Get Factor Scores and the Corresponding Standard Error of Measurement
get_fs.Rdget_fs() is an S3 generic that extracts factor scores from fitted models.
Methods are available for data.frame (fits a CFA internally), lavaan
objects, lmerMod objects, and fitted mirt models (single-group
SingleGroupClass and multi-group MultipleGroupClass; mirt is a
Suggests dependency). Multi-group mirt results carry a trailing group
column and a per-group (list) psi attribute.
Usage
# S3 method for class 'data.frame'
get_fs(
object,
model = NULL,
group = NULL,
local = FALSE,
method = c("regression", "Bartlett", "ML", "EB", "mean"),
corrected_fsT = FALSE,
vfsLT = FALSE,
reliability = FALSE,
format = c("unified", "list"),
prior_mean = NULL,
prior_cov = NULL,
sum_items = NULL,
product = NULL,
...
)
# Default S3 method
get_fs(object, ...)
# S3 method for class 'lavaan'
get_fs(
object,
method = c("regression", "Bartlett", "ML", "EB", "mean"),
corrected_fsT = FALSE,
vfsLT = FALSE,
reliability = FALSE,
format = c("unified", "list"),
prior_mean = NULL,
prior_cov = NULL,
sum_items = NULL,
product = NULL,
...
)
# S3 method for class 'merMod'
get_fs(
object,
method = c("EB", "ML"),
corrected_fsT = FALSE,
vfsLT = FALSE,
fsm = FALSE,
format = c("unified", "list"),
legacy_names = FALSE,
...
)
# S3 method for class 'SingleGroupClass'
get_fs(object, prior_mean = NULL, format = c("unified", "list"), ...)
# S3 method for class 'MultipleGroupClass'
get_fs(object, prior_mean = NULL, format = c("unified", "list"), ...)
get_fs(object, ...)Arguments
- object
A data frame, a fitted lavaan model object, or a fitted lme4::lmer model object (
merMod).- model
An optional string specifying the measurement model in
lavaansyntax. Only used whenobjectis a data frame. Seemodel.syntaxfor more information.- group
Character. Name of the grouping variable for multiple group analysis, which is passed to
cfa. Only used whenobjectis a data frame.- local
Logical. When
TRUE(data-frame input only), each latent inmodelis scored from its own local measurement model — the canonical per-construct 2S-PA stage 1 — instead of the single joint multi-factor model.modelmay be a single string (split into per-latentlhs =~ i1 + i2 + ...statements under a strict grammar) or a character vector of length >= 2 (or a named list of strings), each element a complete single-factor model string fit verbatim (the escape hatch for anything the strict grammar rejects, e.g. within-factor residual covariances). SeeDetails. DefaultFALSE(the joint model, the current behavior).model = NULLis a no-op (the auto single-factor model is trivially local); on a fitted model object (lavaan,merMod,mirt) an error is raised.- method
Character. Method for computing factor scores. For
lavaanand data frame objects:"regression"(default, consistent withlavPredict),"Bartlett", or"mean"(a third, distinct method: sum scores, each score being the plain uncentered mean of the items assigned to its factor, using no latent distribution), with"ML"an alias for"Bartlett"and"EB"an alias for"regression". FormerModobjects:"EB"(empirical Bayes, default; identical to the first random-effect term'sranef() estimates) or"ML"(a prior-free, per-cluster OLS estimate of the random effects, using no random-effects prior, analogous to Bartlett scores forlavaanobjects). The"ML"/"EB"aliases apply to the lavaan path only; formerModobjects the two strings are distinct methods. Bartlett scores have more desirable properties than regression scores and may be preferred for 2S-PA.method = "mean"takes the item-to-factor assignment fromsum_items(auto-derived from the estimated loadings whenNULL); it errors when the model was fitted with missing data retained (e.g. FIML/digamma), and is not supported together withcorrected_fsT,vfsLT,reliability,prior_mean, orprior_cov.- corrected_fsT
Logical. Whether to correct for the sampling error in the factor score weights when computing the error variance estimates of factor scores. Currently ignored for
merModobjects.- vfsLT
Logical. Whether to return the covariance matrix of
fsTandfsL, returned as attributevfsLT; used for second-order SE correction of 2S-PA results. Currently ignored formerModobjects.- reliability
Logical. Whether to return the reliability of factor scores. Available only for single-factor lavaan models; for multi-factor models a warning is issued and no
reliabilityattribute is returned.- format
Output format when
objectis a lavaan or merMod object."unified"(default) returns a single data frame; for multiple groups it carries agroupcolumn and attributesfsT,fsL,fsb, andscoring_matrixare named lists keyed by group label."list"returns the legacy shape: a named list of data frames (one per group) with per-group matrix attributes. Usefs_to_group_list()to convert between the two. FormirtSingleGroupClassandMultipleGroupClassobjectsformatis accepted but the output is always a single per-observation data frame; the multi-group result additionally carries a trailinggroupcolumn (the model's group levels,NAfor completely-missing rows) and a per-group (list)psiattribute.- prior_mean
An optional numeric vector of length
q(the number of latent variables) giving fixed external prior means for the latent variables.NULL(default) uses the lavaan-estimated (group-specific) latent means. Non-NULL values are treated as fixed external priors shared across all lavaan groups. FormirtSingleGroupClassobjects it instead sets the factor prior mean used for the EAP scores; the factor-score intercepts (fsb) then vary per observation asVpost_i %*% solve(psi) %*% prior_mean, i.e. the latent mean scaled by the per-observation shrinkage factor (zero whenprior_mean = NULL), wherepsiis the mirt model's estimated factor covariance. FormirtMultipleGroupClassobjects a non-NULLprior_mean(lengthq) is applied as the factor prior mean to every group (mirt's per-group EAP is otherwise centred on a zero-mean standard-normal prior); each observation's regression form uses the factor covariance of its own group. Only supported for lavaan objects with regression (EB) scoring (and for mirt);reliability = TRUEis not supported together with user-suppliedprior_mean/prior_cov, andprior_covis not supported for mirt. Conceptually similar to themeanargument ofmirt::fscores().- prior_cov
An optional numeric
q x qcovariance matrix (a scalar or 1 x 1 matrix is accepted whenq = 1) giving fixed external prior covariance for the latent variables.NULL(default) uses the lavaan-estimated (group-specific) latent covariance. Non-NULL values must be finite, symmetric and positive definite; whenq > 1the matrix must be named (row and column names matching the latent variable names), so its entries map unambiguously onto the model's latent variables. Values are treated as fixed external priors shared across all lavaan groups. Only supported for lavaan objects with regression (EB) scoring;reliability = TRUEis not supported together with user-suppliedprior_mean/prior_cov. Withcorrected_fsT = TRUEorvfsLT = TRUEthe supplied covariance is treated as fixed, i.e. no sampling uncertainty from the prior itself is propagated. Conceptually similar to thecovargument ofmirt::fscores().- sum_items
A named list mapping each factor name to the item names that make up its sum score, e.g.
list(ind60 = c("x1", "x2", "x3"), dem60 = c("y1", "y2", "y3", "y4")).NULL(default) auto-derives the assignment from the estimated loadings, which requires each indicator to load on exactly one factor and every factor to have at least one item. A supplied list must cover all model factors, and each item may belong to only one sum. Only used forlavaanand data frame objects withmethod = "mean".- product
A character string of the form
"a:b + c:d"(pairs of distinct latent names) or a list of length-2 latent-name pairs. When supplied,get_fs()computes the double-mean-centered product indicator columns (fs_a:fs_b), their standard errors (fs_a:fs_b_se) and their implied loadings (fs_a:fs_b_ld) viacompute_fs_prod()and appends them to the result; seecompute_fs_prod()for the derivation. Single-group lavaan models only (v1); not supported withlocal = TRUE.- ...
additional arguments passed to
cfa(whenobjectis a data frame). SeelavOptionsfor a complete list.- fsm
Currently not used.
- legacy_names
Logical. Random-effect score naming convention for
merModobjects.FALSE(default) usesfs_u0-style names (fs_u0/fs_u1/..., with loadingsu0_by_fs_u0and error termsev_fs_u0,ecov_fs_u1_fs_u0).TRUEreproduces the pre-refactoru0_eb-style column names (u0_eb,u0_by_u0_eb,ev_u0_eb,ecov_u0_eb_u1_eb) in the legacy column order. Note the legacy output is name-compatible, not byte-identical, with the pre-refactorget_fs_lmer()result: it additionally carries score-error columns (u0_eb_se, ...), per-clusterfsL/fsTarray attributes, a per-clusterscoring_matrixlist attribute (seeget_fs()), and has NULL row names (the pre-refactor output had none of these and used the ranef subject IDs as row names).
Value
A data frame containing the factor scores (with prefix "fs_"),
the standard errors (with suffix "_se"), the implied loadings
of indicator _by_ factor scores, and the error variance-covariance
of the factor scores (with prefix "ev_" or "ecov_").
For multi-group lavaan models in "unified" format, a group column
is included. The following attributes are attached:
* fsT: error covariance of factor scores (matrix or named list by group)
* fsL: loading matrix of factor scores (matrix or named list by group)
* fsb: intercepts of factor scores (vector or named list by
group); with method = "mean" the intercept is the mean of the
factor's item intercepts, the measurement intercept of the score
regressed on the uncentered latent (same E[fs] - fsL %*% alpha
convention as the other methods; equals the score's column mean
for models without a mean structure)
* scoring_matrix: weights for computing factor scores from the
observed data, as a named list. For lavaan models: one
score x item matrix per group; with method = "mean" the
weights are the item-mean weights, so S %*% y reproduces the
raw scores exactly (no centering offset). For merMod models:
one num_re x n_j matrix per cluster, where
S_j %*% (y_j - X_j %*% beta) with y_j/X_j the cluster's
rows of the model response and the fixed-effects design
reproduces the cluster's EB scores for method "EB" and the
per-cluster OLS (ML) scores for method "ML".
* psi: effective (prior-adjusted) covariance matrix of the
latent variables (q x q), group-level (not per-pattern), and
a point estimate only (no sampling SEs of the latents are
attached). Mirrors the fsT shape: a named list keyed by group
label for "unified" output; a direct attribute on each group
data frame (plus a list-valued attribute on the outer list) for
"list" output; for merMod objects a single q x q matrix.
With prior_cov supplied it equals the prior (shared across
groups), otherwise the per-group lavaan estimate. For merMod
objects the matrix is the first random-effects term's
VarCorr, with dimnames renamed to match the fsL column
names (u0/u1/..., or the legacy u0_eb/u1_eb names).
