@ngroups)) {
class(return.value[[g]]) <- c("lavaan.matrix.symmetric", "matrix")
}
}
} else if (object@Data@nlevels > 1L &&
length(object@Data@group.label) == 0L) {
names(return.value) <- object@Data@level.label
}
}
return.value
}
lav_object_inspect_gradient <- function(object,
add.labels = FALSE, add.class = FALSE, logl = FALSE,
optim = FALSE) {
lavmodel <- object@Model
lavdata <- object@Data
lavsamplestats <- object@SampleStats
if (optim) {
logl <- FALSE
}
if (lavsamplestats@missing.flag ||
object@Options$estimator == "PML") {
group.weight <- FALSE
} else {
group.weight <- TRUE
}
current.verbose <- lav_verbose()
if (lav_verbose(FALSE)) on.exit(lav_verbose(current.verbose), TRUE)
dx <- lav_model_gradient(lavmodel = lavmodel,
GLIST = NULL,
lavsamplestats = lavsamplestats,
lavdata = object@Data,
lavcache = object@Cache,
type = "free",
group.weight = group.weight)
lav_verbose(current.verbose)
# if logl, rescale to get gradient wrt the loglikelihood
if (logl) {
if (lavmodel@estimator %in% c("ML")) {
if (lavdata@nlevels == 1L) {
# currently, this is just a sign switch
dx <- -1 * dx
} else {
lavpartable <- object@ParTable
# gradient.log = gradient.obj * (2 * tmp.n) / nclusters
if (lavdata@ngroups == 1L) {
tmp.n <- lavdata@Lp[[1]]$nclusters[[1]]
nclusters <- lavdata@Lp[[1]]$nclusters[[2]]
dx <- dx * (2 * tmp.n) / nclusters
} else {
group.values <- lav_partable_group_values(lavpartable)
for (g in seq_len(lavdata@ngroups)) {
tmp.n <- lavdata@Lp[[g]]$nclusters[[1]]
nclusters <- lavdata@Lp[[g]]$nclusters[[2]]
g.idx <-
which((lavpartable$group ==
group.values[g])[lavpartable$free > 0L])
dx[g.idx] <- dx[g.idx] * (2 * tmp.n) / nclusters
}
}
}
} else {
# FIXME:
# non-likelihood: what to do? just switch the sign for now.
# Note: this is used in lavTestScore()
dx <- -1 * dx
}
}
# optim?
if (optim) {
# 1. scale (note: divide, not multiply!)
if (!is.null(object@optim$parscale)) {
dx <- dx / object@optim$parscale
}
# 2. pack
if (lavmodel@eq.constraints) {
dx <- as.numeric(dx %*% lavmodel@eq.constraints.K)
}
# only for PML: divide by tmp.n (to speed up convergence)
if (lavmodel@estimator == "PML") {
dx <- dx / lavsamplestats@ntotal
}
}
# labels
if (add.labels) {
if (optim && lavmodel@eq.constraints) {
tmp.names.all <- lav_partable_labels(object@ParTable, type = "free")
tmp.seq <- seq_len(length(tmp.names.all))
pack.seq <- as.numeric((tmp.seq - lavmodel@eq.constraints.k0) %*%
+lavmodel@eq.constraints.K)
ok.idx <- which(pack.seq %in% tmp.seq)
tmp.names <- rep("(eq.con)", length(pack.seq))
tmp.names[ok.idx] <- tmp.names.all[pack.seq[ok.idx]]
names(dx) <- tmp.names
} else {
names(dx) <- lav_partable_labels(object@ParTable, type = "free")
}
}
# class
if (add.class) {
class(dx) <- c("lavaan.vector", "numeric")
}
dx
}
lav_object_inspect_hessian <- function(object,
add.labels = FALSE, add.class = FALSE) {
return.value <- lav_model_hessian(lavmodel = object@Model,
lavsamplestats = object@SampleStats,
lavdata = object@Data,
lavcache = object@Cache,
lavoptions = object@Options,
group.weight = TRUE)
# labels
if (add.labels) {
colnames(return.value) <- rownames(return.value) <-
lav_partable_labels(object@ParTable, type = "free")
}
# class
if (add.class) {
class(return.value) <- c("lavaan.matrix.symmetric", "matrix")
}
return.value
}
lav_object_inspect_information <- function(object,
information = "default", augmented = FALSE, inverted = FALSE,
add.labels = FALSE, add.class = FALSE) {
if (information != "default") {
# override option
object@Options$information <- information
}
# backward compatibility
if (.hasSlot(object, "h1")) {
lavh1 <- object@h1
} else {
lavh1 <- lav_h1_implied_logl(lavdata = object@Data,
lavsamplestats = object@SampleStats,
lavoptions = object@Options)
}
return.value <- lav_model_information(lavmodel = object@Model,
lavsamplestats = object@SampleStats,
lavdata = object@Data,
lavcache = object@Cache,
lavimplied = object@implied,
lavh1 = lavh1,
lavoptions = object@Options,
extra = FALSE,
augmented = augmented,
inverted = inverted)
# labels
if (add.labels) {
tmp.names <- lav_partable_labels(object@ParTable, type = "free")
if (augmented) {
n.extra <- nrow(return.value) - length(tmp.names)
if (n.extra > 0L) {
tmp.names <- c(tmp.names, paste("aug", 1:n.extra, sep = ""))
}
}
colnames(return.value) <- rownames(return.value) <- tmp.names
}
# class
if (add.class) {
class(return.value) <- c("lavaan.matrix.symmetric", "matrix")
}
return.value
}
lav_object_inspect_h1_information <- function(object, # nolint
information = "default", h1.information = "default", inverted = FALSE,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
if (information != "default") {
# override option
object@Options$information <- information
}
if (h1.information != "default") {
# override option
object@Options$h1.information <- h1.information
}
lavmodel <- object@Model
lavdata <- object@Data
# list!
return.value <- lav_model_h1_information(lavmodel = lavmodel,
lavsamplestats = object@SampleStats,
lavdata = lavdata,
lavcache = object@Cache,
lavimplied = object@implied,
lavh1 = object@h1,
lavoptions = object@Options)
# inverted? (NOT USED)
# if(inverted) {
# return.value <- lapply(return.value, solve) # FIXME: handle errors...
# }
if (add.labels) {
tmp.names <- lav_object_inspect_delta_rownames(object,
drop.list.single.group = FALSE)
}
# labels/class per group
for (g in seq_len(lavmodel@ngroups)) {
# labels
if (add.labels) {
colnames(return.value[[g]]) <-
rownames(return.value[[g]]) <- tmp.names[[g]]
}
# class
if (add.class) {
class(return.value[[g]]) <- c("lavaan.matrix.symmetric", "matrix")
}
}
# drop list?
if (lavmodel@ngroups == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (!is.null(lavdata)) {
if (length(lavdata@group.label) > 0L) {
names(return.value) <- unlist(lavdata@group.label)
}
}
return.value
}
# only to provide a direct function to the old 'getVariability()' function
lav_object_inspect_firstorder <- function(object,
add.labels = FALSE, add.class = FALSE) {
tmp.b0 <- lav_model_information_firstorder(lavmodel = object@Model,
lavsamplestats = object@SampleStats,
lavdata = object@Data,
lavcache = object@Cache,
lavoptions = object@Options,
check.pd = FALSE,
augmented = FALSE,
inverted = FALSE)
attr(tmp.b0, "B0.group") <- NULL
return.value <- tmp.b0
# labels
if (add.labels) {
colnames(return.value) <- rownames(return.value) <-
lav_partable_labels(object@ParTable, type = "free")
}
# class
if (add.class) {
class(return.value) <- c("lavaan.matrix.symmetric", "matrix")
}
return.value
}
lav_object_inspect_vcov <- function(object, standardized = FALSE,
type = "std.all", free.only = TRUE,
add.labels = FALSE, add.class = FALSE, remove.duplicated = FALSE) {
lavmodel <- object@Model
lavoptions <- object@Options
# store partable with pta in object to use cache in called functions
if (is.null(attr(object, "vnames"))) {
object@ParTable <- lav_partable_set_cache(object@ParTable, object@pta)
}
# rotation?
# if( .hasSlot(lavmodel, "nefa") && (lavmodel@nefa > 0L) &&
# lavoptions$rotation != "none" #&& lavoptions$rotation.se == "delta"
# ) {
# rotation <- TRUE
# } else {
# rotation <- FALSE
# }
if (object@optim$npar == 0) {
return.value <- matrix(0, 0, 0)
} else {
# check if we already have it
# tmp <- try(slot(object, "vcov"), silent = TRUE)
# if( !inherits(tmp, "try-error") && !is.null(object@vcov$vcov)
# && !(rotation && standardized)) {
if (.hasSlot(object, "vcov") && !is.null(object@vcov$vcov)) {
return.value <- object@vcov$vcov # if this changes, tag @TDJorgensen in commit message
} else {
# compute it again
# if(rotation && standardized) {
# lavmodel <- lav_model_set_parameters(lavmodel,
# x = object@optim$x)
# lavoptions <- object@Options
# lavoptions$rotation.se <- "delta"
# }
return.value <- lav_model_vcov(lavmodel = lavmodel,
lavsamplestats = object@SampleStats,
lavoptions = lavoptions,
lavdata = object@Data,
lavcache = object@Cache,
lavimplied = object@implied,
lavh1 = object@h1
)
# if(rotation && !standardized) {
# # fixme: compute tmp.vcov.rot manually...
# stop("lavaan ERROR: rerun with store.vcov = TRUE")
# }
if (is.null(return.value)) {
return(return.value)
}
}
}
# strip attributes
attr(return.value, "E.inv") <- NULL
attr(return.value, "B0") <- NULL
attr(return.value, "B0.group") <- NULL
attr(return.value, "Delta") <- NULL
attr(return.value, "WLS.V") <- NULL
attr(return.value, "tmp.boot.COEF") <- NULL
attr(return.value, "tmp.boot.TEST") <- NULL
# standardized?
if (standardized) {
if (type == "std.lv") {
tmp.fun <- lav_standardize_lv_x
} else if (type == "std.all") {
tmp.fun <- lav_standardize_all_x
} else if (type == "std.nox") {
tmp.fun <- lav_standardize_all_nox_x
}
# if(rotation) {
# if(.hasSlot(object@Model, "ceq.simple.only") &&
# object@Model@ceq.simple.only) {
# x.vec <- drop(object@optim$x %*% t(object@Model@ceq.simple.K))
# } else {
# x.vec <- object@optim$x
# }
# tmp.jac <- numDeriv::jacobian(func = tmp.fun, x = x.vec,
# method = "simple",
# method.args = list(eps = 1e-03), # default is 1e-04
# lavobject = object, rotation = rotation)
# } else {
# if(.hasSlot(object@Model, "ceq.simple.only") &&
# object@Model@ceq.simple.only) {
# x <- lav_model_get_parameters(lavmodel)
# x.vec <- drop(x %*% t(object@Model@ceq.simple.K))
# } else {
x.vec <- lav_model_get_parameters(lavmodel)
# }
tmp.jac <- try(lav_func_jacobian_complex(func = tmp.fun, x = x.vec,
lavobject = object),
silent = TRUE)
if (inherits(tmp.jac, "try-error")) { # eg. pnorm()
tmp.jac <- lav_func_jacobian_simple(func = tmp.fun, x = x.vec,
lavobject = object)
}
# }
# tmp.jac contains *all* parameters in the parameter table
if (free.only) {
if (.hasSlot(object@Model, "ceq.simple.only") &&
object@Model@ceq.simple.only) {
free.idx <- which(object@ParTable$free > 0L &&
!duplicated(object@ParTable$free))
} else {
free.idx <- which(object@ParTable$free > 0L)
}
tmp.jac <- tmp.jac[free.idx, , drop = FALSE]
}
return.value <- tmp.jac %*% return.value %*% t(tmp.jac)
# force return.value to be symmetric and pd
return.value <- (return.value + t(return.value)) / 2
# return.value <- lav_matrix_symmetric_force_pd(return.value,
# tol = 1e-09) # was 1e-06 < 0.6-9
}
# labels
if (add.labels) {
# if(rotation && !free.only) {
# # todo
# } else {
colnames(return.value) <- rownames(return.value) <-
lav_partable_labels(object@ParTable,
## add "user" labels?
type = ifelse(standardized && !free.only,
"user", "free"))
# }
}
# alias?
if (remove.duplicated && lavmodel@eq.constraints) {
simple.flag <- lav_constraints_check_simple(lavmodel)
if (simple.flag) {
tmp.lab <- lav_partable_labels(object@ParTable, type = "free")
dup.flag <- duplicated(tmp.lab)
return.value <- return.value[!dup.flag, !dup.flag, drop = FALSE]
} else {
lav_msg_warn(
gettext("alias is TRUE, but equality constraints do not appear
to be simple; returning full vcov"))
}
}
# class
if (add.class) {
class(return.value) <- c("lavaan.matrix.symmetric", "matrix")
}
return.value
}
lav_object_inspect_vcov_def <- function(object, joint = FALSE,
standardized = FALSE, type = "std.all",
add.labels = FALSE, add.class = FALSE) {
lavmodel <- object@Model
lavpartable <- object@ParTable
free.idx <- which(lavpartable$free > 0L)
def.idx <- which(lavpartable$op == ":=")
joint.idx <- c(free.idx, def.idx)
if (!joint && length(def.idx) == 0L) {
return(matrix(0, 0, 0))
} else if (joint && length(joint.idx) == 0L) {
return(matrix(0, 0, 0))
}
if (standardized) {
# compute tmp.vcov for "free" parameters only
tmp.vcov <- lav_object_inspect_vcov(object,
standardized = TRUE,
type = type, free.only = FALSE,
add.labels = FALSE, add.class = FALSE)
if (joint) {
return.value <- tmp.vcov[joint.idx, joint.idx, drop = FALSE]
} else {
return.value <- tmp.vcov[def.idx, def.idx, drop = FALSE]
}
} else {
# get free parameters
x <- lav_model_get_parameters(lavmodel, type = "free")
# bootstrap or not?
if (!is.null(object@boot$coef)) {
tmp.boot <- object@boot$coef
# remove NA rows
error.idx <- attr(tmp.boot, "error.idx")
if (length(error.idx) > 0L) {
tmp.boot <- tmp.boot[-error.idx, , drop = FALSE] # drops attributes
}
tmp.boot.def <- apply(tmp.boot, 1L, lavmodel@def.function)
if (length(def.idx) == 1L) {
tmp.boot.def <- as.matrix(tmp.boot.def)
} else {
tmp.boot.def <- t(tmp.boot.def)
}
return.value <- cov(tmp.boot.def)
} else {
# tmp.vcov
tmp.vcov <- lav_object_inspect_vcov(object,
standardized = FALSE,
type = type, free.only = TRUE,
add.labels = FALSE,
add.class = FALSE)
# regular delta method
tmp.jac <- try(lav_func_jacobian_complex(func = lavmodel@def.function,
x = x), silent = TRUE)
if (inherits(tmp.jac, "try-error")) { # eg. pnorm()
tmp.jac <- lav_func_jacobian_simple(func = lavmodel@def.function,
x = x)
}
if (joint) {
tmp.jac2 <- rbind(diag(nrow = ncol(tmp.jac)), tmp.jac)
return.value <- tmp.jac2 %*% tmp.vcov %*% t(tmp.jac2)
} else {
return.value <- tmp.jac %*% tmp.vcov %*% t(tmp.jac)
}
}
}
# labels
if (add.labels) {
if (joint) {
lhs.names <- lavpartable$lhs[joint.idx]
} else {
lhs.names <- lavpartable$lhs[def.idx]
}
colnames(return.value) <- rownames(return.value) <- lhs.names
}
# class
if (add.class) {
class(return.value) <- c("lavaan.matrix.symmetric", "matrix")
}
return.value
}
lav_object_inspect_UGamma <- function(object, # nolint
add.labels = FALSE, add.class = FALSE) {
out <- lav_test_satorra_bentler(lavobject = object,
method = "original",
return.ugamma = TRUE)
return.value <- out$UGamma
# labels
# if(add.labels) {
# colnames(return.value) <- rownames(return.value) <-
# }
# class
if (add.class) {
class(return.value) <- c("lavaan.matrix.symmetric", "matrix")
}
return.value
}
lav_object_inspect_UfromUGamma <- function(object, # nolint
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
out <- lav_test_satorra_bentler(lavobject = object,
method = "original",
return.u = TRUE)
return.value <- out$UfromUGamma
# labels
# if(add.labels) {
# colnames(return.value) <- rownames(return.value) <-
# }
# class
if (add.class) {
class(return.value) <- c("lavaan.matrix.symmetric", "matrix")
}
return.value
}
# Delta (jacobian: d samplestats / d free_parameters)
lav_object_inspect_delta <- function(object,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
lavmodel <- object@Model
lavdata <- object@Data
lavpartable <- object@ParTable
lavpta <- object@pta
return.value <- lav_object_inspect_delta_internal(lavmodel = lavmodel,
lavdata = lavdata, lavpartable = lavpartable,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
return.value
}
lav_object_inspect_delta_rownames <- function( # nolint
object,
lavmodel = NULL,
lavpartable = NULL,
drop.list.single.group = FALSE) {
if (!is.null(object)) {
lavmodel <- object@Model
lavpartable <- object@ParTable
lavpta <- object@pta
lavdata <- object@Data
} else {
lavdata <- NULL
lavpta <- lav_partable_attributes(lavpartable)
}
categorical <- lavmodel@categorical
correlation <- FALSE
if (.hasSlot(lavmodel, "correlation")) {
correlation <- lavmodel@correlation
}
conditional.x <- lavmodel@conditional.x
group.w.free <- lavmodel@group.w.free
nvar <- lavmodel@nvar
num.idx <- lavmodel@num.idx
th.idx <- lavmodel@th.idx
nblocks <- lavmodel@nblocks
# store names per block, rbind later
tmp.names <- vector("list", length = nblocks)
# output is per group
return.value <- vector("list", lavmodel@ngroups)
for (g in 1:nblocks) {
if (conditional.x) {
ov.names <- lavpta$vnames$ov.nox[[g]]
} else {
ov.names <- lavpta$vnames$ov[[g]]
}
ov.names.x <- lavpta$vnames$ov.x[[g]]
nvar <- length(ov.names)
names.cov <- names.cor <- names.var <- character(0L)
names.mu <- names.pi <- names.th <- character(0L)
names.gw <- character(0L)
# Sigma
# - if continuous: vech(Sigma)
# - if categorical: first numeric variances, then
# tmp <- apply(expand.grid(ov.names, ov.names), 1L,
# paste, collapse = "~~")
# if(categorical) {
# names.cor <- tmp[lav_matrix_vech_idx(nvar, diagonal = FALSE)]
# names.var <- tmp[lav_matrix_diag_idx(nvar)[num.idx[[g]]]]
# } else {
# names.cov <- tmp[lav_matrix_vech_idx(nvar, diagonal = TRUE)]
# }
# NOTE: in 0.6-1, we use the same order, but 'label' in row-wise
# format (eg x1 ~~ x2 instead of x2 ~~ x1)
tmp <- matrix(apply(expand.grid(ov.names, ov.names), 1L,
paste, collapse = "~~"), nrow = nvar)
if (categorical) {
names.cor <- lav_matrix_vechru(tmp, diagonal = FALSE)
names.var <- diag(tmp)[num.idx[[g]]]
} else if (correlation) {
names.cor <- lav_matrix_vechru(tmp, diagonal = FALSE)
} else {
names.cov <- lav_matrix_vechru(tmp, diagonal = TRUE)
}
# Mu
if (!categorical && lavmodel@meanstructure) {
names.mu <- paste(ov.names, "~1", sep = "")
}
# Pi
if (conditional.x && lavmodel@nexo[g] > 0L) {
names.pi <- apply(expand.grid(ov.names, ov.names.x), 1L,
paste, collapse = "~")
}
# th
if (categorical) {
names.th <- lavpta$vnames$th[[g]]
# interweave numeric intercepts, if any
if (length(num.idx[[g]]) > 0L) {
tmp <- character(length(th.idx[[g]]))
tmp[th.idx[[g]] > 0] <- names.th
tmp[th.idx[[g]] == 0] <- paste(ov.names[num.idx[[g]]],
"~1", sep = "")
names.th <- tmp
}
}
# gw
if (group.w.free) {
names.gw <- "w"
}
tmp.names[[g]] <- c(names.gw,
names.th, names.mu,
names.pi,
names.cov, names.var, names.cor)
} # blocks
# multilevel?
if (.hasSlot(lavmodel, "multilevel") && lavmodel@multilevel) {
for (g in 1:lavmodel@ngroups) {
return.value[[g]] <- c(tmp.names[[(g - 1) * 2 + 1]],
tmp.names[[(g - 1) * 2 + 2]])
}
} else {
return.value <- tmp.names
}
# drop list?
if (lavmodel@ngroups == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (!is.null(lavdata)) {
if (length(lavdata@group.label) > 0L) {
names(return.value) <- unlist(lavdata@group.label)
}
}
return.value
}
lav_object_inspect_delta_internal <- function( # nolint
lavmodel = NULL, lavdata = NULL,
lavpartable = NULL,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
return.value <- computeDelta(lavmodel)
if (add.labels) {
tmp.pnames <- lav_partable_labels(lavpartable, type = "free")
tmp.rownames <- lav_object_inspect_delta_rownames(object = NULL,
lavmodel = lavmodel, lavpartable = lavpartable,
drop.list.single.group = FALSE)
}
for (g in seq_len(lavmodel@ngroups)) {
# add labels
if (add.labels) {
colnames(return.value[[g]]) <- tmp.pnames
rownames(return.value[[g]]) <- tmp.rownames[[g]]
}
# add class
if (add.class) {
class(return.value[[g]]) <- c("lavaan.matrix", "matrix")
}
} # ngroups
# drop list?
if (lavmodel@ngroups == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(lavdata@group.label) > 0L) {
names(return.value) <- unlist(lavdata@group.label)
}
}
return.value
}
lav_object_inspect_zero_cell_tables <- # nolint
function(object, add.labels = FALSE, add.class = FALSE,
drop.list.single.group = FALSE) {
# categorical?
if (!object@Model@categorical) {
lav_msg_warn(gettext("no categorical variables in fitted model"))
return(invisible(list()))
}
lavdata <- object@Data
# create 2-way tables
tmp.table <- lavTables(object, dimension = 2L, output = "data.frame",
statistic = NULL)
# select tables with empty cells
empty.id <- tmp.table$id[which(tmp.table$obs.freq == 0)]
if (length(empty.id) == 0L) {
# only when lavInspect() is used, give message
if (add.class) {
cat("(There are no tables with empty cells for this fitted model)\n")
}
return(invisible(list()))
} else {
return.value <- lav_tables_cells_format(
tmp.table[tmp.table$id %in% empty.id, ],
lavdata = lavdata,
drop.list.single.group = drop.list.single.group)
}
return.value
}
lav_object_inspect_coef <- function(object, type = "free",
add.labels = FALSE, add.class = FALSE) {
if (type == "user" || type == "all") {
type <- "user"
idx <- seq_along(object@ParTable$lhs)
} else if (type == "free") {
#idx <- which(object@ParTable$free > 0L & !duplicated(object@ParTable$free))
idx <- which(object@ParTable$free > 0L)
} else {
lav_msg_stop(gettextf(
"%1$s argument must be either %2$s or %3$s",
"type", "free", "user"))
}
tmp.est <- lav_object_inspect_est(object)
cof <- tmp.est[idx]
# labels?
if (add.labels) {
names(cof) <- lav_partable_labels(object@ParTable, type = type)
}
# class
if (add.class) {
class(cof) <- c("lavaan.vector", "numeric")
}
cof
}
lav_object_inspect_npar <- function(object, type = "free") {
if (type == "free") {
npar <- sum(object@ParTable$free > 0L &
!duplicated(object@ParTable$free))
} else {
npar <- length(object@ParTable$lhs)
}
npar
}
lav_object_inspect_icc <- function(object, add.labels = FALSE,
add.class = FALSE,
drop.list.single.group = FALSE) {
lavdata <- object@Data
n.g <- lavdata@ngroups
return.value <- vector("list", n.g)
# clustered data?
if (length(lavdata@cluster) == 0L) {
lav_msg_stop(gettext(
"intraclass correlation only available for clustered data"))
} else if (lavdata@nlevels == 1L) {
lav_msg_stop(gettext(
"intraclass correlation only available if the model syntax
contains levels"))
}
if (length(object@h1) == 0L) {
lav_msg_stop(gettext("h1 slot is not available; refit with h1 = TRUE"))
}
# implied statistics
implied <- object@h1$implied
for (g in 1:n.g) {
sigma.w <- implied$cov[[(g - 1) * lavdata@nlevels + 1]]
sigma.b <- implied$cov[[(g - 1) * lavdata@nlevels + 2]]
w.diag <- diag(sigma.w)
b.diag <- diag(sigma.b)
return.value[[g]] <- numeric(length(w.diag))
ov.names.l <- lavdata@ov.names.l[[g]]
w.idx <- which(ov.names.l[[1]] %in% ov.names.l[[2]])
w.names <- ov.names.l[[1]][w.idx]
b.idx <- match(w.names, ov.names.l[[2]])
return.value[[g]][w.idx] <- b.diag[b.idx] / (w.diag[w.idx] + b.diag[b.idx])
# label
if (add.labels) {
names(return.value[[g]]) <- ov.names.l[[1]]
}
# class
if (add.class) {
class(return.value[[g]]) <- c("lavaan.vector", "numeric")
}
} # g
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
lav_object_inspect_ranef <- function(object, add.labels = FALSE,
add.class = FALSE,
drop.list.single.group = FALSE) {
lavdata <- object@Data
lavsamplestats <- object@SampleStats
n.g <- lavdata@ngroups
return.value <- vector("list", n.g)
# multilevel?
if (lavdata@nlevels == 1L) {
lav_msg_stop(gettext(
"random effects only available for clustered data (in the long format)"))
}
# implied statistics
lavimplied <- object@implied
for (g in 1:n.g) {
tmp.lp <- lavdata@Lp[[g]]
tmp.ylp <- lavsamplestats@YLp[[g]]
# implied for this group
group.idx <- (g - 1) * lavdata@nlevels + seq_len(lavdata@nlevels)
implied.group <- lapply(lavimplied, function(x) x[group.idx])
# random effects (=random intercepts or cluster means)
out <- lav_mvnorm_cluster_implied22l(Lp = tmp.lp, implied = implied.group)
mb.j <- lav_mvnorm_cluster_em_estep_ranef(YLp = tmp.ylp, Lp = tmp.lp,
sigma.w = out$sigma.w, sigma.b = out$sigma.b,
sigma.zz = out$sigma.zz, sigma.yz = out$sigma.yz,
mu.z = out$mu.z, mu.w = out$mu.w, mu.b = out$mu.b,
se = FALSE)
return.value[[g]] <- mb.j
ov.names.l <- lavdata@ov.names.l[[g]]
# label
if (add.labels) {
colnames(return.value[[g]]) <- ov.names.l[[1]]
}
# class
if (add.class) {
class(return.value[[g]]) <- c("lavaan.matrix", "matrix")
}
} # g
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
# casewise loglikelihood contributions
lav_object_inspect_loglik_casewise <- function(object, log. = TRUE, # nolint
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
lavdata <- object@Data
lavsamplestats <- object@SampleStats
lavimplied <- object@implied
lavoptions <- object@Options
n.g <- lavdata@ngroups
return.value <- vector("list", n.g)
# multilevel?
if (lavdata@nlevels > 1L) {
lav_msg_stop(gettext(
"casewise (log)likeloods contributions not yet available for clustered data"))
}
# estimator ML?
if (object@Options$estimator != "ML") {
lav_msg_stop(gettextf(
"casewise (log)likeloods contributions only available for estimator = %s",
dQuote("ML")))
}
for (g in 1:n.g) {
if (lavsamplestats@missing.flag) {
return.value[[g]] <-
lav_mvnorm_missing_llik_casewise(Y = lavdata@X[[g]],
wt = lavdata@weights[[g]],
Mu = lavimplied$mean[[g]],
Sigma = lavimplied$cov[[g]],
x.idx = lavsamplestats@x.idx[[g]])
} else { # single-level, complete data
if (lavoptions$conditional.x) {
if (!is.null(lavdata@weights[[g]])) {
lav_msg_stop(gettext("no support (yet) if weights are used."))