* alpha: effective (prior-adjusted) means of the latent
variables (a named vector of length q), with the same group
nesting and point-estimate semantics as psi. With
prior_mean supplied it equals the prior, otherwise the
per-group lavaan estimate; a named zero vector (0 per latent)
when the model has no (estimated) mean structure. For merMod
objects a named zero vector (random effects are mean zero).
* fs_pattern: for lavaan models, a named list by group of
list(label, pat) entries. label is a character vector with
one entry per case in the group giving that case's
observed-indicator pattern name (NA for cases whose indicators
are all missing); pat is a logical matrix with rows =
indicators and one column per pattern, the columns being named
by pattern name.
For a lavaan group without missing data, its `fsT`/`fsL`/`fsb`/
`scoring_matrix` elements are the plain matrix/vector for the whole
group. When a group's cases split into multiple observed-indicator
patterns (missing data), each such element is instead a named list
with one entry per pattern; the pattern name is the observed
indicator names joined with `"+"` in indicator order (e.g.
`"x1+x3"`).
With `local = TRUE` and missing data (e.g. `missing = "fiml"`),
the `fsT`/`fsL`/`fsb`/`scoring_matrix` attributes are instead
per-row lists (one entry per data row) and the result carries a
`per_obs = TRUE` attribute (the same convention as mirt's
`mirt_per_obs`).
Note: for a single-group lavaan fit in `"unified"` format, the
per-group attribute wrappers (`fsT`, `fsL`, `fsb`,
`scoring_matrix`, `psi`, and `alpha`) are each a one-element list
named with the empty string `""`; `x[[""]]` does not match in R
list subsetting, so read these attributes positionally (e.g.
`attr(fs, "fsT")[[1]]`, `attr(fs, "psi")[[1]]`) rather than by
name.Details
When object is a data frame and model is supplied as a lavaan syntax string,
the function internally calls lavaan::cfa() and then dispatches to the
lavaan method. When object is a fitted model object, the appropriate S3
method is called directly.
get_fs() replaced get_fs_lavaan() and get_fs_lmer(), which are now
thin wrappers retained for backward compatibility.
Local per-construct scoring (local = TRUE)
With local = TRUE (data-frame input only), each latent in model is
scored from its own local single-factor measurement model — the
canonical per-construct 2S-PA stage 1 — instead of the single joint
multi-factor model. Two model forms are accepted:
a single string, split into per-latent
lhs =~ i1 + i2 + ...statements under a strict grammar (#comments,;statement separators, and a trailing+line continuation are allowed; everything else — multi-latent left-hand sides, any~~(latent-latent or residual covariance), structural~paths, ordered|~statements, thresholds$, labels,c(), and fixed values — is rejected). The error names the offending line and points to the alternatives (joint mode,local = FALSE, or the vector form);a character vector of length >= 2 (or a named list of strings), each element a complete single-factor model string fit verbatim (any one-factor
lavaansyntax — the escape hatch for what the strict grammar rejects, e.g. within-factor residual covariances). Each element must define exactly one latent; latent order is the element order and latent names must be unique.
The merged result reproduces the joint layout (same columns, same
attribute shapes) with exactly-zero cross terms: the fsT, fsL, and
psi attributes are block-diagonal, and the off-diagonal _by_ loading
columns and all ecov_* columns are zero. The cross-factor structure is
not estimated by design.
Local scores are pure per-construct. With freely correlated factors
they differ from the joint-model scores, because a joint fit scores every
latent from all of the indicators (verified on the 3-factor
PoliticalDemocracy example: maximum score difference 0.369 for
regression and 0.242 for Bartlett scores, and even the joint fit's
per-factor estimates shift, e.g. psi_ind60 0.4485 joint vs 0.4455
local). With the factors constrained uncorrelated (e.g.
ind60 ~~ 0 * dem60), the likelihood factorizes and the local and joint
scores agree to optimizer tolerance (~1e-5).
Missing data: missing = "fiml" (or any FIML option) is forwarded to
each local fit; the result then carries per-row attribute lists (fsT,
fsL, fsb, scoring_matrix with one entry per data row) and a
per_obs = TRUE attribute (the same convention as mirt's
mirt_per_obs). Listwise deletion is rejected with an error, because
each local fit would drop a different set of rows.
Not supported in local mode (v1): vfsLT = TRUE (the separate local
fits have no cross-latent sampling covariances, so
tspa(corrected_se = TRUE) and corrected grand-standardized SEs are not
available from a local stage 1); prior_cov (a q x q prior cannot be
reduced to the per-latent priors the local fits use); and
reliability = TRUE (the per-latent attribute shape is deferred).
group, std.lv, method, corrected_fsT, prior_mean, and
sum_items are supported.
The result is downstream-transparent: it feeds tspa() directly (no
explicit fsT/fsL needed) and works through fs_indiv() and
fs_to_group_list().
Product-score indicators (product)
When product is supplied, get_fs() computes the double-mean-centered
product indicator columns (fs_a:fs_b), their standard errors
(fs_a:fs_b_se) and their implied loadings (fs_a:fs_b_ld) via
compute_fs_prod() and appends them to the result; see
compute_fs_prod() for the derivation. Single-group lavaan models only
(v1); not supported with local = TRUE.
See also
vignette("Two-Stage Path Analysis (2S-PA) Model Examples", package = "R2spa")for end-to-end stage-1/stage-2 examples.vignette("Scoring Matrices: lavaan CFA and lme4", package = "R2spa")for the scoring-matrix internals.vignette("EFA Scores", package = "R2spa")for EFA-based factor scores.vignette("2S-PA with Missing Data", package = "R2spa")formissing = "fiml".
Examples
library(lavaan)
get_fs(PoliticalDemocracy[c("x1", "x2", "x3")])
#> fs_f1 fs_f1_se f1_by_fs_f1 ev_fs_f1
#> 1 -0.52616832 0.1213615 0.9657673 0.01472862
#> 2 0.14365274 0.1213615 0.9657673 0.01472862
#> 3 0.71435592 0.1213615 0.9657673 0.01472862
#> 4 1.23992565 0.1213615 0.9657673 0.01472862
#> 5 0.83190803 0.1213615 0.9657673 0.01472862
#> 6 0.21238453 0.1213615 0.9657673 0.01472862
#> 7 0.11880855 0.1213615 0.9657673 0.01472862
#> 8 0.11322703 0.1213615 0.9657673 0.01472862
#> 9 0.25617279 0.1213615 0.9657673 0.01472862
#> 10 0.37112496 0.1213615 0.9657673 0.01472862
#> 11 0.67281395 0.1213615 0.9657673 0.01472862
#> 12 0.56885577 0.1213615 0.9657673 0.01472862
#> 13 1.31369791 0.1213615 0.9657673 0.01472862
#> 14 0.22042629 0.1213615 0.9657673 0.01472862
#> 15 0.57849228 0.1213615 0.9657673 0.01472862
#> 16 0.37805983 0.1213615 0.9657673 0.01472862
#> 17 0.05734046 0.1213615 0.9657673 0.01472862