}
return.value[[g]] <- lav_mvreg_loglik_data(
Y = lavdata@X[[g]],
eXo = lavdata@eXo[[g]],
res.int = lavimplied$res.int[[g]],
res.slopes = lavimplied$res.slopes[[g]],
res.cov = lavimplied$res.cov[[g]],
casewise = TRUE)
} else {
if (object@Model@meanstructure) {
tmp.mean <- lavimplied$mean[[g]]
} else {
tmp.mean <- lavsamplestats@mean[[g]]
}
return.value[[g]] <-
lav_mvnorm_loglik_data(Y = lavdata@X[[g]],
wt = lavdata@weights[[g]],
Mu = tmp.mean,
Sigma = lavimplied$cov[[g]],
x.idx = lavsamplestats@x.idx[[g]],
casewise = TRUE)
}
} # single-level, complete data
# log. = FALSE?
if (!log.) {
return.value[[g]] <- exp(return.value[[g]])
}
# label
# if(add.labels) {
# }
# class
if (add.class) {
class(return.value[[g]]) <- c("lavaan.vector", "numeric")
}
} # g
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
# Mahalanobis distances for factor scores or casewise residuals
# type = "lv" -> factor scores
# type = "resid" -> casewise residuals
#
# we always use Bartlett factor scores (see Yuan & Hayashi 2010)
# (this has no impact on the m-distances for the factor scores,
# and only a very slight impact on the m-distances for the casewise
# residuals; but asymptotically, only when we use Bartlett factor
# scores are the 'true scores' (=LAMBDA %*% FS) orthogonal to the
# casewise residuals)
lav_object_inspect_mdist2 <- function(object, type = "resid", squared = TRUE,
add.labels = FALSE, add.class = FALSE,
drop.list.single.group = FALSE) {
lavdata <- object@Data
n.g <- lavdata@ngroups
# lavPredict()
out <- lavPredict(object, type = type, method = "ML", # = Bartlett
label = FALSE, fsm = TRUE, mdist = TRUE,
se = "none", acov = "none")
return.value <- attr(out, "mdist")
for (g in seq_len(n.g)) {
# squared?
if (!squared) {
return.value[[g]] <- sqrt(return.value[[g]])
}
# labels?
# if(add.labels) {
# }
# class
if (add.class) {
class(return.value[[g]]) <- c("lavaan.vector", "numeric")
}
} # g
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
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Defines functions lav_object_inspect_mdist2 lav_object_inspect_loglik_casewise lav_object_inspect_ranef lav_object_inspect_icc lav_object_inspect_npar lav_object_inspect_coef lav_object_inspect_delta_internal lav_object_inspect_delta_rownames lav_object_inspect_delta lav_object_inspect_UfromUGamma lav_object_inspect_UGamma lav_object_inspect_vcov_def lav_object_inspect_vcov lav_object_inspect_firstorder lav_object_inspect_h1_information lav_object_inspect_information lav_object_inspect_hessian lav_object_inspect_gradient lav_object_inspect_sampstat_gamma lav_object_inspect_wls_v lav_object_inspect_wls_obs lav_object_inspect_wls_est lav_object_inspect_empty_idx lav_object_inspect_missing_patterns lav_object_inspect_missing_coverage lav_object_inspect_theta lav_object_inspect_fs_determinacy lav_object_inspect_vy lav_object_inspect_th_idx lav_object_inspect_th lav_object_inspect_mean_ov lav_object_inspect_cov_ov lav_object_inspect_cov_all lav_object_inspect_mean_lv lav_object_inspect_cov_lv lav_object_inspect_residuals lav_object_inspect_implied lav_object_inspect_rsquare lav_object_inspect_cluster_info lav_object_inspect_case_idx lav_object_inspect_data lav_object_inspect_sampstat lav_object_inspect_modelmatrices lav_object_inspect_boot lav_object_inspect_start lav_object_inspect_std_se lav_object_inspect_se lav_object_inspect_est lavInspect.lavaan lavTech.lavaan inspect.lavaan
# inspect a fitted lavaan object
# backward compatibility -- wrapper around lavInspect
inspect.lavaan <- function(object, what = "free", ...) {
lavInspect.lavaan(object = object,
what = what,
add.labels = TRUE,
add.class = TRUE,
drop.list.single.group = TRUE)
}
# the `tech' version: no labels, full matrices, ... for further processing
lavTech.lavaan <- function(object,
what = "free",
add.labels = FALSE,
add.class = FALSE,
list.by.group = FALSE,
drop.list.single.group = FALSE) {
lavInspect.lavaan(object, what = what,
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
}
# the `user' version: with defaults for display only
lavInspect.lavaan <- function(object, # nolint
what = "free",
add.labels = TRUE,
add.class = TRUE,
list.by.group = TRUE,
drop.list.single.group = TRUE) {
# object must inherit from class lavaan
stopifnot(inherits(object, "lavaan"))
# store partable with pta in object to use cache in called functions
object@ParTable <- lav_partable_set_cache(object@ParTable, object@pta)
# old (<0.6) object?
if (!.hasSlot(object@Data, "block.label")) {
object@Data@block.label <- object@Data@group.label
}
# only a single argument
if (length(what) > 1) {
lav_msg_stop(gettextf("argument %s cannot have more than one element",
"what"))
}
# be case insensitive
what <- tolower(what)
#### model matrices, with different contents ####
if (what == "free") {
lav_object_inspect_modelmatrices(object, what = "free",
type = "free", add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "impute" ||
what == "imputed") { # just to ease the transition for semTools!
object@imputed
} else if (what == "partable" || what == "user") {
lav_object_inspect_modelmatrices(object, what = "free",
type = "partable", add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "se" ||
what == "std.err" ||
what == "standard.errors") {
lav_object_inspect_modelmatrices(object, what = "se",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "se.std" ||
what == "std.se") {
lav_object_inspect_modelmatrices(object, what = "std.se",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "start" || what == "starting.values") {
lav_object_inspect_modelmatrices(object, what = "start",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "est" || what == "estimates" ||
what == "x") {
lav_object_inspect_modelmatrices(object, what = "est",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "est.unrotated") {
lav_object_inspect_modelmatrices(object, what = "est.unrotated",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "dx.free") {
lav_object_inspect_modelmatrices(object, what = "dx.free",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "dx.all") {
lav_object_inspect_modelmatrices(object, what = "dx.all",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "std" || what == "std.all" ||
what == "est.std" || what == "std.est" ||
what == "standardized") {
lav_object_inspect_modelmatrices(object, what = "std.all",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "std.lv") {
lav_object_inspect_modelmatrices(object, what = "std.lv",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
} else if (what == "std.nox") {
lav_object_inspect_modelmatrices(object, what = "std.nox",
add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
#### parameter table ####
} else if (what == "list") {
parTable(object)
#### bootstrap coef ####
} else if (what %in% c("boot", "bootstrap", "boot.coef", "coef.boot")) {
lav_object_inspect_boot(object, add.labels = add.labels,
add.class = add.class)
#### fit indices ####
} else if (what == "fit" ||
what == "fitmeasures" ||
what == "fit.measures" ||
what == "fit.indices") {
fitMeasures(object)
#### baseline model ####
} else if (what == "baseline.partable") {
out <- as.data.frame(object@baseline$partable, stringsAsFactors = FALSE)
if (add.class) {
class(out) <- c("lavaan.data.frame", "data.frame")
}
return(out)
} else if (what == "baseline.test") {
object@baseline$test
#### modification indices ####
} else if (what == "mi" ||
what == "modindices" ||
what == "modification.indices") {
modificationIndices(object)
#### sample statistics #####
} else if (what == "obs" ||
what == "observed" ||
what == "sampstat" ||
what == "sampstats" ||
what == "samplestats" ||
what == "samp" ||
what == "sample" ||
what == "samplestatistics") {
# new in 0.6-3: always use h1 = TRUE!!!
lav_object_inspect_sampstat(object, h1 = TRUE, std = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "obs.std" ||
what == "observed.std" ||
what == "sampstat.std" ||
what == "sampstats.std" ||
what == "samplestats.std" ||
what == "samp.std" ||
what == "sample.std" ||
what == "samplestatistics.std") {
lav_object_inspect_sampstat(object, h1 = TRUE, std = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "h1" || what == "missing.h1" || what == "sampstat.h1") {
lav_object_inspect_sampstat(object, h1 = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
#### wls.est - wls.obs - wls.v ####
} else if (what == "wls.est") {
lav_object_inspect_wls_est(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "wls.obs") {
lav_object_inspect_wls_obs(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "wls.v") {
lav_object_inspect_wls_v(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
#### data + missingness ####
} else if (what == "data") {
lav_object_inspect_data(object, add.labels = add.labels,
drop.list.single.group = drop.list.single.group)
} else if (what == "case.idx") {
lav_object_inspect_case_idx(object,
drop.list.single.group = drop.list.single.group)
} else if (what == "ngroups") {
object@Data@ngroups
} else if (what == "group") {
object@Data@group
} else if (what == "cluster") {
object@Data@cluster
} else if (what == "nlevels") {
object@Data@nlevels
} else if (what == "nclusters") {
lav_object_inspect_cluster_info(object, level = 2L,
what = "nclusters",
drop.list.single.group = drop.list.single.group)
} else if (what == "ncluster.size") {
lav_object_inspect_cluster_info(object, level = 2L,
what = "ncluster.size",
drop.list.single.group = drop.list.single.group)
} else if (what == "cluster.size") {
lav_object_inspect_cluster_info(object, level = 2L,
what = "cluster.size",
drop.list.single.group = drop.list.single.group)
} else if (what == "cluster.id") {
lav_object_inspect_cluster_info(object, level = 2L,
what = "cluster.id",
drop.list.single.group = drop.list.single.group)
} else if (what == "cluster.idx") {
lav_object_inspect_cluster_info(object, level = 2L,
what = "cluster.idx",
drop.list.single.group = drop.list.single.group)
} else if (what == "cluster.label") {
lav_object_inspect_cluster_info(object, level = 2L,
what = "cluster.label",
drop.list.single.group = drop.list.single.group)
} else if (what == "cluster.sizes") {
lav_object_inspect_cluster_info(object, level = 2L,
what = "cluster.sizes",
drop.list.single.group = drop.list.single.group)
} else if (what == "average.cluster.size") {
lav_object_inspect_cluster_info(object, level = 2L,
what = "average.cluster.size",
drop.list.single.group = drop.list.single.group)
} else if (what == "ordered") {
object@Data@ordered
} else if (what == "group.label") {
object@Data@group.label
} else if (what == "level.label") {
object@Data@level.label
} else if (what == "nobs") {
unlist(object@Data@nobs)
} else if (what == "norig") {
unlist(object@Data@norig)
} else if (what == "ntotal") {
sum(unlist(object@Data@nobs))
} else if (what == "coverage") {
lav_object_inspect_missing_coverage(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what %in% c("patterns", "pattern")) {
lav_object_inspect_missing_patterns(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "empty.idx") {
lav_object_inspect_empty_idx(object,
drop.list.single.group = drop.list.single.group)
#### rsquare ####
} else if (what == "rsquare" || what == "r-square" || what == "r2") {
lav_object_inspect_rsquare(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
#### model-implied sample statistics ####
} else if (what == "implied" || what == "fitted" ||
what == "expected" || what == "exp") {
lav_object_inspect_implied(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "resid" || what == "res" || what == "residual" ||
what == "residuals") {
lav_object_inspect_residuals(object, h1 = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "cov.lv" || what == "veta") {
lav_object_inspect_cov_lv(object,
correlation.metric = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "cor.lv") {
lav_object_inspect_cov_lv(object,
correlation.metric = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "mean.lv" || what == "eeta") {
lav_object_inspect_mean_lv(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "cov.all") {
lav_object_inspect_cov_all(object,
correlation.metric = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "cor.all") {
lav_object_inspect_cov_all(object,
correlation.metric = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "cov.ov" || what == "sigma" || what == "sigma.hat") {
lav_object_inspect_cov_ov(object,
correlation.metric = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "cor.ov") {
lav_object_inspect_cov_ov(object,
correlation.metric = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "mean.ov" || what == "mu" || what == "mu.hat") {
lav_object_inspect_mean_ov(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "th" || what == "thresholds") {
lav_object_inspect_th(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "th.idx") {
lav_object_inspect_th_idx(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "vy") {
lav_object_inspect_vy(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what %in% c("fs.reliability", "fs.rel", "fs.reliabilities")) {
lav_object_inspect_fs_determinacy(object, squared = TRUE,
fs.method = "regression",
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what %in% c("fs.determinacy", "fs.det", "fs.determin",
"fs.determinacies")) {
lav_object_inspect_fs_determinacy(object, squared = FALSE,
fs.method = "regression",
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what %in% c("fs.reliability.bartlett",
"fs.reliability.Bartlett",
"fs.rel.bartlett", "fs.rel.Bartlett",
"fs.reliabilities.bartlett",
"fs.reliabilities.Bartlett")) {
lav_object_inspect_fs_determinacy(object, squared = TRUE,
fs.method = "Bartlett",
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what %in% c("fs.determinacy.bartlett",
"fs.determinacy.Bartlett",
"fs.det.bartlett", "fs.det.Bartlett",
"fs.determin.bartlett", "fs.determin.Bartlett",
"fs.determinacies.bartlett",
"fs.determinacies.Bartlett")) {
lav_object_inspect_fs_determinacy(object, squared = FALSE,
fs.method = "Bartlett",
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
#### specific model matrices? ####
} else if (what == "theta" || what == "theta.cov") {
lav_object_inspect_theta(object, correlation.metric = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "theta.cor") {
lav_object_inspect_theta(object, correlation.metric = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
#### (squared) Mahalanobis distances ####
} else if (what == "mdist2.fs") {
lav_object_inspect_mdist2(object, type = "lv", squared = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "mdist2.resid") {
lav_object_inspect_mdist2(object, type = "resid", squared = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "mdist.fs") {
lav_object_inspect_mdist2(object, type = "lv", squared = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "mdist.resid") {
lav_object_inspect_mdist2(object, type = "resid", squared = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
#### convergence, meanstructure, categorical ####
} else if (what == "converged") {
object@optim$converged
} else if (what == "iterations" ||
what == "iter" ||
what == "niter") {
object@optim$iterations
} else if (what == "meanstructure") {
object@Model@meanstructure
} else if (what == "categorical") {
object@Model@categorical
} else if (what == "fixed.x") {
object@Model@fixed.x
} else if (what == "parameterization") {
object@Model@parameterization
} else if (what == "npar") {
lav_object_inspect_npar(object, type = "free")
} else if (what == "coef") {
# this breaks simsem and semTools -- 0.6-1
# lav_object_inspect_coef(object, type = "free",
# add.labels = add.labels, add.class = add.class)
lav_object_inspect_modelmatrices(object, what = "est",
type = "free", add.labels = add.labels, add.class = add.class,
list.by.group = list.by.group,
drop.list.single.group = drop.list.single.group)
#### NACOV samplestats ####
} else if (what == "gamma") {
lav_object_inspect_sampstat_gamma(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
#### gradient, Hessian, information, first.order, vcov ####
} else if (what == "gradient") {
lav_object_inspect_gradient(object,
add.labels = add.labels, add.class = add.class, logl = FALSE)
} else if (what == "gradient.logl") {
lav_object_inspect_gradient(object,
add.labels = add.labels, add.class = add.class, logl = TRUE)
} else if (what == "optim.gradient") {
lav_object_inspect_gradient(object,
add.labels = add.labels, add.class = add.class, optim = TRUE)
} else if (what == "hessian") {
lav_object_inspect_hessian(object,
add.labels = add.labels, add.class = add.class)
} else if (what == "information") {
lav_object_inspect_information(object, information = "default",
augmented = FALSE, inverted = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "information.expected") {
lav_object_inspect_information(object, information = "expected",
augmented = FALSE, inverted = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "information.observed") {
lav_object_inspect_information(object, information = "observed",
augmented = FALSE, inverted = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "information.first.order" ||
what == "information.firstorder" ||
what == "first.order") {
lav_object_inspect_information(object, information = "first.order",
augmented = FALSE, inverted = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "augmented.information") {
lav_object_inspect_information(object, information = "default",
augmented = TRUE, inverted = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "augmented.information.expected") {
lav_object_inspect_information(object, information = "expected",
augmented = TRUE, inverted = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "augmented.information.observed") {
lav_object_inspect_information(object, information = "observed",
augmented = TRUE, inverted = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "augmented.information.first.order" ||
what == "augmented.first.order") {
lav_object_inspect_information(object, information = "first.order",
augmented = TRUE, inverted = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "inverted.information") {
lav_object_inspect_information(object, information = "default",
augmented = TRUE, inverted = TRUE,
add.labels = add.labels, add.class = add.class)
} else if (what == "inverted.information.expected") {
lav_object_inspect_information(object, information = "expected",
augmented = TRUE, inverted = TRUE,
add.labels = add.labels, add.class = add.class)
} else if (what == "inverted.information.observed") {
lav_object_inspect_information(object, information = "observed",
augmented = TRUE, inverted = TRUE,
add.labels = add.labels, add.class = add.class)
} else if (what == "inverted.information.first.order" ||
what == "inverted.first.order") {
lav_object_inspect_information(object, information = "first.order",
augmented = TRUE, inverted = TRUE,
add.labels = add.labels, add.class = add.class)
} else if (what == "h1.information") {
lav_object_inspect_h1_information(object, information = "default",
h1.information = "default", inverted = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "h1.information.expected") {
lav_object_inspect_h1_information(object, information = "expected",
h1.information = "default", inverted = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "h1.information.observed") {
lav_object_inspect_h1_information(object, information = "observed",
h1.information = "default", inverted = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "h1.information.first.order" ||
what == "h1.information.firstorder" ||
what == "h1.first.order") {
lav_object_inspect_h1_information(object,
information = "first.order", h1.information = "default",
inverted = FALSE, add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "vcov") {
lav_object_inspect_vcov(object,
standardized = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.std.all" || what == "vcov.standardized" ||
what == "vcov.std") {
lav_object_inspect_vcov(object,
standardized = TRUE, type = "std.all",
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.std.lv") {
lav_object_inspect_vcov(object,
standardized = TRUE, type = "std.lv",
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.std.nox") {
lav_object_inspect_vcov(object,
standardized = TRUE, type = "std.nox",
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.def") {
lav_object_inspect_vcov_def(object, joint = FALSE,
standardized = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.def.std.all" || what == "vcov.def.standardized" ||
what == "vcov.def.std") {
lav_object_inspect_vcov_def(object, joint = FALSE,
standardized = TRUE, type = "std.all",
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.def.std.lv") {
lav_object_inspect_vcov_def(object, joint = FALSE,
standardized = TRUE, type = "std.lv",
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.def.std.nox") {
lav_object_inspect_vcov_def(object, joint = FALSE,
standardized = TRUE, type = "std.nox",
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.def.joint") {
lav_object_inspect_vcov_def(object, joint = TRUE,
standardized = FALSE,
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.def.joint.std.all" ||
what == "vcov.def.joint.standardized" ||
what == "vcov.def.joint.std") {
lav_object_inspect_vcov_def(object, joint = TRUE,
standardized = TRUE, type = "std.all",
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.def.joint.std.lv") {
lav_object_inspect_vcov_def(object, joint = TRUE,
standardized = TRUE, type = "std.lv",
add.labels = add.labels, add.class = add.class)
} else if (what == "vcov.def.joint.std.nox") {
lav_object_inspect_vcov_def(object, joint = TRUE,
standardized = TRUE, type = "std.nox",
add.labels = add.labels, add.class = add.class)
} else if (what == "ugamma" || what == "ug" || what == "u.gamma") {
lav_object_inspect_UGamma(object,
add.labels = add.labels, add.class = add.class)
} else if (what == "ufromugamma" || what == "u") {
lav_object_inspect_UfromUGamma(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
### jacobians ####
} else if (what == "delta") {
lav_object_inspect_delta(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "delta.rownames") {
lav_object_inspect_delta_rownames(object,
drop.list.single.group = drop.list.single.group)
### casewise loglikehoods ###
} else if (what == "loglik.casewise") {
lav_object_inspect_loglik_casewise(object, log. = TRUE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "lik.casewise") {
lav_object_inspect_loglik_casewise(object, log. = FALSE,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
# multilevel #
} else if (what == "icc") {
lav_object_inspect_icc(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
} else if (what == "ranef") {
lav_object_inspect_ranef(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
# post-checking
} else if (what == "post.check" || what == "post") {
lav_object_post_check(object)
# options
} else if (what == "options" || what == "lavoptions") {
object@Options
# version
} else if (what == "version") {
object@version
# call
} else if (what == "call") {
as.list(object@call)
# timing
} else if (what == "timing") {
object@timing
# optim
} else if (what == "optim") {
object@optim
# test
} else if (what == "test") {
object@test
# zero cell tables
} else if (what == "zero.cell.tables") {
lav_object_inspect_zero_cell_tables(object,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
#### not found ####
} else {
lav_msg_stop(gettextf(
"%1$s argument unknown: %2$s",
"what", lav_msg_view(what)
))
}
}
# helper functions (mostly to deal with older 'object' that may have
# been saved somewhere)
lav_object_inspect_est <- function(object, unrotated = FALSE) {
if (inherits(object, "lavaan")) {
# from 0.5-19, they are in the partable
if (!is.null(object@ParTable$est)) {
if (unrotated) {
return.value <- object@ParTable$est.unrotated
} else {
return.value <- object@ParTable$est # if this changes, tag @TDJorgensen in commit message
}
} else if (.hasSlot(object, "Fit")) {
# in < 0.5-19, we should look in @Fit@est
return.value <- object@Fit@est
} else {
partable <- parTable(object)
return.value <- rep(as.numeric(NA), length(partable$lhs))
}
} else {
# try generic coef()
return.value <- coef(object, type = "user")
if (is.matrix(return.value)) {
# lavaanList?
return.value <- rowMeans(return.value)
}
}
return.value
}
lav_object_inspect_se <- function(object) {
# from 0.5-19, they are in the partable
if (!is.null(object@ParTable$se)) {
return.value <- object@ParTable$se
} else if (.hasSlot(object, "Fit")) {
# in < 0.5-19, we should look in @Fit@se
return.value <- object@Fit@se
} else {
partable <- parTable(object)
return.value <- rep(as.numeric(NA), length(partable$lhs))
}
return.value
}
lav_object_inspect_std_se <- function(object) {
if (!is.null(object@ParTable$se.std)) {
return.value <- object@ParTable$se.std
} else {
tmp.std <- standardizedSolution(object)
return.value <- tmp.std$se
}
return.value
}
lav_object_inspect_start <- function(object) {
# from 0.5-19, they are in the partable
if (!is.null(object@ParTable$start)) {
return.value <- object@ParTable$start
} else {
# in < 0.5-19, we should look in @Fit@start
return.value <- object@Fit@start
}
return.value
}
lav_object_inspect_boot <- function(object, add.labels = FALSE,
add.class = FALSE) {
if (object@Options$se != "bootstrap" &&
!any(c("bootstrap", "bollen.stine") %in% object@Options$test)) {
lav_msg_stop(gettext("bootstrap was not used."))
}
# from 0.5-19. they are in a separate slot
tmp <- try(slot(object, "boot"), silent = TRUE)
if (inherits(tmp, "try-error")) {
# older version of object?
est <- lav_object_inspect_est(object)
tmp.boot <- attr(est, "tmp.boot.COEF")
} else {
# 0.5-19 way
tmp.boot <- object@boot$coef
}
# add coef names
if (add.labels) {
colnames(tmp.boot) <- names(coef(object))
}
# add class
if (add.class) {
class(tmp.boot) <- c("lavaan.matrix", "matrix")
}
tmp.boot
}
lav_object_inspect_modelmatrices <- function(object, what = "free", # nolint
type = "free", add.labels = FALSE, add.class = FALSE,
list.by.group = FALSE,
drop.list.single.group = FALSE) {
glist <- object@Model@GLIST
current.verbose <- lav_verbose()
if (what == "dx.free") {
if (lav_verbose(FALSE)) on.exit(lav_verbose(current.verbose), TRUE)
tmp.dx <- lav_model_gradient(
lavmodel = object@Model,
GLIST = NULL,
lavsamplestats = object@SampleStats,
lavdata = object@Data,
lavcache = object@Cache,
type = "free",
group.weight = TRUE,
ceq.simple = TRUE,
Delta = NULL)
} else if (what == "dx.all") {
if (lav_verbose(FALSE)) on.exit(lav_verbose(current.verbose), TRUE)
glist <- lav_model_gradient(lavmodel = object@Model,
GLIST = NULL,
lavsamplestats = object@SampleStats,
lavdata = object@Data,
lavcache = object@Cache,
type = "allofthem",
group.weight = TRUE,
ceq.simple = FALSE,
Delta = NULL)
names(glist) <- names(object@Model@GLIST)
} else if (what == "std.all") {
tmp.std <- lav_standardize_all(object)
} else if (what == "std.lv") {
tmp.std <- lav_standardize_lv(object)
} else if (what == "std.nox") {
tmp.std <- lav_standardize_all_nox(object)
} else if (what == "se") {
tmp.se <- lav_object_inspect_se(object)
} else if (what == "std.se") {
tmp.se <- lav_object_inspect_std_se(object)
} else if (what == "start") {
tmp.start <- lav_object_inspect_start(object)
} else if (what == "est") {
tmp.est <- lav_object_inspect_est(object)
} else if (what == "est.unrotated") {
if (!is.null(object@Options$rotation) &&
object@Options$rotation == "none") {
tmp.est <- lav_object_inspect_est(object, unrotated = FALSE)
} else {
tmp.est <- lav_object_inspect_est(object, unrotated = TRUE)
}
}
for (mm in seq_along(glist)) {
if (add.labels) {
dimnames(glist[[mm]]) <- object@Model@dimNames[[mm]]
}
if (what == "free") {
# fill in free parameter counts
if (type == "free") {
m.el.idx <- object@Model@m.free.idx[[mm]]
x.el.idx <- object@Model@x.free.idx[[mm]]
# } else if(type == "unco") {
# m.el.idx <- object@Model@m.unco.idx[[mm]]
# x.el.idx <- object@Model@x.unco.idx[[mm]]
} else if (type == "partable") {
m.el.idx <- object@Model@m.user.idx[[mm]]
x.el.idx <- object@Model@x.user.idx[[mm]]
} else {
lav_msg_stop(gettextf(
"%1$s argument unknown: %2$s",
"type", lav_msg_view(type)
))
}
# erase everything
glist[[mm]][, ] <- 0.0
glist[[mm]][m.el.idx] <- x.el.idx
} else if (what == "se" || what == "std.se") {
# fill in standard errors
m.user.idx <- object@Model@m.user.idx[[mm]]
x.user.idx <- object@Model@x.user.idx[[mm]]
# erase everything
glist[[mm]][, ] <- 0.0
glist[[mm]][m.user.idx] <- tmp.se[x.user.idx]
} else if (what == "start") {
# fill in starting values
m.user.idx <- object@Model@m.user.idx[[mm]]
x.user.idx <- object@Model@x.user.idx[[mm]]
glist[[mm]][m.user.idx] <- tmp.start[x.user.idx]
} else if (what %in% c("est", "est.unrotated")) {
# fill in estimated parameter values
m.user.idx <- object@Model@m.user.idx[[mm]]
x.user.idx <- object@Model@x.user.idx[[mm]]
glist[[mm]][m.user.idx] <- tmp.est[x.user.idx]
} else if (what == "dx.free") {
# fill in derivatives free parameters
m.el.idx <- object@Model@m.free.idx[[mm]]
x.el.idx <- object@Model@x.free.idx[[mm]]
# erase everything
glist[[mm]][, ] <- 0.0
glist[[mm]][m.el.idx] <- tmp.dx[x.el.idx]
} else if (what %in% c("std.all", "std.lv", "std.nox")) {
m.user.idx <- object@Model@m.user.idx[[mm]]
x.user.idx <- object@Model@x.user.idx[[mm]]
glist[[mm]][m.user.idx] <- tmp.std[x.user.idx]
}
# class
if (add.class) {
if (object@Model@isSymmetric[mm]) {
class(glist[[mm]]) <- c("lavaan.matrix.symmetric", "matrix")
} else {
class(glist[[mm]]) <- c("lavaan.matrix", "matrix")
}
}
}
# try to reflect `equality constraints'
con.flag <- FALSE
if (what == "free" && object@Model@eq.constraints) {
# extract constraints from parameter table
partable <- parTable(object)
tmp.con <- partable[partable$op %in% c("==", "<", ">"),
c("lhs", "op", "rhs")]
rownames(tmp.con) <- NULL
# replace 'labels' by parameter numbers
tmp.id <- lav_partable_constraints_label_id(partable)
tmp.label <- names(tmp.id)
for (con in seq_len(nrow(tmp.con))) {
# lhs
lhs.labels <- all.vars(as.formula(paste("~", tmp.con[con, "lhs"])))
if (length(lhs.labels) > 0L) {
# par id
lhs.freeid <- tmp.id[match(lhs.labels, tmp.label)]
# substitute
tmp <- tmp.con[con, "lhs"]
for (pat in seq_along(lhs.labels)) {
tmp <- sub(lhs.labels[pat], lhs.freeid[pat], tmp)
}
tmp.con[con, "lhs"] <- tmp
}
# rhs
rhs.labels <- all.vars(as.formula(paste("~", tmp.con[con, "rhs"])))
if (length(rhs.labels) > 0L) {
# par id
rhs.freeid <- tmp.id[match(rhs.labels, tmp.label)]
# substitute
tmp <- tmp.con[con, "rhs"]
for (pat in seq_along(rhs.labels)) {
tmp <- sub(rhs.labels[pat], rhs.freeid[pat], tmp)
}
tmp.con[con, "rhs"] <- tmp
}
} # con
# add this info at the top
# glist <- c(constraints = list(tmp.con), glist)
# no, not a good idea, it does not work with list.by.group
# add it as a 'header' attribute?
attr(tmp.con, "header") <- "Note: model contains equality constraints:"
con.flag <- TRUE
}
# should we group them per block?
if (list.by.group) {
lavmodel <- object@Model
nmat <- lavmodel@nmat
return.value <- vector("list", length = lavmodel@nblocks)
for (b in seq_len(lavmodel@nblocks)) {
# which mm belong to this block?
mm.in.group <- 1:nmat[b] + cumsum(c(0, nmat))[b]
return.value[[b]] <- glist[mm.in.group]
}
if (lavmodel@nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (lavmodel@nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
} else {
return.value <- glist
}
# header
if (con.flag) {
attr(return.value, "header") <- tmp.con
}
# lavaan.list
if (add.class) {
class(return.value) <- c("lavaan.list", "list")
}
return.value
}
# - fixme, should we export this function?
# - since 0.5-21, conditional.x = TRUE returns residual sample statistics
# for ML, we have both joint and residual cov/var/...; but for
# categorical = TRUE, we only have residual cov/var...; so, we
# only return residual in both cases, whenever residual
# - since 0.6-3, we always extract the values from the @h1 slot (if present)
# if meanstructure = FALSE, do NOT include $mean elements any longer
lav_object_inspect_sampstat <- function(object, h1 = TRUE, # nolint
std = FALSE,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# old (<0.6) object?