#> 18 -0.01609202 0.1213615 0.9657673 0.01472862
#> 19 0.88923616 0.1213615 0.9657673 0.01472862
#> 20 1.11445897 0.1213615 0.9657673 0.01472862
#> 21 0.94657339 0.1213615 0.9657673 0.01472862
#> 22 0.90122770 0.1213615 0.9657673 0.01472862
#> 23 0.58409450 0.1213615 0.9657673 0.01472862
#> 24 0.64089192 0.1213615 0.9657673 0.01472862
#> 25 0.91021968 0.1213615 0.9657673 0.01472862
#> 26 -0.89660969 0.1213615 0.9657673 0.01472862
#> 27 -0.13195991 0.1213615 0.9657673 0.01472862
#> 28 -0.52968769 0.1213615 0.9657673 0.01472862
#> 29 -0.81799629 0.1213615 0.9657673 0.01472862
#> 30 -1.27199371 0.1213615 0.9657673 0.01472862
#> 31 -0.32096024 0.1213615 0.9657673 0.01472862
#> 32 -1.16780103 0.1213615 0.9657673 0.01472862
#> 33 -0.12295473 0.1213615 0.9657673 0.01472862
#> 34 -0.04285945 0.1213615 0.9657673 0.01472862
#> 35 -0.34323505 0.1213615 0.9657673 0.01472862
#> 36 -0.60541633 0.1213615 0.9657673 0.01472862
#> 37 0.17688718 0.1213615 0.9657673 0.01472862
#> 38 -0.55066055 0.1213615 0.9657673 0.01472862
#> 39 -1.05988219 0.1213615 0.9657673 0.01472862
#> 40 -0.04138802 0.1213615 0.9657673 0.01472862
#> 41 -0.12611837 0.1213615 0.9657673 0.01472862
#> 42 -0.60322892 0.1213615 0.9657673 0.01472862
#> 43 -0.11057176 0.1213615 0.9657673 0.01472862
#> 44 -1.06423085 0.1213615 0.9657673 0.01472862
#> 45 -1.08354999 0.1213615 0.9657673 0.01472862
#> 46 -0.84009484 0.1213615 0.9657673 0.01472862
#> 47 -1.14678213 0.1213615 0.9657673 0.01472862
#> 48 -0.57578976 0.1213615 0.9657673 0.01472862
#> 49 0.07186692 0.1213615 0.9657673 0.01472862
#> 50 0.14682421 0.1213615 0.9657673 0.01472862
#> 51 0.35871830 0.1213615 0.9657673 0.01472862
#> 52 -0.43403195 0.1213615 0.9657673 0.01472862
#> 53 0.44603111 0.1213615 0.9657673 0.01472862
#> 54 0.26352000 0.1213615 0.9657673 0.01472862
#> 55 0.55051165 0.1213615 0.9657673 0.01472862
#> 56 0.23453122 0.1213615 0.9657673 0.01472862
#> 57 0.27968138 0.1213615 0.9657673 0.01472862
#> 58 0.70960640 0.1213615 0.9657673 0.01472862
#> 59 0.25227978 0.1213615 0.9657673 0.01472862
#> 60 1.18849297 0.1213615 0.9657673 0.01472862
#> 61 0.21104946 0.1213615 0.9657673 0.01472862
#> 62 -1.16516281 0.1213615 0.9657673 0.01472862
#> 63 -0.85560065 0.1213615 0.9657673 0.01472862
#> 64 0.13398476 0.1213615 0.9657673 0.01472862
#> 65 -0.07912189 0.1213615 0.9657673 0.01472862
#> 66 -0.27146711 0.1213615 0.9657673 0.01472862
#> 67 -0.04417217 0.1213615 0.9657673 0.01472862
#> 68 -1.33425662 0.1213615 0.9657673 0.01472862
#> 69 -0.38720750 0.1213615 0.9657673 0.01472862
#> 70 -0.55355511 0.1213615 0.9657673 0.01472862
#> 71 -0.72242623 0.1213615 0.9657673 0.01472862
#> 72 0.30607449 0.1213615 0.9657673 0.01472862
#> 73 0.77707950 0.1213615 0.9657673 0.01472862
#> 74 0.06847481 0.1213615 0.9657673 0.01472862
#> 75 -0.11052927 0.1213615 0.9657673 0.01472862
# Multiple factors
get_fs(PoliticalDemocracy[c("x1", "x2", "x3", "y1", "y2", "y3", "y4")],
model = " ind60 =~ x1 + x2 + x3
dem60 =~ y1 + y2 + y3 + y4 ")
#> fs_ind60 fs_dem60 fs_ind60_se fs_dem60_se ind60_by_fs_ind60
#> 1 -0.54258816 -2.74640573 0.1245694 0.6307323 0.9553858
#> 2 0.12647664 -2.85646114 0.1245694 0.6307323 0.9553858
#> 3 0.73408891 2.74401728 0.1245694 0.6307323 0.9553858
#> 4 1.25253604 3.10856431 0.1245694 0.6307323 0.9553858
#> 5 0.83355267 1.92455641 0.1245694 0.6307323 0.9553858
#> 6 0.22426801 1.02292332 0.1245694 0.6307323 0.9553858
#> 7 0.12517739 1.00406461 0.1245694 0.6307323 0.9553858
#> 8 0.11783867 -0.37216403 0.1245694 0.6307323 0.9553858
#> 9 0.25175134 -1.24897911 0.1245694 0.6307323 0.9553858
#> 10 0.39938631 2.85267059 0.1245694 0.6307323 0.9553858
#> 11 0.67497777 1.41959595 0.1245694 0.6307323 0.9553858
#> 12 0.56462020 1.08769844 0.1245694 0.6307323 0.9553858
#> 13 1.31236592 1.54090232 0.1245694 0.6307323 0.9553858
#> 14 0.23246021 1.77370863 0.1245694 0.6307323 0.9553858
#> 15 0.58638481 2.45676871 0.1245694 0.6307323 0.9553858
#> 16 0.38404785 2.35887573 0.1245694 0.6307323 0.9553858
#> 17 0.05076465 0.04034088 0.1245694 0.6307323 0.9553858
#> 18 -0.01747337 -1.86718064 0.1245694 0.6307323 0.9553858
#> 19 0.90920762 3.61477756 0.1245694 0.6307323 0.9553858
#> 20 1.12553557 0.88355273 0.1245694 0.6307323 0.9553858
#> 21 0.97202590 3.62673300 0.1245694 0.6307323 0.9553858
#> 22 0.87820036 -3.02428925 0.1245694 0.6307323 0.9553858
#> 23 0.57540754 -1.51695438 0.1245694 0.6307323 0.9553858
#> 24 0.66221224 2.76341635 0.1245694 0.6307323 0.9553858
#> 25 0.92358281 2.00507336 0.1245694 0.6307323 0.9553858
#> 26 -0.89353051 -0.92008050 0.1245694 0.6307323 0.9553858
#> 27 -0.13984744 -1.19025576 0.1245694 0.6307323 0.9553858
#> 28 -0.53828496 -1.01247764 0.1245694 0.6307323 0.9553858
#> 29 -0.80834865 0.10456709 0.1245694 0.6307323 0.9553858
#> 30 -1.25324343 -0.71847055 0.1245694 0.6307323 0.9553858
#> 31 -0.33373641 -1.61401581 0.1245694 0.6307323 0.9553858
#> 32 -1.17441075 -3.27250363 0.1245694 0.6307323 0.9553858
#> 33 -0.12409974 -1.17530231 0.1245694 0.6307323 0.9553858
#> 34 -0.04239173 -0.53796274 0.1245694 0.6307323 0.9553858
#> 35 -0.34010528 0.74552889 0.1245694 0.6307323 0.9553858
#> 36 -0.58953870 1.61018662 0.1245694 0.6307323 0.9553858
#> 37 0.17453657 -0.28144814 0.1245694 0.6307323 0.9553858
#> 38 -0.54457243 0.37694690 0.1245694 0.6307323 0.9553858
#> 39 -1.05196602 -0.62919501 0.1245694 0.6307323 0.9553858
#> 40 -0.05504697 -0.03346842 0.1245694 0.6307323 0.9553858
#> 41 -0.12364358 -0.38394102 0.1245694 0.6307323 0.9553858
#> 42 -0.59058710 1.35347275 0.1245694 0.6307323 0.9553858
#> 43 -0.11968796 0.89227782 0.1245694 0.6307323 0.9553858
#> 44 -1.07176064 -2.08481096 0.1245694 0.6307323 0.9553858
#> 45 -1.09139097 -2.07944291 0.1245694 0.6307323 0.9553858
#> 46 -0.83287255 1.59590721 0.1245694 0.6307323 0.9553858
#> 47 -1.14519896 -1.53352201 0.1245694 0.6307323 0.9553858
#> 48 -0.56115378 2.08138051 0.1245694 0.6307323 0.9553858
#> 49 0.06493340 -1.04044137 0.1245694 0.6307323 0.9553858
#> 50 0.15671638 1.72618633 0.1245694 0.6307323 0.9553858
#> 51 0.34626130 -1.24967043 0.1245694 0.6307323 0.9553858
#> 52 -0.45158373 -2.31742576 0.1245694 0.6307323 0.9553858
#> 53 0.43233465 -1.07533341 0.1245694 0.6307323 0.9553858
#> 54 0.25779725 -0.02904676 0.1245694 0.6307323 0.9553858
#> 55 0.51730650 -2.78207923 0.1245694 0.6307323 0.9553858
#> 56 0.20104991 -2.49001474 0.1245694 0.6307323 0.9553858
#> 57 0.25318620 -2.52145147 0.1245694 0.6307323 0.9553858
#> 58 0.72354623 1.86717109 0.1245694 0.6307323 0.9553858
#> 59 0.24619740 -0.93321102 0.1245694 0.6307323 0.9553858
#> 60 1.21681210 3.19853937 0.1245694 0.6307323 0.9553858
#> 61 0.18167599 -3.15685030 0.1245694 0.6307323 0.9553858
#> 62 -1.16605067 -3.41334680 0.1245694 0.6307323 0.9553858
#> 63 -0.86491026 -3.11864398 0.1245694 0.6307323 0.9553858
#> 64 0.10990059 -0.47238885 0.1245694 0.6307323 0.9553858
#> 65 -0.07376176 2.95292007 0.1245694 0.6307323 0.9553858