if (!.hasSlot(object@Data, "block.label")) {
object@Data@block.label <- object@Data@group.label
}
nblocks <- object@Model@nblocks
ov.names <- object@pta$vnames$ov
ov.names.res <- object@pta$vnames$ov.nox
ov.names.x <- object@pta$vnames$ov.x
# slots
lavsamplestats <- object@SampleStats
lavmodel <- object@Model
# if nlevels, override h1 to be TRUE, and set conditional.x = FALSE
if (object@Data@nlevels > 1L) {
h1 <- TRUE
conditional.x <- FALSE # for now (0.6-12)
} else {
conditional.x <- lavmodel@conditional.x
}
# check if we have a non-empty @h1 slot
if (!.hasSlot(object, "h1")) {
h1 <- FALSE
} else if (length(object@h1) == 0L) {
h1 <- FALSE
} else {
h1.implied <- object@h1$implied
}
# if h1 = FALSE and nlevels > 1L, nothing to show...
if (!h1 && object@Data@nlevels > 1L) {
lav_msg_stop(gettext(
"sample statistics not available; refit with option h1 = TRUE"))
}
return.value <- vector("list", length = nblocks)
for (b in seq_len(nblocks)) {
if (!conditional.x) {
# covariance matrix
if (h1) {
return.value[[b]]$cov <- h1.implied$cov[[b]]
} else {
return.value[[b]]$cov <- lavsamplestats@cov[[b]]
}
if (std) {
diag.orig <- diag(return.value[[b]]$cov)
return.value[[b]]$cov <- cov2cor(return.value[[b]]$cov)
}
if (add.labels && !is.null(return.value[[b]]$cov)) {
rownames(return.value[[b]]$cov) <- colnames(return.value[[b]]$cov) <-
ov.names[[b]]
}
if (add.class) {
class(return.value[[b]]$cov) <- c("lavaan.matrix.symmetric", "matrix")
}
# mean vector
if (lavmodel@meanstructure) {
if (h1) {
return.value[[b]]$mean <- as.numeric(h1.implied$mean[[b]])
} else {
return.value[[b]]$mean <- as.numeric(lavsamplestats@mean[[b]])
}
if (std) {
diag.orig[diag.orig < .Machine$double.eps] <- NA
return.value[[b]]$mean <- return.value[[b]]$mean / sqrt(diag.orig)
}
if (add.labels) {
names(return.value[[b]]$mean) <- ov.names[[b]]
}
if (add.class) {
class(return.value[[b]]$mean) <- c("lavaan.vector", "numeric")
}
}
# thresholds
if (lavmodel@categorical) {
if (h1) {
return.value[[b]]$th <- as.numeric(h1.implied$th[[b]])
} else {
return.value[[b]]$th <- as.numeric(lavsamplestats@th[[b]])
}
if (length(lavmodel@num.idx[[b]]) > 0L) {
num.idx <- which(lavmodel@th.idx[[b]] == 0)
return.value[[b]]$th <- return.value[[b]]$th[-num.idx]
}
# FIXME: what to do if std = TRUE (depends on delta/theta)
if (add.labels) {
names(return.value[[b]]$th) <- object@pta$vnames$th[[b]]
}
if (add.class) {
class(return.value[[b]]$th) <- c("lavaan.vector", "numeric")
}
}
# !conditional.x
} else {
# if conditional.x = TRUE
# residual covariance matrix
if (h1) {
return.value[[b]]$res.cov <- h1.implied$res.cov[[b]]
} else {
return.value[[b]]$res.cov <- lavsamplestats@res.cov[[b]]
}
if (std) {
diag.orig <- diag(return.value[[b]]$res.cov)
return.value[[b]]$res.cov <- cov2cor(return.value[[b]]$res.cov)
}
if (add.labels) {
rownames(return.value[[b]]$res.cov) <-
colnames(return.value[[b]]$res.cov) <-
ov.names.res[[b]]
}
if (add.class) {
class(return.value[[b]]$res.cov) <-
c("lavaan.matrix.symmetric", "matrix")
}
# intercepts
if (lavmodel@meanstructure) {
if (h1) {
return.value[[b]]$res.int <- as.numeric(h1.implied$res.int[[b]])
} else {
return.value[[b]]$res.int <- as.numeric(lavsamplestats@res.int[[b]])
}
if (std) {
diag.orig[diag.orig < .Machine$double.eps] <- NA
return.value[[b]]$res.int <- return.value[[b]]$res.int /
sqrt(diag.orig)
}
if (add.labels) {
names(return.value[[b]]$res.int) <- ov.names.res[[b]]
}
if (add.class) {
class(return.value[[b]]$res.int) <- c("lavaan.vector", "numeric")
}
}
# thresholds
if (lavmodel@categorical) {
if (h1) {
return.value[[b]]$res.th <- as.numeric(h1.implied$res.th[[b]])
} else {
return.value[[b]]$res.th <- as.numeric(lavsamplestats@res.th[[b]])
}
if (length(lavmodel@num.idx[[b]]) > 0L) {
num.idx <- which(lavmodel@th.idx[[b]] == 0)
return.value[[b]]$res.th <- return.value[[b]]$res.th[-num.idx]
}
# FIXME: if std: what to do?
if (add.labels) {
names(return.value[[b]]$res.th) <- object@pta$vnames$th[[b]]
}
if (add.class) {
class(return.value[[b]]$res.th) <- c("lavaan.vector", "numeric")
}
}
# slopes
if (lavmodel@nexo[b] > 0L) {
if (h1) {
return.value[[b]]$res.slopes <- h1.implied$res.slopes[[b]]
} else {
return.value[[b]]$res.slopes <- lavsamplestats@res.slopes[[b]]
}
# FIXME: if std: what to do? (here: b.z = b * s.x /s.y)
if (std) {
tmp.y <- matrix(sqrt(diag.orig),
nrow(return.value[[b]]$res.slopes),
ncol(return.value[[b]]$res.slopes))
tmp.x <- matrix(sqrt(diag(lavsamplestats@cov.x[[b]])),
nrow(return.value[[b]]$res.slopes),
ncol(return.value[[b]]$res.slopes), byrow = TRUE)
return.value[[b]]$res.slopes <- return.value[[b]]$res.slopes /
tmp.y * tmp.x
}
if (add.labels) {
rownames(return.value[[b]]$res.slopes) <- ov.names.res[[b]]
colnames(return.value[[b]]$res.slopes) <- ov.names.x[[b]]
}
if (add.class) {
class(return.value[[b]]$res.slopes) <- c("lavaan.matrix", "matrix")
}
}
# cov.x
if (lavmodel@nexo[b] > 0L) {
return.value[[b]]$cov.x <- lavsamplestats@cov.x[[b]]
if (std) {
diag.orig <- diag(return.value[[b]]$cov.x)
return.value[[b]]$cov.x <- cov2cor(return.value[[b]]$cov.x)
}
if (add.labels) {
rownames(return.value[[b]]$cov.x) <- ov.names.x[[b]]
colnames(return.value[[b]]$cov.x) <- ov.names.x[[b]]
}
if (add.class) {
class(return.value[[b]]$cov.x) <-
c("lavaan.matrix.symmetric", "matrix")
}
}
# mean.x
if (lavmodel@nexo[b] > 0L) {
return.value[[b]]$mean.x <- as.numeric(object@SampleStats@mean.x[[b]])
if (std) {
diag.orig[diag.orig < .Machine$double.eps] <- NA
return.value[[b]]$mean.x <- return.value[[b]]$mean.x / sqrt(diag.orig)
}
if (add.labels) {
names(return.value[[b]]$mean.x) <- ov.names.x[[b]]
}
if (add.class) {
class(return.value[[b]]$mean.x) <- c("lavaan.vector", "numeric")
}
}
} # conditional.x
# stochastic weights
if (lavmodel@group.w.free) {
# to be consistent with the 'implied' values,
# transform so group.w is the 'log(group.freq)'
return.value[[b]]$group.w <-
log(lavsamplestats@group.w[[b]] * lavsamplestats@ntotal)
if (add.labels) {
names(return.value[[b]]$group.w) <- "w"
}
if (add.class) {
class(return.value[[b]]$group.w) <- c("lavaan.vector", "numeric")
}
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_data <- function(object, add.labels = FALSE,
drop.list.single.group = FALSE) {
n.g <- object@Data@ngroups
if (object@Model@conditional.x) {
return.value <- vector("list", length = n.g)
for (g in 1:n.g) {
return.value[[g]] <- cbind(object@Data@X[[g]],
object@Data@eXo[[g]])
}
} else {
return.value <- object@Data@X
}
if (add.labels) {
for (g in 1:n.g) {
if (object@Model@conditional.x) {
colnames(return.value[[g]]) <- c(object@Data@ov.names[[g]],
object@Data@ov.names.x[[g]])
} else {
colnames(return.value[[g]]) <- object@Data@ov.names[[g]]
}
}
}
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
lav_object_inspect_case_idx <- function(object,
drop.list.single.group = FALSE) {
n.g <- object@Data@ngroups
return.value <- object@Data@case.idx
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
# lav_object_inspect_case_idx <- function(object, level = 1L,
# drop.list.single.group = FALSE) {
# #FIXME: if lavaan ever allows 3-level or cross-classifed models,
# # "level=" should be a character indicating the clustering variable
#
# n.g <- object@Data@ngroups
# nlevels <- object@Data@nlevels
# if (nlevels == 1L) level <- 1L
# # if what="cluster.idx" for single-level model
#
# if (level == 2L) {
# # level-2 (cluster) IDs
# return.value <- lapply(object@Data@Lp, function(gg)
# gg$cluster.id[[2]][ gg$cluster.idx[[2]] ])
# #FIXME: update if lavaan ever accepts 3-level or
# cross-classified models
#
# } else return.value <- object@Data@case.idx # level-1 (casewise) IDs
#
# if(n.g == 1L && drop.list.single.group) {
# return.value <- return.value[[1]]
# } else {
# if(length(object@Data@group.label) > 0L) {
# names(return.value) <- unlist(object@Data@group.label)
# }
# }
# return.value
# }
#
# cluster info
lav_object_inspect_cluster_info <- function( # nolint
object,
what = "cluster.size",
level = 2L,
drop.list.single.group = FALSE) {
n.g <- object@Data@ngroups
nlevels <- object@Data@nlevels
# just in case we have no clusters
if (nlevels == 1L) {
if (what %in% c("nclusters", "ncluster.size", "cluster.id")) {
return.value <- as.list(rep(1L, n.g))
} else if (what %in% c("cluster.size", "cluster.sizes")) {
return.value <- object@Data@nobs
} else if (what %in% c("cluster.idx", "cluster.label")) {
# everybody belongs to cluster 1
return.value <- lapply(seq_len(n.g),
function(gg) rep(1L, object@Data@nobs[[gg]]))
}
}
# if we do have clusters
if (nlevels > 1L) {
return.value <- vector("list", length = n.g)
for (g in seq_len(n.g)) {
tmp.lp <- object@Data@Lp[[g]]
if (what == "nclusters") {
return.value[[g]] <- tmp.lp$nclusters[[level]]
} else if (what == "ncluster.size") {
return.value[[g]] <- tmp.lp$ncluster.size[[level]]
} else if (what == "cluster.size") {
return.value[[g]] <- tmp.lp$cluster.size[[level]]
} else if (what == "cluster.id") {
return.value[[g]] <- tmp.lp$cluster.id[[level]]
} else if (what == "cluster.idx") {
return.value[[g]] <- tmp.lp$cluster.idx[[level]]
} else if (what == "cluster.label") {
return.value[[g]] <-
tmp.lp$cluster.id[[level]][tmp.lp$cluster.idx[[level]]]
} else if (what == "cluster.sizes") {
return.value[[g]] <- tmp.lp$cluster.sizes[[level]]
} else if (what == "average.cluster.size") {
nn.g <- object@Data@nobs[[g]]
cluster.size <- tmp.lp$cluster.size[[level]]
nclusters <- tmp.lp$nclusters[[level]]
return.value[[g]] <- (nn.g^2 - sum(cluster.size^2)) /
(nn.g * (nclusters - 1L))
}
} # g
} # nlevels > 1L
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
# count the number of clusters, or obtain tmp.n within each cluster
# lav_object_inspect_ncluster <- function(object, sizes = FALSE, #level = 2L,
# drop.list.single.group = FALSE) {
# n.g <- object@Data@ngroups
# nlevels <- object@Data@nlevels
#
# if (nlevels == 1L) {
# # single-level model, return sample size(s) or count 1 cluster per group
# return.value <- if (sizes) unlist(object@Data@nobs) else rep(1L, n.g)
#
# } else if (sizes) {
# # for each group, a vector of cluster sizes
# return.value <- lapply(object@Data@Lp, function(gg) gg$cluster.size[[2]])
# #FIXME: update if lavaan ever accepts 3-level or cross-classified models
#
# if (n.g == 1L && drop.list.single.group)
# return.value <- return.value[[1]]
#
# } else {
# # number of clusters in each group
# return.value <- sapply(object@Data@Lp, function(gg) gg$nclusters[[2]])
# #FIXME: update if lavaan ever accepts 3-level or cross-classified models
# }
#
# # assign group names, if applicable
# if (n.g > 1L) names(return.value) <- unlist(object@Data@group.label)
# return.value
# }
lav_object_inspect_rsquare <- function(object, est.std.all = NULL,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
nblocks <- object@Model@nblocks
return.value <- vector("list", length = nblocks)
if (is.null(est.std.all)) {
est.std.all <- lav_standardize_all(object)
}
partable <- object@ParTable
partable$rsquare <- 1.0 - est.std.all
# no values > 1.0
partable$rsquare[partable$rsquare > 1.0] <- as.numeric(NA)
for (b in seq_len(nblocks)) {
ind.names <- partable$rhs[which(partable$op == "=~" &
partable$block == b)]
eqs.y.names <- partable$lhs[which(partable$op == "~" &
partable$block == b)]
y.names <- unique(c(ind.names, eqs.y.names))
idx <- which(partable$op == "~~" & partable$lhs %in% y.names &
partable$rhs == partable$lhs & partable$block == b)
tmp <- partable$rsquare[idx]
if (add.labels && length(tmp) > 0L) {
names(tmp) <- partable$lhs[idx]
}
if (add.class) {
class(tmp) <- c("lavaan.vector", "numeric")
}
return.value[[b]] <- tmp
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
# model implied sample stats
lav_object_inspect_implied <- function(object, # nolint
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# old (<0.6) object?
if (!.hasSlot(object@Data, "block.label")) {
object@Data@block.label <- object@Data@group.label
}
nblocks <- object@Model@nblocks
ov.names <- object@pta$vnames$ov
ov.names.res <- object@pta$vnames$ov.nox
ov.names.x <- object@pta$vnames$ov.x
# slots
lavimplied <- object@implied
lavmodel <- object@Model
# if nlevels, always set conditional.x = FALSE
if (object@Data@nlevels > 1L) {
lavimplied <- lav_model_implied_cond2uncond(lavimplied)
conditional.x <- FALSE # for now (0.6-12)
} else {
conditional.x <- lavmodel@conditional.x
}
return.value <- vector("list", length = nblocks)
for (b in seq_len(nblocks)) {
if (!conditional.x) {
# covariance matrix
return.value[[b]]$cov <- lavimplied$cov[[b]]
if (add.labels && !is.null(return.value[[b]]$cov)) {
rownames(return.value[[b]]$cov) <- colnames(return.value[[b]]$cov) <-
ov.names[[b]]
}
if (add.class) {
class(return.value[[b]]$cov) <- c("lavaan.matrix.symmetric", "matrix")
}
# mean vector
if (lavmodel@meanstructure) {
return.value[[b]]$mean <- as.numeric(lavimplied$mean[[b]])
if (add.labels) {
names(return.value[[b]]$mean) <- ov.names[[b]]
}
if (add.class) {
class(return.value[[b]]$mean) <- c("lavaan.vector", "numeric")
}
}
# thresholds
if (lavmodel@categorical) {
return.value[[b]]$th <- as.numeric(lavimplied$th[[b]])
if (length(object@Model@num.idx[[b]]) > 0L) {
num.idx <- which(object@Model@th.idx[[b]] == 0)
return.value[[b]]$th <- return.value[[b]]$th[-num.idx]
}
if (add.labels) {
names(return.value[[b]]$th) <- object@pta$vnames$th[[b]]
}
if (add.class) {
class(return.value[[b]]$th) <- c("lavaan.vector", "numeric")
}
}
# !conditional.x
} else {
# if conditional.x = TRUE
# residual covariance matrix
return.value[[b]]$res.cov <- lavimplied$res.cov[[b]]
if (add.labels) {
rownames(return.value[[b]]$res.cov) <-
colnames(return.value[[b]]$res.cov) <-
ov.names.res[[b]]
}
if (add.class) {
class(return.value[[b]]$res.cov) <-
c("lavaan.matrix.symmetric", "matrix")
}
# intercepts
if (lavmodel@meanstructure) {
return.value[[b]]$res.int <- as.numeric(lavimplied$res.int[[b]])
if (add.labels) {
names(return.value[[b]]$res.int) <- ov.names.res[[b]]
}
if (add.class) {
class(return.value[[b]]$res.int) <- c("lavaan.vector", "numeric")
}
}
# thresholds
if (lavmodel@categorical) {
return.value[[b]]$res.th <- as.numeric(lavimplied$res.th[[b]])
if (length(object@Model@num.idx[[b]]) > 0L) {
num.idx <- which(object@Model@th.idx[[b]] == 0)
return.value[[b]]$res.th <- return.value[[b]]$res.th[-num.idx]
}
if (add.labels) {
names(return.value[[b]]$res.th) <- object@pta$vnames$th[[b]]
}
if (add.class) {
class(return.value[[b]]$res.th) <- c("lavaan.vector", "numeric")
}
}
# slopes
if (lavmodel@nexo[b] > 0L) {
return.value[[b]]$res.slopes <- lavimplied$res.slopes[[b]]
if (add.labels) {
rownames(return.value[[b]]$res.slopes) <- ov.names.res[[b]]
colnames(return.value[[b]]$res.slopes) <- ov.names.x[[b]]
}
if (add.class) {
class(return.value[[b]]$res.slopes) <- c("lavaan.matrix", "matrix")
}
}
# cov.x
if (lavmodel@nexo[b] > 0L) {
return.value[[b]]$cov.x <- lavimplied$cov.x[[b]]
if (add.labels) {
rownames(return.value[[b]]$cov.x) <- ov.names.x[[b]]
colnames(return.value[[b]]$cov.x) <- ov.names.x[[b]]
}
if (add.class) {
class(return.value[[b]]$cov.x) <-
c("lavaan.matrix.symmetric", "matrix")
}
}
# mean.x
if (lavmodel@nexo[b] > 0L) {
return.value[[b]]$mean.x <- as.numeric(lavimplied$mean.x[[b]])
if (add.labels) {
names(return.value[[b]]$mean.x) <- ov.names.x[[b]]
}
if (add.class) {
class(return.value[[b]]$mean.x) <- c("lavaan.vector", "numeric")
}
}
} # conditional.x
# stochastic weights
if (lavmodel@group.w.free) {
return.value[[b]]$group.w <- lavimplied$group.w[[b]]
if (add.labels) {
names(return.value[[b]]$group.w) <- "w" # somewhat redundant
}
if (add.class) {
class(return.value[[b]]$group.w) <- c("lavaan.vector", "numeric")
}
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
# residuals: _inspect_sampstat - _inspect_implied
lav_object_inspect_residuals <- function(object, h1 = TRUE,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
lav_residuals(object, type = "raw", h1 = h1,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
}
lav_object_inspect_cov_lv <- function(object, correlation.metric = FALSE,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# compute lv covar
return.value <- computeVETA(lavmodel = object@Model, remove.dummy.lv = TRUE)
# nblocks
nblocks <- length(return.value)
# cor + labels + class
for (b in seq_len(nblocks)) {
if (correlation.metric && nrow(return.value[[b]]) > 1L) {
# note: cov2cor fails if matrix is empty!
return.value[[b]] <- cov2cor(return.value[[b]])
}
if (add.labels) {
colnames(return.value[[b]]) <- rownames(return.value[[b]]) <-
object@pta$vnames$lv[[b]]
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.matrix.symmetric", "matrix")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_mean_lv <- function(object,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# compute lv means
return.value <- computeEETA(lavmodel = object@Model,
lavsamplestats = object@SampleStats,
remove.dummy.lv = TRUE)
# nblocks
nblocks <- length(return.value)
# ensure numeric
return.value <- lapply(return.value, as.numeric)
# labels + class
for (b in seq_len(nblocks)) {
if (add.labels && length(return.value[[b]]) > 0L) {
names(return.value[[b]]) <- object@pta$vnames$lv[[b]]
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.vector", "numeric")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_cov_all <- function(object, correlation.metric = FALSE,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# compute extended model implied covariance matrix (both ov and lv)
return.value <- computeCOV(lavmodel = object@Model,
remove.dummy.lv = TRUE)
# nblocks
nblocks <- length(return.value)
# cor + labels + class
for (b in seq_len(nblocks)) {
if (correlation.metric && nrow(return.value[[b]]) > 1L) {
# note: cov2cor fails if matrix is empty!
return.value[[b]] <- cov2cor(return.value[[b]])
}
if (add.labels) {
tmp.names <- c(object@pta$vnames$ov.model[[b]],
object@pta$vnames$lv[[b]])
colnames(return.value[[b]]) <- rownames(return.value[[b]]) <- tmp.names
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.matrix.symmetric", "matrix")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_cov_ov <- function(object, correlation.metric = FALSE,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# get model-implied covariance matrix observed
if (object@Model@conditional.x) {
return.value <- object@implied$res.cov
} else {
return.value <- object@implied$cov
}
# nblocks
nblocks <- length(return.value)
# cor + labels + class
for (b in seq_len(nblocks)) {
if (correlation.metric && nrow(return.value[[b]]) > 1L) {
# note: cov2cor fails if matrix is empty!
return.value[[b]] <- cov2cor(return.value[[b]])
}
if (add.labels) {
colnames(return.value[[b]]) <- rownames(return.value[[b]]) <-
object@pta$vnames$ov.model[[b]]
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.matrix.symmetric", "matrix")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_mean_ov <- function(object,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# compute ov means
if (object@Model@conditional.x) {
return.value <- object@implied$res.int
} else {
return.value <- object@implied$mean
}
# nblocks
nblocks <- length(return.value)
# make numeric
return.value <- lapply(return.value, as.numeric)
# labels + class
for (b in seq_len(nblocks)) {
if (add.labels && length(return.value[[b]]) > 0L) {
names(return.value[[b]]) <- object@pta$vnames$ov.model[[b]]
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.vector", "numeric")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_th <- function(object,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# thresholds
if (object@Model@conditional.x) {
return.value <- object@implied$res.th
} else {
return.value <- object@implied$th
}
# nblocks
nblocks <- length(return.value)
# make numeric
return.value <- lapply(return.value, as.numeric)
# labels + class
for (b in seq_len(nblocks)) {
if (length(object@Model@num.idx[[b]]) > 0L) {
num.idx <- which(object@Model@th.idx[[b]] == 0)
return.value[[b]] <- return.value[[b]][-num.idx]
}
if (add.labels && length(return.value[[b]]) > 0L) {
names(return.value[[b]]) <- object@pta$vnames$th[[b]]
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.vector", "numeric")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_th_idx <- function(object,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# thresholds idx
return.value <- object@SampleStats@th.idx
# nblocks
nblocks <- length(return.value)
# labels + class
for (b in seq_len(nblocks)) {
if (add.labels && length(return.value[[b]]) > 0L) {
names(return.value[[b]]) <- object@SampleStats@th.names[[b]]
}
if (add.class && !is.null(return.value[[b]])) {
class(return.value[[b]]) <- c("lavaan.vector", "numeric")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_vy <- function(object,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# 'unconditional' model-implied variances
# - same as diag(Sigma.hat) if all Y are continuous)
# - 1.0 (or delta^2) if categorical
# - if also Gamma, cov.x is used (only if categorical)
return.value <- computeVY(lavmodel = object@Model, GLIST = NULL,
diagonal.only = TRUE)
# nblocks
nblocks <- length(return.value)
# labels + class
for (b in seq_len(nblocks)) {
if (add.labels && length(return.value[[b]]) > 0L) {
if (object@Model@categorical) {
names(return.value[[b]]) <- object@pta$vnames$ov.nox[[b]]
} else {
names(return.value[[b]]) <- object@pta$vnames$ov[[b]]
}
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.vector", "numeric")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_fs_determinacy <- function(object,
squared = TRUE, fs.method = "regression",
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
FS <- lavPredict(object, type = "lv", method = fs.method, rel = TRUE)
return.value <- attr(FS, "rel")
# determinacies or reliabilities?
if (!squared) {
return.value <- lapply(return.value, sqrt)
}
# nblocks
nblocks <- length(return.value)
# ensure numeric
return.value <- lapply(return.value, as.numeric)
# labels + class
for (b in seq_len(nblocks)) {
if (add.labels && length(return.value[[b]]) > 0L) {
names(return.value[[b]]) <- object@pta$vnames$lv[[b]]
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.vector", "numeric")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_theta <- function(object, correlation.metric = FALSE,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# get residual covariances
return.value <- computeTHETA(lavmodel = object@Model)
# nblocks
nblocks <- length(return.value)
# labels + class
for (b in seq_len(nblocks)) {
if (correlation.metric && nrow(return.value[[b]]) > 0L) {
if (all(return.value[[b]] == 0)) {
return.value[[b]] <- return.value[[b]]
} else {
return.value[[b]] <- cov2cor(return.value[[b]])
}
}
if (add.labels && length(return.value[[b]]) > 0L) {
colnames(return.value[[b]]) <- rownames(return.value[[b]]) <-
object@pta$vnames$ov.model[[b]]
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.matrix.symmetric", "matrix")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_missing_coverage <- function(object, # nolint
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
n.g <- object@Data@ngroups
return.value <- vector("list", n.g)
for (g in 1:n.g) {
if (!is.null(object@Data@Mp[[g]])) {
return.value[[g]] <- object@Data@Mp[[g]]$coverage
} else {
nvar <- length(object@Data@ov.names[[g]])
return.value[[g]] <- matrix(1.0, nvar, nvar)
}
if (add.labels && length(return.value[[g]]) > 0L) {
colnames(return.value[[g]]) <- rownames(return.value[[g]]) <-
object@pta$vnames$ov.model[[g]]
}
if (add.class) {
class(return.value[[g]]) <- c("lavaan.matrix.symmetric", "matrix")
}
}
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
lav_object_inspect_missing_patterns <- function(object, # nolint
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
n.g <- object@Data@ngroups
return.value <- vector("list", n.g)
for (g in 1:n.g) {
if (!is.null(object@Data@Mp[[g]])) {
return.value[[g]] <- object@Data@Mp[[g]]$pat
} else {
nvar <- length(object@Data@ov.names[[g]])
return.value[[g]] <- matrix(TRUE, 1L, nvar)
rownames(return.value[[g]]) <- object@Data@nobs[[g]]
}
if (add.labels && length(return.value[[g]]) > 0L) {
colnames(return.value[[g]]) <- object@pta$vnames$ov.model[[g]]
}
if (add.class) {
class(return.value[[g]]) <- c("lavaan.matrix", "matrix")
}
}
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
lav_object_inspect_empty_idx <- function(object,
drop.list.single.group = FALSE) {
n.g <- object@Data@ngroups
# get empty idx
return.value <- vector("list", n.g)
for (g in 1:n.g) {
if (!is.null(object@Data@Mp[[g]])) {
return.value[[g]] <- object@Data@Mp[[g]]$empty.idx
} else {
return.value[[g]] <- integer(0L)
}
}
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
lav_object_inspect_wls_est <- function(object,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# old (<0.6) object?
if (!.hasSlot(object@Data, "block.label")) {
object@Data@block.label <- object@Data@group.label
}
return.value <- lav_model_wls_est(object@Model)
if (add.labels) {
tmp.names <- lav_object_inspect_delta_rownames(object,
drop.list.single.group = FALSE)
}
# nblocks
nblocks <- length(return.value)
for (b in seq_len(nblocks)) {
if (add.labels && length(return.value[[b]]) > 0L &&
object@Data@nlevels == 1L) {
names(return.value[[b]]) <- tmp.names[[b]]
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.vector", "numeric")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_wls_obs <- function(object,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# old (<0.6) object?
if (!.hasSlot(object@Data, "block.label")) {
object@Data@block.label <- object@Data@group.label
}
return.value <- object@SampleStats@WLS.obs ### FIXME: should be in @h1??
if (add.labels) {
tmp.names <- lav_object_inspect_delta_rownames(object,
drop.list.single.group = FALSE)
}
# nblocks
nblocks <- length(return.value)
for (b in seq_len(nblocks)) {
if (add.labels && length(return.value[[b]]) > 0L &&
object@Data@nlevels == 1L) {
names(return.value[[b]]) <- tmp.names[[b]]
}
if (add.class) {
class(return.value[[b]]) <- c("lavaan.vector", "numeric")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_wls_v <- function(object,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
# old (<0.6) object?
if (!.hasSlot(object@Data, "block.label")) {
object@Data@block.label <- object@Data@group.label
}
# return.value <- lav_model_wls_v(lavmodel = object@Model,
# lavsamplestats = object@SampleStats,
# structured = TRUE,
# lavdata = object@Data)
# WLS.V == (traditionally) h1 expected information
return.value <- lav_model_h1_information_expected(lavobject = object)
# this affects fit measures gfi, agfi, pgfi
# nblocks
nblocks <- length(return.value)
# if estimator == "DWLS" or "ULS", we only stored the diagonal
# hence, we create a full matrix here
if (object@Options$estimator %in% c("DWLS", "ULS")) {
return.value <- lapply(return.value,
function(x) {
nr <- NROW(x)
diag(x, nrow = nr, ncol = nr)
})
}
if (add.labels) {
tmp.names <- lav_object_inspect_delta_rownames(object,
drop.list.single.group = FALSE)
}
# label + class
for (b in seq_len(nblocks)) {
# labels
if (add.labels && nrow(return.value[[b]]) > 0L &&
object@Data@nlevels == 1L) {
colnames(return.value[[b]]) <-
rownames(return.value[[b]]) <- tmp.names[[b]]
}
# class
if (add.class) {
class(return.value[[b]]) <- c("lavaan.matrix", "matrix")
}
}
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (nblocks > 1L) {
names(return.value) <- object@Data@block.label
}
return.value
}
lav_object_inspect_sampstat_gamma <- function(object, # nolint
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
if (!is.null(object@SampleStats@NACOV[[1]])) {
return.value <- object@SampleStats@NACOV
} else {
return.value <- lav_object_gamma(object)
}
if (add.labels) {
tmp.names <- lav_object_inspect_delta_rownames(object,
drop.list.single.group = FALSE)
}
# nblocks
nblocks <- length(return.value)
if (nblocks == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
# labels
if (add.labels) {
colnames(return.value) <- rownames(return.value) <- tmp.names[[1]]
}
# class
if (add.class) {
class(return.value) <- c("lavaan.matrix.symmetric", "matrix")
}
} else {
if (object@Data@nlevels == 1L && length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
# labels
if (add.labels) {
for (g in seq_len(object@Data@ngroups)) {
colnames(return.value[[g]]) <-
rownames(return.value[[g]]) <- tmp.names[[g]]
}
}
# class
if (add.class) {
for (g in seq_len(object@Data@ngroups)) {
class(return.value[[g]]) <- c("lavaan.matrix.symmetric", "matrix")
}
}
} else if (object@Data@nlevels > 1L &&
length(object@Data@group.label) == 0L) {
names(return.value) <- object@Data@level.label
}
}
return.value
}
lav_object_inspect_gradient <- function(object,
add.labels = FALSE, add.class = FALSE, logl = FALSE,
optim = FALSE) {
lavmodel <- object@Model
lavdata <- object@Data
lavsamplestats <- object@SampleStats
if (optim) {
logl <- FALSE
}
if (lavsamplestats@missing.flag ||
object@Options$estimator == "PML") {
group.weight <- FALSE
} else {
group.weight <- TRUE
}
current.verbose <- lav_verbose()
if (lav_verbose(FALSE)) on.exit(lav_verbose(current.verbose), TRUE)
dx <- lav_model_gradient(lavmodel = lavmodel,
GLIST = NULL,
lavsamplestats = lavsamplestats,
lavdata = object@Data,
lavcache = object@Cache,
type = "free",
group.weight = group.weight)
lav_verbose(current.verbose)
# if logl, rescale to get gradient wrt the loglikelihood
if (logl) {
if (lavmodel@estimator %in% c("ML")) {
if (lavdata@nlevels == 1L) {
# currently, this is just a sign switch
dx <- -1 * dx
} else {
lavpartable <- object@ParTable
# gradient.log = gradient.obj * (2 * tmp.n) / nclusters
if (lavdata@ngroups == 1L) {
tmp.n <- lavdata@Lp[[1]]$nclusters[[1]]
nclusters <- lavdata@Lp[[1]]$nclusters[[2]]
dx <- dx * (2 * tmp.n) / nclusters
} else {
group.values <- lav_partable_group_values(lavpartable)
for (g in seq_len(lavdata@ngroups)) {
tmp.n <- lavdata@Lp[[g]]$nclusters[[1]]
nclusters <- lavdata@Lp[[g]]$nclusters[[2]]
g.idx <-
which((lavpartable$group ==
group.values[g])[lavpartable$free > 0L])