#> 66 -0.28782931 -1.96509718 0.1245694 0.6307323 0.9553858
#> 67 -0.02508160 2.96218478 0.1245694 0.6307323 0.9553858
#> 68 -1.31843215 -1.59567027 0.1245694 0.6307323 0.9553858
#> 69 -0.40462357 -1.79146161 0.1245694 0.6307323 0.9553858
#> 70 -0.55568363 -1.01578892 0.1245694 0.6307323 0.9553858
#> 71 -0.71308015 0.08818212 0.1245694 0.6307323 0.9553858
#> 72 0.31014319 1.70765911 0.1245694 0.6307323 0.9553858
#> 73 0.79092897 1.86102556 0.1245694 0.6307323 0.9553858
#> 74 0.08770237 3.12885767 0.1245694 0.6307323 0.9553858
#> 75 -0.14138149 -2.41398025 0.1245694 0.6307323 0.9553858
#> ind60_by_fs_dem60 dem60_by_fs_ind60 dem60_by_fs_dem60 ev_fs_ind60
#> 1 0.181827 0.005867694 0.8688887 0.01551752
#> 2 0.181827 0.005867694 0.8688887 0.01551752
#> 3 0.181827 0.005867694 0.8688887 0.01551752
#> 4 0.181827 0.005867694 0.8688887 0.01551752
#> 5 0.181827 0.005867694 0.8688887 0.01551752
#> 6 0.181827 0.005867694 0.8688887 0.01551752
#> 7 0.181827 0.005867694 0.8688887 0.01551752
#> 8 0.181827 0.005867694 0.8688887 0.01551752
#> 9 0.181827 0.005867694 0.8688887 0.01551752
#> 10 0.181827 0.005867694 0.8688887 0.01551752
#> 11 0.181827 0.005867694 0.8688887 0.01551752
#> 12 0.181827 0.005867694 0.8688887 0.01551752
#> 13 0.181827 0.005867694 0.8688887 0.01551752
#> 14 0.181827 0.005867694 0.8688887 0.01551752
#> 15 0.181827 0.005867694 0.8688887 0.01551752
#> 16 0.181827 0.005867694 0.8688887 0.01551752
#> 17 0.181827 0.005867694 0.8688887 0.01551752
#> 18 0.181827 0.005867694 0.8688887 0.01551752
#> 19 0.181827 0.005867694 0.8688887 0.01551752
#> 20 0.181827 0.005867694 0.8688887 0.01551752
#> 21 0.181827 0.005867694 0.8688887 0.01551752
#> 22 0.181827 0.005867694 0.8688887 0.01551752
#> 23 0.181827 0.005867694 0.8688887 0.01551752
#> 24 0.181827 0.005867694 0.8688887 0.01551752
#> 25 0.181827 0.005867694 0.8688887 0.01551752
#> 26 0.181827 0.005867694 0.8688887 0.01551752
#> 27 0.181827 0.005867694 0.8688887 0.01551752
#> 28 0.181827 0.005867694 0.8688887 0.01551752
#> 29 0.181827 0.005867694 0.8688887 0.01551752
#> 30 0.181827 0.005867694 0.8688887 0.01551752
#> 31 0.181827 0.005867694 0.8688887 0.01551752
#> 32 0.181827 0.005867694 0.8688887 0.01551752
#> 33 0.181827 0.005867694 0.8688887 0.01551752
#> 34 0.181827 0.005867694 0.8688887 0.01551752
#> 35 0.181827 0.005867694 0.8688887 0.01551752
#> 36 0.181827 0.005867694 0.8688887 0.01551752
#> 37 0.181827 0.005867694 0.8688887 0.01551752
#> 38 0.181827 0.005867694 0.8688887 0.01551752
#> 39 0.181827 0.005867694 0.8688887 0.01551752
#> 40 0.181827 0.005867694 0.8688887 0.01551752
#> 41 0.181827 0.005867694 0.8688887 0.01551752
#> 42 0.181827 0.005867694 0.8688887 0.01551752
#> 43 0.181827 0.005867694 0.8688887 0.01551752
#> 44 0.181827 0.005867694 0.8688887 0.01551752
#> 45 0.181827 0.005867694 0.8688887 0.01551752
#> 46 0.181827 0.005867694 0.8688887 0.01551752
#> 47 0.181827 0.005867694 0.8688887 0.01551752
#> 48 0.181827 0.005867694 0.8688887 0.01551752
#> 49 0.181827 0.005867694 0.8688887 0.01551752
#> 50 0.181827 0.005867694 0.8688887 0.01551752
#> 51 0.181827 0.005867694 0.8688887 0.01551752
#> 52 0.181827 0.005867694 0.8688887 0.01551752
#> 53 0.181827 0.005867694 0.8688887 0.01551752
#> 54 0.181827 0.005867694 0.8688887 0.01551752
#> 55 0.181827 0.005867694 0.8688887 0.01551752
#> 56 0.181827 0.005867694 0.8688887 0.01551752
#> 57 0.181827 0.005867694 0.8688887 0.01551752
#> 58 0.181827 0.005867694 0.8688887 0.01551752
#> 59 0.181827 0.005867694 0.8688887 0.01551752
#> 60 0.181827 0.005867694 0.8688887 0.01551752
#> 61 0.181827 0.005867694 0.8688887 0.01551752
#> 62 0.181827 0.005867694 0.8688887 0.01551752
#> 63 0.181827 0.005867694 0.8688887 0.01551752
#> 64 0.181827 0.005867694 0.8688887 0.01551752
#> 65 0.181827 0.005867694 0.8688887 0.01551752
#> 66 0.181827 0.005867694 0.8688887 0.01551752
#> 67 0.181827 0.005867694 0.8688887 0.01551752
#> 68 0.181827 0.005867694 0.8688887 0.01551752
#> 69 0.181827 0.005867694 0.8688887 0.01551752
#> 70 0.181827 0.005867694 0.8688887 0.01551752
#> 71 0.181827 0.005867694 0.8688887 0.01551752
#> 72 0.181827 0.005867694 0.8688887 0.01551752
#> 73 0.181827 0.005867694 0.8688887 0.01551752
#> 74 0.181827 0.005867694 0.8688887 0.01551752
#> 75 0.181827 0.005867694 0.8688887 0.01551752
#> ecov_fs_dem60_fs_ind60 ev_fs_dem60
#> 1 0.005632564 0.3978232
#> 2 0.005632564 0.3978232
#> 3 0.005632564 0.3978232
#> 4 0.005632564 0.3978232
#> 5 0.005632564 0.3978232
#> 6 0.005632564 0.3978232
#> 7 0.005632564 0.3978232
#> 8 0.005632564 0.3978232
#> 9 0.005632564 0.3978232
#> 10 0.005632564 0.3978232
#> 11 0.005632564 0.3978232
#> 12 0.005632564 0.3978232
#> 13 0.005632564 0.3978232
#> 14 0.005632564 0.3978232
#> 15 0.005632564 0.3978232
#> 16 0.005632564 0.3978232
#> 17 0.005632564 0.3978232
#> 18 0.005632564 0.3978232
#> 19 0.005632564 0.3978232
#> 20 0.005632564 0.3978232
#> 21 0.005632564 0.3978232
#> 22 0.005632564 0.3978232
#> 23 0.005632564 0.3978232
#> 24 0.005632564 0.3978232
#> 25 0.005632564 0.3978232
#> 26 0.005632564 0.3978232
#> 27 0.005632564 0.3978232
#> 28 0.005632564 0.3978232
#> 29 0.005632564 0.3978232
#> 30 0.005632564 0.3978232
#> 31 0.005632564 0.3978232
#> 32 0.005632564 0.3978232
#> 33 0.005632564 0.3978232
#> 34 0.005632564 0.3978232
#> 35 0.005632564 0.3978232
#> 36 0.005632564 0.3978232
#> 37 0.005632564 0.3978232
#> 38 0.005632564 0.3978232
#> 39 0.005632564 0.3978232
#> 40 0.005632564 0.3978232
#> 41 0.005632564 0.3978232
#> 42 0.005632564 0.3978232
#> 43 0.005632564 0.3978232
#> 44 0.005632564 0.3978232
#> 45 0.005632564 0.3978232
#> 46 0.005632564 0.3978232
#> 47 0.005632564 0.3978232
#> 48 0.005632564 0.3978232
#> 49 0.005632564 0.3978232
#> 50 0.005632564 0.3978232
#> 51 0.005632564 0.3978232
#> 52 0.005632564 0.3978232
#> 53 0.005632564 0.3978232
#> 54 0.005632564 0.3978232
#> 55 0.005632564 0.3978232
#> 56 0.005632564 0.3978232
#> 57 0.005632564 0.3978232
#> 58 0.005632564 0.3978232
#> 59 0.005632564 0.3978232
#> 60 0.005632564 0.3978232
#> 61 0.005632564 0.3978232
#> 62 0.005632564 0.3978232
#> 63 0.005632564 0.3978232
#> 64 0.005632564 0.3978232
#> 65 0.005632564 0.3978232
#> 66 0.005632564 0.3978232
#> 67 0.005632564 0.3978232
#> 68 0.005632564 0.3978232
#> 69 0.005632564 0.3978232
#> 70 0.005632564 0.3978232
#> 71 0.005632564 0.3978232
#> 72 0.005632564 0.3978232
#> 73 0.005632564 0.3978232
#> 74 0.005632564 0.3978232
#> 75 0.005632564 0.3978232
# Local per-construct scoring: each latent is scored from its own
# single-factor model (the canonical 2S-PA stage 1) and the results are
# merged into the usual multi-factor layout
get_fs(PoliticalDemocracy[c("x1", "x2", "x3", "y1", "y2", "y3", "y4",
"y5", "y6", "y7", "y8")],
model = " ind60 =~ x1 + x2 + x3
dem60 =~ y1 + y2 + y3 + y4
dem65 =~ y5 + y6 + y7 + y8 ",
local = TRUE)
#> fs_ind60 fs_dem60 fs_dem65 fs_ind60_se fs_dem60_se fs_dem65_se
#> 1 -0.52616832 -2.74872236 -1.37171890 0.1213615 0.6756472 0.5724405
#> 2 0.14365274 -3.03608028 -0.95085100 0.1213615 0.6756472 0.5724405
#> 3 0.71435592 2.67185886 2.73801194 0.1213615 0.6756472 0.5724405
#> 4 1.23992565 2.99369974 1.78509094 0.1213615 0.6756472 0.5724405
#> 5 0.83190803 1.92429320 1.54470402 0.1213615 0.6756472 0.5724405
#> 6 0.21238453 0.99227979 -1.05084086 0.1213615 0.6756472 0.5724405
#> 7 0.11880855 0.99227979 -0.53096192 0.1213615 0.6756472 0.5724405
#> 8 0.11322703 -0.21586296 1.39783099 0.1213615 0.6756472 0.5724405
#> 9 0.25617279 -1.44378438 -0.39762599 0.1213615 0.6756472 0.5724405
#> 10 0.37112496 2.92254844 3.03518364 0.1213615 0.6756472 0.5724405
#> 11 0.67281395 1.34957735 1.69157706 0.1213615 0.6756472 0.5724405
#> 12 0.56885577 0.99227979 1.24608800 0.1213615 0.6756472 0.5724405
#> 13 1.31369791 1.34957735 2.00369398 0.1213615 0.6756472 0.5724405
#> 14 0.22042629 1.84912937 -0.70338102 0.1213615 0.6756472 0.5724405
#> 15 0.57849228 2.31552345 2.88831061 0.1213615 0.6756472 0.5724405
#> 16 0.37805983 2.40746412 0.80499951 0.1213615 0.6756472 0.5724405
#> 17 0.05734046 -0.15715192 -1.19771390 0.1213615 0.6756472 0.5724405
#> 18 -0.01609202 -2.10674549 -0.98511812 0.1213615 0.6756472 0.5724405
#> 19 0.88923616 3.60483453 3.47431108 0.1213615 0.6756472 0.5724405
#> 20 1.11445897 0.63497829 -0.05469352 0.1213615 0.6756472 0.5724405
#> 21 0.94657339 3.60483453 3.62118412 0.1213615 0.6756472 0.5724405
#> 22 0.90122770 -3.39337795 -2.62734059 0.1213615 0.6756472 0.5724405
#> 23 0.58409450 -1.81938756 -1.27737266 0.1213615 0.6756472 0.5724405
#> 24 0.64089192 2.65451561 2.28230961 0.1213615 0.6756472 0.5724405
#> 25 0.91021968 1.97337239 2.21580445 0.1213615 0.6756472 0.5724405
#> 26 -0.89660969 -0.77027212 -1.38899894 0.1213615 0.6756472 0.5724405
#> 27 -0.13195991 -1.27818082 -0.39126438 0.1213615 0.6756472 0.5724405
#> 28 -0.52968769 -0.99453217 -1.91311530 0.1213615 0.6756472 0.5724405
#> 29 -0.81799629 0.42472744 -0.03742971 0.1213615 0.6756472 0.5724405
#> 30 -1.27199371 -0.45058999 -2.42453443 0.1213615 0.6756472 0.5724405
#> 31 -0.32096024 -1.72743303 -0.98362660 0.1213615 0.6756472 0.5724405
#> 32 -1.16780103 -3.22096319 -2.92108665 0.1213615 0.6756472 0.5724405
#> 33 -0.12295473 -1.24467171 -0.98362660 0.1213615 0.6756472 0.5724405
#> 34 -0.04285945 -0.53007648 -2.27361739 0.1213615 0.6756472 0.5724405
#> 35 -0.34323505 0.75106691 0.37463233 0.1213615 0.6756472 0.5724405
#> 36 -0.60541633 1.82492000 -0.19951669 0.1213615 0.6756472 0.5724405
#> 37 0.17688718 -0.28513805 -1.00185417 0.1213615 0.6756472 0.5724405
#> 38 -0.55066055 0.38898019 -1.59180427 0.1213615 0.6756472 0.5724405
#> 39 -1.05988219 -0.56048618 -0.75214950 0.1213615 0.6756472 0.5724405
#> 40 -0.04138802 0.03253769 -1.06763415 0.1213615 0.6756472 0.5724405
#> 41 -0.12611837 -0.22523213 -1.34262601 0.1213615 0.6756472 0.5724405
#> 42 -0.60322892 1.39285768 0.84762975 0.1213615 0.6756472 0.5724405
#> 43 -0.11057176 0.92901951 0.27384517 0.1213615 0.6756472 0.5724405
#> 44 -1.06423085 -1.84123563 0.31144974 0.1213615 0.6756472 0.5724405
#> 45 -1.08354999 -1.82951509 -1.61654871 0.1213615 0.6756472 0.5724405
#> 46 -0.84009484 1.87568627 1.66585009 0.1213615 0.6756472 0.5724405
#> 47 -1.14678213 -1.46208990 -2.62734059 0.1213615 0.6756472 0.5724405
#> 48 -0.57578976 2.39046791 2.06987729 0.1213615 0.6756472 0.5724405
#> 49 0.07186692 -0.86170657 -0.99922629 0.1213615 0.6756472 0.5724405
#> 50 0.14682421 1.81245195 0.80546890 0.1213615 0.6756472 0.5724405
#> 51 0.35871830 -1.46208990 -0.53813741 0.1213615 0.6756472 0.5724405
#> 52 -0.43403195 -2.26050229 -1.18649882 0.1213615 0.6756472 0.5724405
#> 53 0.44603111 -1.17574904 0.50699008 0.1213615 0.6756472 0.5724405
#> 54 0.26352000 0.02760093 1.69204606 0.1213615 0.6756472 0.5724405
#> 55 0.55051165 -3.03608028 -2.62734059 0.1213615 0.6756472 0.5724405
#> 56 0.23453122 -2.68411252 -1.27737266 0.1213615 0.6756472 0.5724405
#> 57 0.27968138 -2.67878261 -1.92858516 0.1213615 0.6756472 0.5724405
#> 58 0.70960640 1.84912937 1.54470402 0.1213615 0.6756472 0.5724405
#> 59 0.25227978 -1.15540938 -1.03962578 0.1213615 0.6756472 0.5724405
#> 60 1.18849297 3.03011868 3.47431108 0.1213615 0.6756472 0.5724405
#> 61 0.21104946 -3.39337795 -2.77421362 0.1213615 0.6756472 0.5724405
#> 62 -1.16516281 -3.39337795 -2.92108665 0.1213615 0.6756472 0.5724405
#> 63 -0.85560065 -3.10602002 -1.23014270 0.1213615 0.6756472 0.5724405
#> 64 0.13398476 -0.44957067 -0.24588734 0.1213615 0.6756472 0.5724405
#> 65 -0.07912189 3.03011868 3.32743805 0.1213615 0.6756472 0.5724405
#> 66 -0.27146711 -1.87744937 0.34647922 0.1213615 0.6756472 0.5724405
#> 67 -0.04417217 3.03011868 2.88681895 0.1213615 0.6756472 0.5724405
#> 68 -1.33425662 -1.23074578 -0.94504866 0.1213615 0.6756472 0.5724405
#> 69 -0.38720750 -1.65235182 -2.33359452 0.1213615 0.6756472 0.5724405
#> 70 -0.55355511 -0.99453217 -0.39275590 0.1213615 0.6756472 0.5724405
#> 71 -0.72242623 0.33138994 -0.30824367 0.1213615 0.6756472 0.5724405
#> 72 0.30607449 1.79828639 1.98383160 0.1213615 0.6756472 0.5724405
#> 73 0.77707950 1.81469038 1.72470940 0.1213615 0.6756472 0.5724405
#> 74 0.06847481 3.22923146 3.18205667 0.1213615 0.6756472 0.5724405
#> 75 -0.11052927 -2.44305891 -2.33508605 0.1213615 0.6756472 0.5724405
#> ind60_by_fs_ind60 ind60_by_fs_dem60 ind60_by_fs_dem65 dem60_by_fs_ind60
#> 1 0.9657673 0 0 0
#> 2 0.9657673 0 0 0
#> 3 0.9657673 0 0 0
#> 4 0.9657673 0 0 0
#> 5 0.9657673 0 0 0
#> 6 0.9657673 0 0 0
#> 7 0.9657673 0 0 0
#> 8 0.9657673 0 0 0
#> 9 0.9657673 0 0 0
#> 10 0.9657673 0 0 0
#> 11 0.9657673 0 0 0
#> 12 0.9657673 0 0 0
#> 13 0.9657673 0 0 0
#> 14 0.9657673 0 0 0
#> 15 0.9657673 0 0 0
#> 16 0.9657673 0 0 0
#> 17 0.9657673 0 0 0
#> 18 0.9657673 0 0 0
#> 19 0.9657673 0 0 0
#> 20 0.9657673 0 0 0
#> 21 0.9657673 0 0 0
#> 22 0.9657673 0 0 0
#> 23 0.9657673 0 0 0
#> 24 0.9657673 0 0 0
#> 25 0.9657673 0 0 0
#> 26 0.9657673 0 0 0
#> 27 0.9657673 0 0 0
#> 28 0.9657673 0 0 0
#> 29 0.9657673 0 0 0
#> 30 0.9657673 0 0 0
#> 31 0.9657673 0 0 0
#> 32 0.9657673 0 0 0
#> 33 0.9657673 0 0 0
#> 34 0.9657673 0 0 0
#> 35 0.9657673 0 0 0
#> 36 0.9657673 0 0 0
#> 37 0.9657673 0 0 0
#> 38 0.9657673 0 0 0
#> 39 0.9657673 0 0 0
#> 40 0.9657673 0 0 0
#> 41 0.9657673 0 0 0
#> 42 0.9657673 0 0 0
#> 43 0.9657673 0 0 0
#> 44 0.9657673 0 0 0
#> 45 0.9657673 0 0 0
#> 46 0.9657673 0 0 0
#> 47 0.9657673 0 0 0
#> 48 0.9657673 0 0 0
#> 49 0.9657673 0 0 0
#> 50 0.9657673 0 0 0
#> 51 0.9657673 0 0 0
#> 52 0.9657673 0 0 0
#> 53 0.9657673 0 0 0
#> 54 0.9657673 0 0 0
#> 55 0.9657673 0 0 0
#> 56 0.9657673 0 0 0
#> 57 0.9657673 0 0 0
#> 58 0.9657673 0 0 0
#> 59 0.9657673 0 0 0
#> 60 0.9657673 0 0 0
#> 61 0.9657673 0 0 0
#> 62 0.9657673 0 0 0
#> 63 0.9657673 0 0 0
#> 64 0.9657673 0 0 0
#> 65 0.9657673 0 0 0
#> 66 0.9657673 0 0 0
#> 67 0.9657673 0 0 0
#> 68 0.9657673 0 0 0
#> 69 0.9657673 0 0 0
#> 70 0.9657673 0 0 0
#> 71 0.9657673 0 0 0