dx[g.idx] <- dx[g.idx] * (2 * tmp.n) / nclusters
}
}
}
} else {
# FIXME:
# non-likelihood: what to do? just switch the sign for now.
# Note: this is used in lavTestScore()
dx <- -1 * dx
}
}
# optim?
if (optim) {
# 1. scale (note: divide, not multiply!)
if (!is.null(object@optim$parscale)) {
dx <- dx / object@optim$parscale
}
# 2. pack
if (lavmodel@eq.constraints) {
dx <- as.numeric(dx %*% lavmodel@eq.constraints.K)
}
# only for PML: divide by tmp.n (to speed up convergence)
if (lavmodel@estimator == "PML") {
dx <- dx / lavsamplestats@ntotal
}
}
# labels
if (add.labels) {
if (optim && lavmodel@eq.constraints) {
tmp.names.all <- lav_partable_labels(object@ParTable, type = "free")
tmp.seq <- seq_len(length(tmp.names.all))
pack.seq <- as.numeric((tmp.seq - lavmodel@eq.constraints.k0) %*%
+lavmodel@eq.constraints.K)
ok.idx <- which(pack.seq %in% tmp.seq)
tmp.names <- rep("(eq.con)", length(pack.seq))
tmp.names[ok.idx] <- tmp.names.all[pack.seq[ok.idx]]
names(dx) <- tmp.names
} else {
names(dx) <- lav_partable_labels(object@ParTable, type = "free")
}
}
# class
if (add.class) {
class(dx) <- c("lavaan.vector", "numeric")
}
dx
}
lav_object_inspect_hessian <- function(object,
add.labels = FALSE, add.class = FALSE) {
return.value <- lav_model_hessian(lavmodel = object@Model,
lavsamplestats = object@SampleStats,
lavdata = object@Data,
lavcache = object@Cache,
lavoptions = object@Options,
group.weight = TRUE)
# labels
if (add.labels) {
colnames(return.value) <- rownames(return.value) <-
lav_partable_labels(object@ParTable, type = "free")
}
# class
if (add.class) {
class(return.value) <- c("lavaan.matrix.symmetric", "matrix")
}
return.value
}
lav_object_inspect_information <- function(object,
information = "default", augmented = FALSE, inverted = FALSE,
add.labels = FALSE, add.class = FALSE) {
if (information != "default") {
# override option
object@Options$information <- information
}
# backward compatibility
if (.hasSlot(object, "h1")) {
lavh1 <- object@h1
} else {
lavh1 <- lav_h1_implied_logl(lavdata = object@Data,
lavsamplestats = object@SampleStats,
lavoptions = object@Options)
}
return.value <- lav_model_information(lavmodel = object@Model,
lavsamplestats = object@SampleStats,
lavdata = object@Data,
lavcache = object@Cache,
lavimplied = object@implied,
lavh1 = lavh1,
lavoptions = object@Options,
extra = FALSE,
augmented = augmented,
inverted = inverted)
# labels
if (add.labels) {
tmp.names <- lav_partable_labels(object@ParTable, type = "free")
if (augmented) {
n.extra <- nrow(return.value) - length(tmp.names)
if (n.extra > 0L) {
tmp.names <- c(tmp.names, paste("aug", 1:n.extra, sep = ""))
}
}
colnames(return.value) <- rownames(return.value) <- tmp.names
}
# class
if (add.class) {
class(return.value) <- c("lavaan.matrix.symmetric", "matrix")
}
return.value
}
lav_object_inspect_h1_information <- function(object, # nolint
information = "default", h1.information = "default", inverted = FALSE,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
if (information != "default") {
# override option
object@Options$information <- information
}
if (h1.information != "default") {
# override option
object@Options$h1.information <- h1.information
}
lavmodel <- object@Model
lavdata <- object@Data
# list!
return.value <- lav_model_h1_information(lavmodel = lavmodel,
lavsamplestats = object@SampleStats,
lavdata = lavdata,
lavcache = object@Cache,
lavimplied = object@implied,
lavh1 = object@h1,
lavoptions = object@Options)
# inverted? (NOT USED)
# if(inverted) {
# return.value <- lapply(return.value, solve) # FIXME: handle errors...
# }
if (add.labels) {
tmp.names <- lav_object_inspect_delta_rownames(object,
drop.list.single.group = FALSE)
}
# labels/class per group
for (g in seq_len(lavmodel@ngroups)) {
# labels
if (add.labels) {
colnames(return.value[[g]]) <-
rownames(return.value[[g]]) <- tmp.names[[g]]
}
# class
if (add.class) {
class(return.value[[g]]) <- c("lavaan.matrix.symmetric", "matrix")
}
}
# drop list?
if (lavmodel@ngroups == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (!is.null(lavdata)) {
if (length(lavdata@group.label) > 0L) {
names(return.value) <- unlist(lavdata@group.label)
}
}
return.value
}
# only to provide a direct function to the old 'getVariability()' function
lav_object_inspect_firstorder <- function(object,
add.labels = FALSE, add.class = FALSE) {
tmp.b0 <- lav_model_information_firstorder(lavmodel = object@Model,
lavsamplestats = object@SampleStats,
lavdata = object@Data,
lavcache = object@Cache,
lavoptions = object@Options,
check.pd = FALSE,
augmented = FALSE,
inverted = FALSE)
attr(tmp.b0, "B0.group") <- NULL
return.value <- tmp.b0
# labels
if (add.labels) {
colnames(return.value) <- rownames(return.value) <-
lav_partable_labels(object@ParTable, type = "free")
}
# class
if (add.class) {
class(return.value) <- c("lavaan.matrix.symmetric", "matrix")
}
return.value
}
lav_object_inspect_vcov <- function(object, standardized = FALSE,
type = "std.all", free.only = TRUE,
add.labels = FALSE, add.class = FALSE, remove.duplicated = FALSE) {
lavmodel <- object@Model
lavoptions <- object@Options
# store partable with pta in object to use cache in called functions
if (is.null(attr(object, "vnames"))) {
object@ParTable <- lav_partable_set_cache(object@ParTable, object@pta)
}
# rotation?
# if( .hasSlot(lavmodel, "nefa") && (lavmodel@nefa > 0L) &&
# lavoptions$rotation != "none" #&& lavoptions$rotation.se == "delta"
# ) {
# rotation <- TRUE
# } else {
# rotation <- FALSE
# }
if (object@optim$npar == 0) {
return.value <- matrix(0, 0, 0)
} else {
# check if we already have it
# tmp <- try(slot(object, "vcov"), silent = TRUE)
# if( !inherits(tmp, "try-error") && !is.null(object@vcov$vcov)
# && !(rotation && standardized)) {
if (.hasSlot(object, "vcov") && !is.null(object@vcov$vcov)) {
return.value <- object@vcov$vcov # if this changes, tag @TDJorgensen in commit message
} else {
# compute it again
# if(rotation && standardized) {
# lavmodel <- lav_model_set_parameters(lavmodel,
# x = object@optim$x)
# lavoptions <- object@Options
# lavoptions$rotation.se <- "delta"
# }
return.value <- lav_model_vcov(lavmodel = lavmodel,
lavsamplestats = object@SampleStats,
lavoptions = lavoptions,
lavdata = object@Data,
lavcache = object@Cache,
lavimplied = object@implied,
lavh1 = object@h1
)
# if(rotation && !standardized) {
# # fixme: compute tmp.vcov.rot manually...
# stop("lavaan ERROR: rerun with store.vcov = TRUE")
# }
if (is.null(return.value)) {
return(return.value)
}
}
}
# strip attributes
attr(return.value, "E.inv") <- NULL
attr(return.value, "B0") <- NULL
attr(return.value, "B0.group") <- NULL
attr(return.value, "Delta") <- NULL
attr(return.value, "WLS.V") <- NULL
attr(return.value, "tmp.boot.COEF") <- NULL
attr(return.value, "tmp.boot.TEST") <- NULL
# standardized?
if (standardized) {
if (type == "std.lv") {
tmp.fun <- lav_standardize_lv_x
} else if (type == "std.all") {
tmp.fun <- lav_standardize_all_x
} else if (type == "std.nox") {
tmp.fun <- lav_standardize_all_nox_x
}
# if(rotation) {
# if(.hasSlot(object@Model, "ceq.simple.only") &&
# object@Model@ceq.simple.only) {
# x.vec <- drop(object@optim$x %*% t(object@Model@ceq.simple.K))
# } else {
# x.vec <- object@optim$x
# }
# tmp.jac <- numDeriv::jacobian(func = tmp.fun, x = x.vec,
# method = "simple",
# method.args = list(eps = 1e-03), # default is 1e-04
# lavobject = object, rotation = rotation)
# } else {
# if(.hasSlot(object@Model, "ceq.simple.only") &&
# object@Model@ceq.simple.only) {
# x <- lav_model_get_parameters(lavmodel)
# x.vec <- drop(x %*% t(object@Model@ceq.simple.K))
# } else {
x.vec <- lav_model_get_parameters(lavmodel)
# }
tmp.jac <- try(lav_func_jacobian_complex(func = tmp.fun, x = x.vec,
lavobject = object),
silent = TRUE)
if (inherits(tmp.jac, "try-error")) { # eg. pnorm()
tmp.jac <- lav_func_jacobian_simple(func = tmp.fun, x = x.vec,
lavobject = object)
}
# }
# tmp.jac contains *all* parameters in the parameter table
if (free.only) {
if (.hasSlot(object@Model, "ceq.simple.only") &&
object@Model@ceq.simple.only) {
free.idx <- which(object@ParTable$free > 0L &&
!duplicated(object@ParTable$free))
} else {
free.idx <- which(object@ParTable$free > 0L)
}
tmp.jac <- tmp.jac[free.idx, , drop = FALSE]
}
return.value <- tmp.jac %*% return.value %*% t(tmp.jac)
# force return.value to be symmetric and pd
return.value <- (return.value + t(return.value)) / 2
# return.value <- lav_matrix_symmetric_force_pd(return.value,
# tol = 1e-09) # was 1e-06 < 0.6-9
}
# labels
if (add.labels) {
# if(rotation && !free.only) {
# # todo
# } else {
colnames(return.value) <- rownames(return.value) <-
lav_partable_labels(object@ParTable,
## add "user" labels?
type = ifelse(standardized && !free.only,
"user", "free"))
# }
}
# alias?
if (remove.duplicated && lavmodel@eq.constraints) {
simple.flag <- lav_constraints_check_simple(lavmodel)
if (simple.flag) {
tmp.lab <- lav_partable_labels(object@ParTable, type = "free")
dup.flag <- duplicated(tmp.lab)
return.value <- return.value[!dup.flag, !dup.flag, drop = FALSE]
} else {
lav_msg_warn(
gettext("alias is TRUE, but equality constraints do not appear
to be simple; returning full vcov"))
}
}
# class
if (add.class) {
class(return.value) <- c("lavaan.matrix.symmetric", "matrix")
}
return.value
}
lav_object_inspect_vcov_def <- function(object, joint = FALSE,
standardized = FALSE, type = "std.all",
add.labels = FALSE, add.class = FALSE) {
lavmodel <- object@Model
lavpartable <- object@ParTable
free.idx <- which(lavpartable$free > 0L)
def.idx <- which(lavpartable$op == ":=")
joint.idx <- c(free.idx, def.idx)
if (!joint && length(def.idx) == 0L) {
return(matrix(0, 0, 0))
} else if (joint && length(joint.idx) == 0L) {
return(matrix(0, 0, 0))
}
if (standardized) {
# compute tmp.vcov for "free" parameters only
tmp.vcov <- lav_object_inspect_vcov(object,
standardized = TRUE,
type = type, free.only = FALSE,
add.labels = FALSE, add.class = FALSE)
if (joint) {
return.value <- tmp.vcov[joint.idx, joint.idx, drop = FALSE]
} else {
return.value <- tmp.vcov[def.idx, def.idx, drop = FALSE]
}
} else {
# get free parameters
x <- lav_model_get_parameters(lavmodel, type = "free")