#> 72 0.9657673 0 0 0
#> 73 0.9657673 0 0 0
#> 74 0.9657673 0 0 0
#> 75 0.9657673 0 0 0
#> dem60_by_fs_dem60 dem60_by_fs_dem65 dem65_by_fs_ind60 dem65_by_fs_dem60
#> 1 0.8868049 0 0 0
#> 2 0.8868049 0 0 0
#> 3 0.8868049 0 0 0
#> 4 0.8868049 0 0 0
#> 5 0.8868049 0 0 0
#> 6 0.8868049 0 0 0
#> 7 0.8868049 0 0 0
#> 8 0.8868049 0 0 0
#> 9 0.8868049 0 0 0
#> 10 0.8868049 0 0 0
#> 11 0.8868049 0 0 0
#> 12 0.8868049 0 0 0
#> 13 0.8868049 0 0 0
#> 14 0.8868049 0 0 0
#> 15 0.8868049 0 0 0
#> 16 0.8868049 0 0 0
#> 17 0.8868049 0 0 0
#> 18 0.8868049 0 0 0
#> 19 0.8868049 0 0 0
#> 20 0.8868049 0 0 0
#> 21 0.8868049 0 0 0
#> 22 0.8868049 0 0 0
#> 23 0.8868049 0 0 0
#> 24 0.8868049 0 0 0
#> 25 0.8868049 0 0 0
#> 26 0.8868049 0 0 0
#> 27 0.8868049 0 0 0
#> 28 0.8868049 0 0 0
#> 29 0.8868049 0 0 0
#> 30 0.8868049 0 0 0
#> 31 0.8868049 0 0 0
#> 32 0.8868049 0 0 0
#> 33 0.8868049 0 0 0
#> 34 0.8868049 0 0 0
#> 35 0.8868049 0 0 0
#> 36 0.8868049 0 0 0
#> 37 0.8868049 0 0 0
#> 38 0.8868049 0 0 0
#> 39 0.8868049 0 0 0
#> 40 0.8868049 0 0 0
#> 41 0.8868049 0 0 0
#> 42 0.8868049 0 0 0
#> 43 0.8868049 0 0 0
#> 44 0.8868049 0 0 0
#> 45 0.8868049 0 0 0
#> 46 0.8868049 0 0 0
#> 47 0.8868049 0 0 0
#> 48 0.8868049 0 0 0
#> 49 0.8868049 0 0 0
#> 50 0.8868049 0 0 0
#> 51 0.8868049 0 0 0
#> 52 0.8868049 0 0 0
#> 53 0.8868049 0 0 0
#> 54 0.8868049 0 0 0
#> 55 0.8868049 0 0 0
#> 56 0.8868049 0 0 0
#> 57 0.8868049 0 0 0
#> 58 0.8868049 0 0 0
#> 59 0.8868049 0 0 0
#> 60 0.8868049 0 0 0
#> 61 0.8868049 0 0 0
#> 62 0.8868049 0 0 0
#> 63 0.8868049 0 0 0
#> 64 0.8868049 0 0 0
#> 65 0.8868049 0 0 0
#> 66 0.8868049 0 0 0
#> 67 0.8868049 0 0 0
#> 68 0.8868049 0 0 0
#> 69 0.8868049 0 0 0
#> 70 0.8868049 0 0 0
#> 71 0.8868049 0 0 0
#> 72 0.8868049 0 0 0
#> 73 0.8868049 0 0 0
#> 74 0.8868049 0 0 0
#> 75 0.8868049 0 0 0
#> dem65_by_fs_dem65 ev_fs_ind60 ecov_fs_dem60_fs_ind60 ev_fs_dem60
#> 1 0.8998252 0.01472862 0 0.4564991
#> 2 0.8998252 0.01472862 0 0.4564991
#> 3 0.8998252 0.01472862 0 0.4564991
#> 4 0.8998252 0.01472862 0 0.4564991
#> 5 0.8998252 0.01472862 0 0.4564991
#> 6 0.8998252 0.01472862 0 0.4564991
#> 7 0.8998252 0.01472862 0 0.4564991
#> 8 0.8998252 0.01472862 0 0.4564991
#> 9 0.8998252 0.01472862 0 0.4564991
#> 10 0.8998252 0.01472862 0 0.4564991
#> 11 0.8998252 0.01472862 0 0.4564991
#> 12 0.8998252 0.01472862 0 0.4564991
#> 13 0.8998252 0.01472862 0 0.4564991
#> 14 0.8998252 0.01472862 0 0.4564991
#> 15 0.8998252 0.01472862 0 0.4564991
#> 16 0.8998252 0.01472862 0 0.4564991
#> 17 0.8998252 0.01472862 0 0.4564991
#> 18 0.8998252 0.01472862 0 0.4564991
#> 19 0.8998252 0.01472862 0 0.4564991
#> 20 0.8998252 0.01472862 0 0.4564991
#> 21 0.8998252 0.01472862 0 0.4564991
#> 22 0.8998252 0.01472862 0 0.4564991
#> 23 0.8998252 0.01472862 0 0.4564991
#> 24 0.8998252 0.01472862 0 0.4564991
#> 25 0.8998252 0.01472862 0 0.4564991
#> 26 0.8998252 0.01472862 0 0.4564991
#> 27 0.8998252 0.01472862 0 0.4564991
#> 28 0.8998252 0.01472862 0 0.4564991
#> 29 0.8998252 0.01472862 0 0.4564991
#> 30 0.8998252 0.01472862 0 0.4564991
#> 31 0.8998252 0.01472862 0 0.4564991
#> 32 0.8998252 0.01472862 0 0.4564991
#> 33 0.8998252 0.01472862 0 0.4564991
#> 34 0.8998252 0.01472862 0 0.4564991
#> 35 0.8998252 0.01472862 0 0.4564991
#> 36 0.8998252 0.01472862 0 0.4564991
#> 37 0.8998252 0.01472862 0 0.4564991
#> 38 0.8998252 0.01472862 0 0.4564991
#> 39 0.8998252 0.01472862 0 0.4564991
#> 40 0.8998252 0.01472862 0 0.4564991
#> 41 0.8998252 0.01472862 0 0.4564991
#> 42 0.8998252 0.01472862 0 0.4564991
#> 43 0.8998252 0.01472862 0 0.4564991
#> 44 0.8998252 0.01472862 0 0.4564991
#> 45 0.8998252 0.01472862 0 0.4564991
#> 46 0.8998252 0.01472862 0 0.4564991
#> 47 0.8998252 0.01472862 0 0.4564991
#> 48 0.8998252 0.01472862 0 0.4564991
#> 49 0.8998252 0.01472862 0 0.4564991
#> 50 0.8998252 0.01472862 0 0.4564991
#> 51 0.8998252 0.01472862 0 0.4564991
#> 52 0.8998252 0.01472862 0 0.4564991
#> 53 0.8998252 0.01472862 0 0.4564991
#> 54 0.8998252 0.01472862 0 0.4564991
#> 55 0.8998252 0.01472862 0 0.4564991
#> 56 0.8998252 0.01472862 0 0.4564991
#> 57 0.8998252 0.01472862 0 0.4564991
#> 58 0.8998252 0.01472862 0 0.4564991
#> 59 0.8998252 0.01472862 0 0.4564991
#> 60 0.8998252 0.01472862 0 0.4564991
#> 61 0.8998252 0.01472862 0 0.4564991
#> 62 0.8998252 0.01472862 0 0.4564991
#> 63 0.8998252 0.01472862 0 0.4564991
#> 64 0.8998252 0.01472862 0 0.4564991
#> 65 0.8998252 0.01472862 0 0.4564991
#> 66 0.8998252 0.01472862 0 0.4564991
#> 67 0.8998252 0.01472862 0 0.4564991
#> 68 0.8998252 0.01472862 0 0.4564991
#> 69 0.8998252 0.01472862 0 0.4564991
#> 70 0.8998252 0.01472862 0 0.4564991
#> 71 0.8998252 0.01472862 0 0.4564991
#> 72 0.8998252 0.01472862 0 0.4564991
#> 73 0.8998252 0.01472862 0 0.4564991
#> 74 0.8998252 0.01472862 0 0.4564991
#> 75 0.8998252 0.01472862 0 0.4564991
#> ecov_fs_dem65_fs_ind60 ecov_fs_dem65_fs_dem60 ev_fs_dem65
#> 1 0 0 0.3276882
#> 2 0 0 0.3276882
#> 3 0 0 0.3276882
#> 4 0 0 0.3276882
#> 5 0 0 0.3276882
#> 6 0 0 0.3276882
#> 7 0 0 0.3276882
#> 8 0 0 0.3276882
#> 9 0 0 0.3276882
#> 10 0 0 0.3276882
#> 11 0 0 0.3276882
#> 12 0 0 0.3276882
#> 13 0 0 0.3276882
#> 14 0 0 0.3276882
#> 15 0 0 0.3276882
#> 16 0 0 0.3276882
#> 17 0 0 0.3276882
#> 18 0 0 0.3276882
#> 19 0 0 0.3276882
#> 20 0 0 0.3276882
#> 21 0 0 0.3276882
#> 22 0 0 0.3276882
#> 23 0 0 0.3276882
#> 24 0 0 0.3276882
#> 25 0 0 0.3276882
#> 26 0 0 0.3276882
#> 27 0 0 0.3276882
#> 28 0 0 0.3276882
#> 29 0 0 0.3276882
#> 30 0 0 0.3276882
#> 31 0 0 0.3276882
#> 32 0 0 0.3276882
#> 33 0 0 0.3276882
#> 34 0 0 0.3276882
#> 35 0 0 0.3276882
#> 36 0 0 0.3276882
#> 37 0 0 0.3276882
#> 38 0 0 0.3276882
#> 39 0 0 0.3276882
#> 40 0 0 0.3276882
#> 41 0 0 0.3276882
#> 42 0 0 0.3276882
#> 43 0 0 0.3276882
#> 44 0 0 0.3276882
#> 45 0 0 0.3276882
#> 46 0 0 0.3276882
#> 47 0 0 0.3276882
#> 48 0 0 0.3276882
#> 49 0 0 0.3276882
#> 50 0 0 0.3276882
#> 51 0 0 0.3276882
#> 52 0 0 0.3276882
#> 53 0 0 0.3276882
#> 54 0 0 0.3276882
#> 55 0 0 0.3276882
#> 56 0 0 0.3276882
#> 57 0 0 0.3276882
#> 58 0 0 0.3276882
#> 59 0 0 0.3276882
#> 60 0 0 0.3276882
#> 61 0 0 0.3276882
#> 62 0 0 0.3276882
#> 63 0 0 0.3276882
#> 64 0 0 0.3276882
#> 65 0 0 0.3276882
#> 66 0 0 0.3276882
#> 67 0 0 0.3276882
#> 68 0 0 0.3276882
#> 69 0 0 0.3276882
#> 70 0 0 0.3276882
#> 71 0 0 0.3276882
#> 72 0 0 0.3276882
#> 73 0 0 0.3276882
#> 74 0 0 0.3276882
#> 75 0 0 0.3276882
# Vector form: one complete single-factor model string per latent, fit
# verbatim (e.g. a within-factor residual covariance the strict string
# grammar rejects)
get_fs(PoliticalDemocracy[c("x1", "x2", "x3", "y1", "y2", "y3", "y4")],
model = c("ind60 =~ x1 + x2 + x3",
"dem60 =~ y1 + y2 + y3 + y4
y1 ~~ y4"),
local = TRUE)
#> Warning: lavaan->lav_object_post_check():
#> the covariance matrix of the residuals of the observed variables (theta)
#> is not positive definite ; use lavInspect(fit, "theta") to investigate.
#> fs_ind60 fs_dem60 fs_ind60_se fs_dem60_se ind60_by_fs_ind60
#> 1 -0.52616832 -3.482363561 0.1213615 NA 0.9657673
#> 2 0.14365274 -4.226253608 0.1213615 NA 0.9657673
#> 3 0.71435592 3.043357833 0.1213615 NA 0.9657673
#> 4 1.23992565 3.876514686 0.1213615 NA 0.9657673
#> 5 0.83190803 3.681706567 0.1213615 NA 0.9657673
#> 6 0.21238453 2.617461300 0.1213615 NA 0.9657673
#> 7 0.11880855 2.617461300 0.1213615 NA 0.9657673
#> 8 0.11322703 -0.733925249 0.1213615 NA 0.9657673
#> 9 0.25617279 -1.906371531 0.1213615 NA 0.9657673
#> 10 0.37112496 4.606997293 0.1213615 NA 0.9657673
#> 11 0.67281395 2.193926473 0.1213615 NA 0.9657673
#> 12 0.56885577 2.617461300 0.1213615 NA 0.9657673
#> 13 1.31369791 2.193926473 0.1213615 NA 0.9657673
#> 14 0.22042629 1.667491994 0.1213615 NA 0.9657673
#> 15 0.57849228 4.221173427 0.1213615 NA 0.9657673
#> 16 0.37805983 2.052992030 0.1213615 NA 0.9657673
#> 17 0.05734046 -0.358098888 0.1213615 NA 0.9657673
#> 18 -0.01609202 -2.254446012 0.1213615 NA 0.9657673
#> 19 0.88923616 4.861883741 0.1213615 NA 0.9657673
#> 20 1.11445897 3.041000291 0.1213615 NA 0.9657673
#> 21 0.94657339 4.861883741 0.1213615 NA 0.9657673
#> 22 0.90122770 -3.802718655 0.1213615 NA 0.9657673
#> 23 0.58409450 -1.510555964 0.1213615 NA 0.9657673
#> 24 0.64089192 3.769919095 0.1213615 NA 0.9657673
#> 25 0.91021968 2.420942322 0.1213615 NA 0.9657673
#> 26 -0.89660969 0.369255631 0.1213615 NA 0.9657673
#> 27 -0.13195991 -1.458001288 0.1213615 NA 0.9657673
#> 28 -0.52968769 -2.091861670 0.1213615 NA 0.9657673
#> 29 -0.81799629 1.642330703 0.1213615 NA 0.9657673
#> 30 -1.27199371 0.684402047 0.1213615 NA 0.9657673
#> 31 -0.32096024 -1.272511149 0.1213615 NA 0.9657673
#> 32 -1.16780103 -3.356384626 0.1213615 NA 0.9657673
#> 33 -0.12295473 -0.022775870 0.1213615 NA 0.9657673
#> 34 -0.04285945 -0.869845651 0.1213615 NA 0.9657673
#> 35 -0.34323505 0.052334920 0.1213615 NA 0.9657673
#> 36 -0.60541633 0.688161013 0.1213615 NA 0.9657673
#> 37 0.17688718 -0.912540974 0.1213615 NA 0.9657673
#> 38 -0.55066055 1.163563921 0.1213615 NA 0.9657673
#> 39 -1.05988219 -0.871426961 0.1213615 NA 0.9657673
#> 40 -0.04138802 -2.169358240 0.1213615 NA 0.9657673
#> 41 -0.12611837 -2.619661501 0.1213615 NA 0.9657673
#> 42 -0.60322892 1.636501394 0.1213615 NA 0.9657673
#> 43 -0.11057176 0.007466061 0.1213615 NA 0.9657673
#> 44 -1.06423085 -1.795830233 0.1213615 NA 0.9657673
#> 45 -1.08354999 -1.724444561 0.1213615 NA 0.9657673
#> 46 -0.84009484 1.669122251 0.1213615 NA 0.9657673
#> 47 -1.14678213 -1.934090918 0.1213615 NA 0.9657673
#> 48 -0.57578976 2.273570206 0.1213615 NA 0.9657673
#> 49 0.07186692 -1.294362204 0.1213615 NA 0.9657673
#> 50 0.14682421 2.004363896 0.1213615 NA 0.9657673
#> 51 0.35871830 -1.934090918 0.1213615 NA 0.9657673
#> 52 -0.43403195 -2.933191503 0.1213615 NA 0.9657673
#> 53 0.44603111 -1.069288009 0.1213615 NA 0.9657673
#> 54 0.26352000 -0.745155896 0.1213615 NA 0.9657673
#> 55 0.55051165 -4.226253608 0.1213615 NA 0.9657673
#> 56 0.23453122 -3.963936887 0.1213615 NA 0.9657673
#> 57 0.27968138 -4.649788562 0.1213615 NA 0.9657673
#> 58 0.70960640 1.667491994 0.1213615 NA 0.9657673
#> 59 0.25227978 -1.283394346 0.1213615 NA 0.9657673
#> 60 1.18849297 3.374103646 0.1213615 NA 0.9657673
#> 61 0.21104946 -3.802718655 0.1213615 NA 0.9657673
#> 62 -1.16516281 -3.802718655 0.1213615 NA 0.9657673
#> 63 -0.85560065 -3.058828608 0.1213615 NA 0.9657673
#> 64 0.13398476 -0.182343617 0.1213615 NA 0.9657673
#> 65 -0.07912189 3.374103646 0.1213615 NA 0.9657673
#> 66 -0.27146711 -2.346117556 0.1213615 NA 0.9657673
#> 67 -0.04417217 3.374103646 0.1213615 NA 0.9657673
#> 68 -1.33425662 -3.166360868 0.1213615 NA 0.9657673
#> 69 -0.38720750 -2.612944604 0.1213615 NA 0.9657673
#> 70 -0.55355511 -2.091861670 0.1213615 NA 0.9657673
#> 71 -0.72242623 -1.395712591 0.1213615 NA 0.9657673
#> 72 0.30607449 1.189863436 0.1213615 NA 0.9657673
#> 73 0.77707950 1.868752533 0.1213615 NA 0.9657673
#> 74 0.06847481 5.257699189 0.1213615 NA 0.9657673
#> 75 -0.11052927 -2.710754128 0.1213615 NA 0.9657673
#> ind60_by_fs_dem60 dem60_by_fs_ind60 dem60_by_fs_dem60 ev_fs_ind60
#> 1 0 0 1.144843 0.01472862
#> 2 0 0 1.144843 0.01472862
#> 3 0 0 1.144843 0.01472862
#> 4 0 0 1.144843 0.01472862
#> 5 0 0 1.144843 0.01472862
#> 6 0 0 1.144843 0.01472862
#> 7 0 0 1.144843 0.01472862
#> 8 0 0 1.144843 0.01472862
#> 9 0 0 1.144843 0.01472862
#> 10 0 0 1.144843 0.01472862
#> 11 0 0 1.144843 0.01472862
#> 12 0 0 1.144843 0.01472862
#> 13 0 0 1.144843 0.01472862
#> 14 0 0 1.144843 0.01472862
#> 15 0 0 1.144843 0.01472862
#> 16 0 0 1.144843 0.01472862
#> 17 0 0 1.144843 0.01472862
#> 18 0 0 1.144843 0.01472862
#> 19 0 0 1.144843 0.01472862
#> 20 0 0 1.144843 0.01472862
#> 21 0 0 1.144843 0.01472862
#> 22 0 0 1.144843 0.01472862
#> 23 0 0 1.144843 0.01472862
#> 24 0 0 1.144843 0.01472862
#> 25 0 0 1.144843 0.01472862
#> 26 0 0 1.144843 0.01472862
#> 27 0 0 1.144843 0.01472862
#> 28 0 0 1.144843 0.01472862
#> 29 0 0 1.144843 0.01472862
#> 30 0 0 1.144843 0.01472862
#> 31 0 0 1.144843 0.01472862
#> 32 0 0 1.144843 0.01472862
#> 33 0 0 1.144843 0.01472862
#> 34 0 0 1.144843 0.01472862
#> 35 0 0 1.144843 0.01472862
#> 36 0 0 1.144843 0.01472862
#> 37 0 0 1.144843 0.01472862
#> 38 0 0 1.144843 0.01472862
#> 39 0 0 1.144843 0.01472862
#> 40 0 0 1.144843 0.01472862
#> 41 0 0 1.144843 0.01472862
#> 42 0 0 1.144843 0.01472862
#> 43 0 0 1.144843 0.01472862
#> 44 0 0 1.144843 0.01472862
#> 45 0 0 1.144843 0.01472862
#> 46 0 0 1.144843 0.01472862
#> 47 0 0 1.144843 0.01472862
#> 48 0 0 1.144843 0.01472862
#> 49 0 0 1.144843 0.01472862
#> 50 0 0 1.144843 0.01472862
#> 51 0 0 1.144843 0.01472862
#> 52 0 0 1.144843 0.01472862
#> 53 0 0 1.144843 0.01472862
#> 54 0 0 1.144843 0.01472862
#> 55 0 0 1.144843 0.01472862
#> 56 0 0 1.144843 0.01472862
#> 57 0 0 1.144843 0.01472862
#> 58 0 0 1.144843 0.01472862
#> 59 0 0 1.144843 0.01472862
#> 60 0 0 1.144843 0.01472862
#> 61 0 0 1.144843 0.01472862
#> 62 0 0 1.144843 0.01472862
#> 63 0 0 1.144843 0.01472862
#> 64 0 0 1.144843 0.01472862
#> 65 0 0 1.144843 0.01472862
#> 66 0 0 1.144843 0.01472862
#> 67 0 0 1.144843 0.01472862
#> 68 0 0 1.144843 0.01472862
#> 69 0 0 1.144843 0.01472862
#> 70 0 0 1.144843 0.01472862
#> 71 0 0 1.144843 0.01472862
#> 72 0 0 1.144843 0.01472862
#> 73 0 0 1.144843 0.01472862
#> 74 0 0 1.144843 0.01472862
#> 75 0 0 1.144843 0.01472862
#> ecov_fs_dem60_fs_ind60 ev_fs_dem60
#> 1 0 -1.016191
#> 2 0 -1.016191
#> 3 0 -1.016191
#> 4 0 -1.016191
#> 5 0 -1.016191
#> 6 0 -1.016191
#> 7 0 -1.016191
#> 8 0 -1.016191
#> 9 0 -1.016191
#> 10 0 -1.016191
#> 11 0 -1.016191
#> 12 0 -1.016191
#> 13 0 -1.016191
#> 14 0 -1.016191
#> 15 0 -1.016191
#> 16 0 -1.016191
#> 17 0 -1.016191
#> 18 0 -1.016191
#> 19 0 -1.016191
#> 20 0 -1.016191
#> 21 0 -1.016191
#> 22 0 -1.016191
#> 23 0 -1.016191
#> 24 0 -1.016191
#> 25 0 -1.016191
#> 26 0 -1.016191
#> 27 0 -1.016191
#> 28 0 -1.016191
#> 29 0 -1.016191
#> 30 0 -1.016191
#> 31 0 -1.016191
#> 32 0 -1.016191
#> 33 0 -1.016191
#> 34 0 -1.016191
#> 35 0 -1.016191
#> 36 0 -1.016191
#> 37 0 -1.016191
#> 38 0 -1.016191
#> 39 0 -1.016191
#> 40 0 -1.016191
#> 41 0 -1.016191
#> 42 0 -1.016191
#> 43 0 -1.016191
#> 44 0 -1.016191
#> 45 0 -1.016191
#> 46 0 -1.016191
#> 47 0 -1.016191
#> 48 0 -1.016191
#> 49 0 -1.016191
#> 50 0 -1.016191
#> 51 0 -1.016191
#> 52 0 -1.016191
#> 53 0 -1.016191
#> 54 0 -1.016191
#> 55 0 -1.016191
#> 56 0 -1.016191
#> 57 0 -1.016191
#> 58 0 -1.016191
#> 59 0 -1.016191
#> 60 0 -1.016191
#> 61 0 -1.016191
#> 62 0 -1.016191
#> 63 0 -1.016191
#> 64 0 -1.016191