# bootstrap or not?
if (!is.null(object@boot$coef)) {
tmp.boot <- object@boot$coef
# remove NA rows
error.idx <- attr(tmp.boot, "error.idx")
if (length(error.idx) > 0L) {
tmp.boot <- tmp.boot[-error.idx, , drop = FALSE] # drops attributes
}
tmp.boot.def <- apply(tmp.boot, 1L, lavmodel@def.function)
if (length(def.idx) == 1L) {
tmp.boot.def <- as.matrix(tmp.boot.def)
} else {
tmp.boot.def <- t(tmp.boot.def)
}
return.value <- cov(tmp.boot.def)
} else {
# tmp.vcov
tmp.vcov <- lav_object_inspect_vcov(object,
standardized = FALSE,
type = type, free.only = TRUE,
add.labels = FALSE,
add.class = FALSE)
# regular delta method
tmp.jac <- try(lav_func_jacobian_complex(func = lavmodel@def.function,
x = x), silent = TRUE)
if (inherits(tmp.jac, "try-error")) { # eg. pnorm()
tmp.jac <- lav_func_jacobian_simple(func = lavmodel@def.function,
x = x)
}
if (joint) {
tmp.jac2 <- rbind(diag(nrow = ncol(tmp.jac)), tmp.jac)
return.value <- tmp.jac2 %*% tmp.vcov %*% t(tmp.jac2)
} else {
return.value <- tmp.jac %*% tmp.vcov %*% t(tmp.jac)
}
}
}
# labels
if (add.labels) {
if (joint) {
lhs.names <- lavpartable$lhs[joint.idx]
} else {
lhs.names <- lavpartable$lhs[def.idx]
}
colnames(return.value) <- rownames(return.value) <- lhs.names
}
# class
if (add.class) {
class(return.value) <- c("lavaan.matrix.symmetric", "matrix")
}
return.value
}
lav_object_inspect_UGamma <- function(object, # nolint
add.labels = FALSE, add.class = FALSE) {
out <- lav_test_satorra_bentler(lavobject = object,
method = "original",
return.ugamma = TRUE)
return.value <- out$UGamma
# labels
# if(add.labels) {
# colnames(return.value) <- rownames(return.value) <-
# }
# class
if (add.class) {
class(return.value) <- c("lavaan.matrix.symmetric", "matrix")
}
return.value
}
lav_object_inspect_UfromUGamma <- function(object, # nolint
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
out <- lav_test_satorra_bentler(lavobject = object,
method = "original",
return.u = TRUE)
return.value <- out$UfromUGamma
# labels
# if(add.labels) {
# colnames(return.value) <- rownames(return.value) <-
# }
# class
if (add.class) {
class(return.value) <- c("lavaan.matrix.symmetric", "matrix")
}
return.value
}
# Delta (jacobian: d samplestats / d free_parameters)
lav_object_inspect_delta <- function(object,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
lavmodel <- object@Model
lavdata <- object@Data
lavpartable <- object@ParTable
lavpta <- object@pta
return.value <- lav_object_inspect_delta_internal(lavmodel = lavmodel,
lavdata = lavdata, lavpartable = lavpartable,
add.labels = add.labels, add.class = add.class,
drop.list.single.group = drop.list.single.group)
return.value
}
lav_object_inspect_delta_rownames <- function( # nolint
object,
lavmodel = NULL,
lavpartable = NULL,
drop.list.single.group = FALSE) {
if (!is.null(object)) {
lavmodel <- object@Model
lavpartable <- object@ParTable
lavpta <- object@pta
lavdata <- object@Data
} else {
lavdata <- NULL
lavpta <- lav_partable_attributes(lavpartable)
}
categorical <- lavmodel@categorical
correlation <- FALSE
if (.hasSlot(lavmodel, "correlation")) {
correlation <- lavmodel@correlation
}
conditional.x <- lavmodel@conditional.x
group.w.free <- lavmodel@group.w.free
nvar <- lavmodel@nvar
num.idx <- lavmodel@num.idx
th.idx <- lavmodel@th.idx
nblocks <- lavmodel@nblocks
# store names per block, rbind later
tmp.names <- vector("list", length = nblocks)
# output is per group
return.value <- vector("list", lavmodel@ngroups)
for (g in 1:nblocks) {
if (conditional.x) {
ov.names <- lavpta$vnames$ov.nox[[g]]
} else {
ov.names <- lavpta$vnames$ov[[g]]
}
ov.names.x <- lavpta$vnames$ov.x[[g]]
nvar <- length(ov.names)
names.cov <- names.cor <- names.var <- character(0L)
names.mu <- names.pi <- names.th <- character(0L)
names.gw <- character(0L)
# Sigma
# - if continuous: vech(Sigma)
# - if categorical: first numeric variances, then
# tmp <- apply(expand.grid(ov.names, ov.names), 1L,
# paste, collapse = "~~")
# if(categorical) {
# names.cor <- tmp[lav_matrix_vech_idx(nvar, diagonal = FALSE)]
# names.var <- tmp[lav_matrix_diag_idx(nvar)[num.idx[[g]]]]
# } else {
# names.cov <- tmp[lav_matrix_vech_idx(nvar, diagonal = TRUE)]
# }
# NOTE: in 0.6-1, we use the same order, but 'label' in row-wise
# format (eg x1 ~~ x2 instead of x2 ~~ x1)
tmp <- matrix(apply(expand.grid(ov.names, ov.names), 1L,
paste, collapse = "~~"), nrow = nvar)
if (categorical) {
names.cor <- lav_matrix_vechru(tmp, diagonal = FALSE)
names.var <- diag(tmp)[num.idx[[g]]]
} else if (correlation) {
names.cor <- lav_matrix_vechru(tmp, diagonal = FALSE)
} else {
names.cov <- lav_matrix_vechru(tmp, diagonal = TRUE)
}
# Mu
if (!categorical && lavmodel@meanstructure) {
names.mu <- paste(ov.names, "~1", sep = "")
}
# Pi
if (conditional.x && lavmodel@nexo[g] > 0L) {
names.pi <- apply(expand.grid(ov.names, ov.names.x), 1L,
paste, collapse = "~")
}
# th
if (categorical) {
names.th <- lavpta$vnames$th[[g]]
# interweave numeric intercepts, if any
if (length(num.idx[[g]]) > 0L) {
tmp <- character(length(th.idx[[g]]))
tmp[th.idx[[g]] > 0] <- names.th
tmp[th.idx[[g]] == 0] <- paste(ov.names[num.idx[[g]]],
"~1", sep = "")
names.th <- tmp
}
}
# gw
if (group.w.free) {
names.gw <- "w"
}
tmp.names[[g]] <- c(names.gw,
names.th, names.mu,
names.pi,
names.cov, names.var, names.cor)
} # blocks
# multilevel?
if (.hasSlot(lavmodel, "multilevel") && lavmodel@multilevel) {
for (g in 1:lavmodel@ngroups) {
return.value[[g]] <- c(tmp.names[[(g - 1) * 2 + 1]],
tmp.names[[(g - 1) * 2 + 2]])
}
} else {
return.value <- tmp.names
}
# drop list?
if (lavmodel@ngroups == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else if (!is.null(lavdata)) {
if (length(lavdata@group.label) > 0L) {
names(return.value) <- unlist(lavdata@group.label)
}
}
return.value
}
lav_object_inspect_delta_internal <- function( # nolint
lavmodel = NULL, lavdata = NULL,
lavpartable = NULL,
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
return.value <- computeDelta(lavmodel)
if (add.labels) {
tmp.pnames <- lav_partable_labels(lavpartable, type = "free")
tmp.rownames <- lav_object_inspect_delta_rownames(object = NULL,
lavmodel = lavmodel, lavpartable = lavpartable,
drop.list.single.group = FALSE)
}
for (g in seq_len(lavmodel@ngroups)) {
# add labels
if (add.labels) {
colnames(return.value[[g]]) <- tmp.pnames
rownames(return.value[[g]]) <- tmp.rownames[[g]]
}
# add class
if (add.class) {
class(return.value[[g]]) <- c("lavaan.matrix", "matrix")
}
} # ngroups
# drop list?
if (lavmodel@ngroups == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(lavdata@group.label) > 0L) {
names(return.value) <- unlist(lavdata@group.label)
}
}
return.value
}
lav_object_inspect_zero_cell_tables <- # nolint
function(object, add.labels = FALSE, add.class = FALSE,
drop.list.single.group = FALSE) {
# categorical?
if (!object@Model@categorical) {
lav_msg_warn(gettext("no categorical variables in fitted model"))
return(invisible(list()))
}
lavdata <- object@Data
# create 2-way tables
tmp.table <- lavTables(object, dimension = 2L, output = "data.frame",
statistic = NULL)
# select tables with empty cells
empty.id <- tmp.table$id[which(tmp.table$obs.freq == 0)]
if (length(empty.id) == 0L) {
# only when lavInspect() is used, give message
if (add.class) {
cat("(There are no tables with empty cells for this fitted model)\n")
}
return(invisible(list()))
} else {
return.value <- lav_tables_cells_format(
tmp.table[tmp.table$id %in% empty.id, ],
lavdata = lavdata,
drop.list.single.group = drop.list.single.group)
}
return.value
}
lav_object_inspect_coef <- function(object, type = "free",
add.labels = FALSE, add.class = FALSE) {
if (type == "user" || type == "all") {
type <- "user"
idx <- seq_along(object@ParTable$lhs)
} else if (type == "free") {
#idx <- which(object@ParTable$free > 0L & !duplicated(object@ParTable$free))
idx <- which(object@ParTable$free > 0L)
} else {
lav_msg_stop(gettextf(
"%1$s argument must be either %2$s or %3$s",
"type", "free", "user"))
}
tmp.est <- lav_object_inspect_est(object)
cof <- tmp.est[idx]
# labels?
if (add.labels) {
names(cof) <- lav_partable_labels(object@ParTable, type = type)
}
# class
if (add.class) {
class(cof) <- c("lavaan.vector", "numeric")
}
cof
}
lav_object_inspect_npar <- function(object, type = "free") {
if (type == "free") {
npar <- sum(object@ParTable$free > 0L &
!duplicated(object@ParTable$free))
} else {
npar <- length(object@ParTable$lhs)
}
npar
}
lav_object_inspect_icc <- function(object, add.labels = FALSE,
add.class = FALSE,
drop.list.single.group = FALSE) {
lavdata <- object@Data
n.g <- lavdata@ngroups
return.value <- vector("list", n.g)
# clustered data?
if (length(lavdata@cluster) == 0L) {
lav_msg_stop(gettext(
"intraclass correlation only available for clustered data"))
} else if (lavdata@nlevels == 1L) {
lav_msg_stop(gettext(
"intraclass correlation only available if the model syntax
contains levels"))
}
if (length(object@h1) == 0L) {
lav_msg_stop(gettext("h1 slot is not available; refit with h1 = TRUE"))
}
# implied statistics
implied <- object@h1$implied
for (g in 1:n.g) {
sigma.w <- implied$cov[[(g - 1) * lavdata@nlevels + 1]]
sigma.b <- implied$cov[[(g - 1) * lavdata@nlevels + 2]]
w.diag <- diag(sigma.w)
b.diag <- diag(sigma.b)
return.value[[g]] <- numeric(length(w.diag))
ov.names.l <- lavdata@ov.names.l[[g]]
w.idx <- which(ov.names.l[[1]] %in% ov.names.l[[2]])
w.names <- ov.names.l[[1]][w.idx]
b.idx <- match(w.names, ov.names.l[[2]])
return.value[[g]][w.idx] <- b.diag[b.idx] / (w.diag[w.idx] + b.diag[b.idx])
# label
if (add.labels) {
names(return.value[[g]]) <- ov.names.l[[1]]
}
# class
if (add.class) {
class(return.value[[g]]) <- c("lavaan.vector", "numeric")
}
} # g
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
lav_object_inspect_ranef <- function(object, add.labels = FALSE,
add.class = FALSE,
drop.list.single.group = FALSE) {
lavdata <- object@Data
lavsamplestats <- object@SampleStats
n.g <- lavdata@ngroups
return.value <- vector("list", n.g)
# multilevel?
if (lavdata@nlevels == 1L) {
lav_msg_stop(gettext(
"random effects only available for clustered data (in the long format)"))
}
# implied statistics
lavimplied <- object@implied
for (g in 1:n.g) {
tmp.lp <- lavdata@Lp[[g]]
tmp.ylp <- lavsamplestats@YLp[[g]]
# implied for this group
group.idx <- (g - 1) * lavdata@nlevels + seq_len(lavdata@nlevels)
implied.group <- lapply(lavimplied, function(x) x[group.idx])
# random effects (=random intercepts or cluster means)
out <- lav_mvnorm_cluster_implied22l(Lp = tmp.lp, implied = implied.group)
mb.j <- lav_mvnorm_cluster_em_estep_ranef(YLp = tmp.ylp, Lp = tmp.lp,
sigma.w = out$sigma.w, sigma.b = out$sigma.b,
sigma.zz = out$sigma.zz, sigma.yz = out$sigma.yz,
mu.z = out$mu.z, mu.w = out$mu.w, mu.b = out$mu.b,
se = FALSE)
return.value[[g]] <- mb.j
ov.names.l <- lavdata@ov.names.l[[g]]
# label
if (add.labels) {
colnames(return.value[[g]]) <- ov.names.l[[1]]
}
# class
if (add.class) {
class(return.value[[g]]) <- c("lavaan.matrix", "matrix")
}
} # g
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
# casewise loglikelihood contributions
lav_object_inspect_loglik_casewise <- function(object, log. = TRUE, # nolint
add.labels = FALSE, add.class = FALSE, drop.list.single.group = FALSE) {
lavdata <- object@Data
lavsamplestats <- object@SampleStats
lavimplied <- object@implied
lavoptions <- object@Options
n.g <- lavdata@ngroups
return.value <- vector("list", n.g)
# multilevel?
if (lavdata@nlevels > 1L) {
lav_msg_stop(gettext(
"casewise (log)likeloods contributions not yet available for clustered data"))
}
# estimator ML?
if (object@Options$estimator != "ML") {
lav_msg_stop(gettextf(
"casewise (log)likeloods contributions only available for estimator = %s",
dQuote("ML")))
}
for (g in 1:n.g) {
if (lavsamplestats@missing.flag) {
return.value[[g]] <-
lav_mvnorm_missing_llik_casewise(Y = lavdata@X[[g]],
wt = lavdata@weights[[g]],
Mu = lavimplied$mean[[g]],
Sigma = lavimplied$cov[[g]],
x.idx = lavsamplestats@x.idx[[g]])
} else { # single-level, complete data
if (lavoptions$conditional.x) {
if (!is.null(lavdata@weights[[g]])) {
lav_msg_stop(gettext("no support (yet) if weights are used."))
}
return.value[[g]] <- lav_mvreg_loglik_data(
Y = lavdata@X[[g]],
eXo = lavdata@eXo[[g]],
res.int = lavimplied$res.int[[g]],
res.slopes = lavimplied$res.slopes[[g]],
res.cov = lavimplied$res.cov[[g]],
casewise = TRUE)
} else {
if (object@Model@meanstructure) {
tmp.mean <- lavimplied$mean[[g]]
} else {
tmp.mean <- lavsamplestats@mean[[g]]
}
return.value[[g]] <-
lav_mvnorm_loglik_data(Y = lavdata@X[[g]],
wt = lavdata@weights[[g]],
Mu = tmp.mean,
Sigma = lavimplied$cov[[g]],
x.idx = lavsamplestats@x.idx[[g]],
casewise = TRUE)
}
} # single-level, complete data
# log. = FALSE?
if (!log.) {
return.value[[g]] <- exp(return.value[[g]])
}
# label
# if(add.labels) {
# }
# class
if (add.class) {
class(return.value[[g]]) <- c("lavaan.vector", "numeric")
}
} # g
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
# Mahalanobis distances for factor scores or casewise residuals
# type = "lv" -> factor scores
# type = "resid" -> casewise residuals
#
# we always use Bartlett factor scores (see Yuan & Hayashi 2010)
# (this has no impact on the m-distances for the factor scores,
# and only a very slight impact on the m-distances for the casewise
# residuals; but asymptotically, only when we use Bartlett factor
# scores are the 'true scores' (=LAMBDA %*% FS) orthogonal to the
# casewise residuals)
lav_object_inspect_mdist2 <- function(object, type = "resid", squared = TRUE,
add.labels = FALSE, add.class = FALSE,
drop.list.single.group = FALSE) {
lavdata <- object@Data
n.g <- lavdata@ngroups
# lavPredict()
out <- lavPredict(object, type = type, method = "ML", # = Bartlett
label = FALSE, fsm = TRUE, mdist = TRUE,
se = "none", acov = "none")
return.value <- attr(out, "mdist")
for (g in seq_len(n.g)) {
# squared?
if (!squared) {
return.value[[g]] <- sqrt(return.value[[g]])
}
# labels?
# if(add.labels) {
# }
# class
if (add.class) {
class(return.value[[g]]) <- c("lavaan.vector", "numeric")
}
} # g
if (n.g == 1L && drop.list.single.group) {
return.value <- return.value[[1]]
} else {
if (length(object@Data@group.label) > 0L) {
names(return.value) <- unlist(object@Data@group.label)
}
}
return.value
}
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