#> 65 0 -1.016191
#> 66 0 -1.016191
#> 67 0 -1.016191
#> 68 0 -1.016191
#> 69 0 -1.016191
#> 70 0 -1.016191
#> 71 0 -1.016191
#> 72 0 -1.016191
#> 73 0 -1.016191
#> 74 0 -1.016191
#> 75 0 -1.016191
# Multiple-group
hs_model <- ' visual =~ x1 + x2 + x3 '
fit <- cfa(hs_model,
data = HolzingerSwineford1939,
group = "school")
get_fs(HolzingerSwineford1939, hs_model, group = "school")
#> fs_visual fs_visual_se visual_by_fs_visual ev_fs_visual school
#> 1 -0.821165191 0.3391326 0.6734826 0.11501089 Pasteur
#> 2 -0.124009418 0.3391326 0.6734826 0.11501089 Pasteur
#> 3 -0.370072089 0.3391326 0.6734826 0.11501089 Pasteur
#> 4 0.440928618 0.3391326 0.6734826 0.11501089 Pasteur
#> 5 -0.691389016 0.3391326 0.6734826 0.11501089 Pasteur
#> 6 -0.110032619 0.3391326 0.6734826 0.11501089 Pasteur
#> 7 -0.904127845 0.3391326 0.6734826 0.11501089 Pasteur
#> 8 -0.031747573 0.3391326 0.6734826 0.11501089 Pasteur
#> 9 -0.439478981 0.3391326 0.6734826 0.11501089 Pasteur
#> 10 -0.938939050 0.3391326 0.6734826 0.11501089 Pasteur
#> 11 -0.436821880 0.3391326 0.6734826 0.11501089 Pasteur
#> 12 0.305033497 0.3391326 0.6734826 0.11501089 Pasteur
#> 13 0.522076263 0.3391326 0.6734826 0.11501089 Pasteur
#> 14 -0.090367931 0.3391326 0.6734826 0.11501089 Pasteur
#> 15 0.526276771 0.3391326 0.6734826 0.11501089 Pasteur
#> 16 -0.226580678 0.3391326 0.6734826 0.11501089 Pasteur
#> 17 -0.582016192 0.3391326 0.6734826 0.11501089 Pasteur
#> 18 0.017040431 0.3391326 0.6734826 0.11501089 Pasteur
#> 19 0.563052459 0.3391326 0.6734826 0.11501089 Pasteur
#> 20 0.746621910 0.3391326 0.6734826 0.11501089 Pasteur
#> 21 0.234672405 0.3391326 0.6734826 0.11501089 Pasteur
#> 22 1.157487518 0.3391326 0.6734826 0.11501089 Pasteur
#> 23 -0.162272449 0.3391326 0.6734826 0.11501089 Pasteur
#> 24 -0.556027059 0.3391326 0.6734826 0.11501089 Pasteur
#> 25 -0.321443540 0.3391326 0.6734826 0.11501089 Pasteur
#> 26 0.153141050 0.3391326 0.6734826 0.11501089 Pasteur
#> 27 0.696234416 0.3391326 0.6734826 0.11501089 Pasteur
#> 28 -0.020961039 0.3391326 0.6734826 0.11501089 Pasteur
#> 29 0.532601236 0.3391326 0.6734826 0.11501089 Pasteur
#> 30 -0.727687585 0.3391326 0.6734826 0.11501089 Pasteur
#> 31 -0.676719580 0.3391326 0.6734826 0.11501089 Pasteur
#> 32 -1.120216393 0.3391326 0.6734826 0.11501089 Pasteur
#> 33 -0.313631732 0.3391326 0.6734826 0.11501089 Pasteur
#> 34 -0.187091845 0.3391326 0.6734826 0.11501089 Pasteur
#> 35 -0.887709484 0.3391326 0.6734826 0.11501089 Pasteur
#> 36 -0.760795908 0.3391326 0.6734826 0.11501089 Pasteur
#> 37 0.556943532 0.3391326 0.6734826 0.11501089 Pasteur
#> 38 -0.458666570 0.3391326 0.6734826 0.11501089 Pasteur
#> 39 0.514741536 0.3391326 0.6734826 0.11501089 Pasteur
#> 40 0.373009089 0.3391326 0.6734826 0.11501089 Pasteur
#> 41 -0.528550562 0.3391326 0.6734826 0.11501089 Pasteur
#> 42 -0.865864795 0.3391326 0.6734826 0.11501089 Pasteur
#> 43 -1.182344640 0.3391326 0.6734826 0.11501089 Pasteur
#> 44 -0.435334517 0.3391326 0.6734826 0.11501089 Pasteur
#> 45 0.306520860 0.3391326 0.6734826 0.11501089 Pasteur
#> 46 0.821604565 0.3391326 0.6734826 0.11501089 Pasteur
#> 47 1.213927875 0.3391326 0.6734826 0.11501089 Pasteur
#> 48 -0.851887996 0.3391326 0.6734826 0.11501089 Pasteur
#> 49 -0.085053749 0.3391326 0.6734826 0.11501089 Pasteur
#> 50 -0.508885873 0.3391326 0.6734826 0.11501089 Pasteur
#> 51 0.502467638 0.3391326 0.6734826 0.11501089 Pasteur
#> 52 0.284732253 0.3391326 0.6734826 0.11501089 Pasteur
#> 53 0.202677755 0.3391326 0.6734826 0.11501089 Pasteur
#> 54 -0.335953502 0.3391326 0.6734826 0.11501089 Pasteur
#> 55 0.556410369 0.3391326 0.6734826 0.11501089 Pasteur
#> 56 -0.058746970 0.3391326 0.6734826 0.11501089 Pasteur
#> 57 -0.066932487 0.3391326 0.6734826 0.11501089 Pasteur
#> 58 0.554230368 0.3391326 0.6734826 0.11501089 Pasteur
#> 59 -0.321761185 0.3391326 0.6734826 0.11501089 Pasteur
#> 60 -0.421834819 0.3391326 0.6734826 0.11501089 Pasteur
#> 61 0.345476529 0.3391326 0.6734826 0.11501089 Pasteur
#> 62 0.194809883 0.3391326 0.6734826 0.11501089 Pasteur
#> 63 -0.207870208 0.3391326 0.6734826 0.11501089 Pasteur
#> 64 -0.441658981 0.3391326 0.6734826 0.11501089 Pasteur
#> 65 0.102070958 0.3391326 0.6734826 0.11501089 Pasteur
#> 66 0.311198487 0.3391326 0.6734826 0.11501089 Pasteur
#> 67 0.676364229 0.3391326 0.6734826 0.11501089 Pasteur
#> 68 0.297858262 0.3391326 0.6734826 0.11501089 Pasteur
#> 69 -1.055487128 0.3391326 0.6734826 0.11501089 Pasteur
#> 70 -0.737997019 0.3391326 0.6734826 0.11501089 Pasteur
#> 71 -1.576099236 0.3391326 0.6734826 0.11501089 Pasteur
#> 72 0.534360181 0.3391326 0.6734826 0.11501089 Pasteur
#> 73 -0.105888156 0.3391326 0.6734826 0.11501089 Pasteur
#> 74 0.266237302 0.3391326 0.6734826 0.11501089 Pasteur
#> 75 -0.352427927 0.3391326 0.6734826 0.11501089 Pasteur
#> 76 -0.334783784 0.3391326 0.6734826 0.11501089 Pasteur
#> 77 0.133588508 0.3391326 0.6734826 0.11501089 Pasteur
#> 78 -1.035662965 0.3391326 0.6734826 0.11501089 Pasteur
#> 79 0.762507108 0.3391326 0.6734826 0.11501089 Pasteur
#> 80 -0.260699265 0.3391326 0.6734826 0.11501089 Pasteur
#> 81 -0.329095893 0.3391326 0.6734826 0.11501089 Pasteur
#> 82 0.752413211 0.3391326 0.6734826 0.11501089 Pasteur
#> 83 0.149268188 0.3391326 0.6734826 0.11501089 Pasteur
#> 84 -0.208880471 0.3391326 0.6734826 0.11501089 Pasteur
#> 85 -1.078285998 0.3391326 0.6734826 0.11501089 Pasteur
#> 86 0.306043760 0.3391326 0.6734826 0.11501089 Pasteur
#> 87 0.349677056 0.3391326 0.6734826 0.11501089 Pasteur
#> 88 0.165686549 0.3391326 0.6734826 0.11501089 Pasteur
#> 89 0.077307606 0.3391326 0.6734826 0.11501089 Pasteur
#> 90 -0.077401396 0.3391326 0.6734826 0.11501089 Pasteur
#> 91 -0.081863485 0.3391326 0.6734826 0.11501089 Pasteur
#> 92 0.106748566 0.3391326 0.6734826 0.11501089 Pasteur
#> 93 -0.211593616 0.3391326 0.6734826 0.11501089 Pasteur
#> 94 -0.926665153 0.3391326 0.6734826 0.11501089 Pasteur
#> 95 -0.739484382 0.3391326 0.6734826 0.11501089 Pasteur
#> 96 0.570387167 0.3391326 0.6734826 0.11501089 Pasteur
#> 97 -0.913642554 0.3391326 0.6734826 0.11501089 Pasteur
#> 98 0.547484887 0.3391326 0.6734826 0.11501089 Pasteur
#> 99 -0.602850599 0.3391326 0.6734826 0.11501089 Pasteur
#> 100 0.225794270 0.3391326 0.6734826 0.11501089 Pasteur
#> 101 0.620447015 0.3391326 0.6734826 0.11501089 Pasteur
#> 102 0.158885005 0.3391326 0.6734826 0.11501089 Pasteur
#> 103 -0.127938344 0.3391326 0.6734826 0.11501089 Pasteur
#> 104 -0.420347455 0.3391326 0.6734826 0.11501089 Pasteur
#> 105 1.327978307 0.3391326 0.6734826 0.11501089 Pasteur
#> 106 0.181843348 0.3391326 0.6734826 0.11501089 Pasteur
#> 107 -0.148932224 0.3391326 0.6734826 0.11501089 Pasteur
#> 108 0.612373626 0.3391326 0.6734826 0.11501089 Pasteur
#> 109 -0.066558798 0.3391326 0.6734826 0.11501089 Pasteur
#> 110 -0.420880619 0.3391326 0.6734826 0.11501089 Pasteur
#> 111 1.127036295 0.3391326 0.6734826 0.11501089 Pasteur
#> 112 0.237591068 0.3391326 0.6734826 0.11501089 Pasteur
#> 113 0.853758689 0.3391326 0.6734826 0.11501089 Pasteur
#> 114 -0.143618023 0.3391326 0.6734826 0.11501089 Pasteur
#> 115 0.475206679 0.3391326 0.6734826 0.11501089 Pasteur
#> 116 -0.670554590 0.3391326 0.6734826 0.11501089 Pasteur
#> 117 0.022672257 0.3391326 0.6734826 0.11501089 Pasteur
#> 118 0.302002707 0.3391326 0.6734826 0.11501089 Pasteur
#> 119 0.151392125 0.3391326 0.6734826 0.11501089 Pasteur
#> 120 -0.475300449 0.3391326 0.6734826 0.11501089 Pasteur
#> 121 -0.346740056 0.3391326 0.6734826 0.11501089 Pasteur
#> 122 -0.078888759 0.3391326 0.6734826 0.11501089 Pasteur
#> 123 1.197237913 0.3391326 0.6734826 0.11501089 Pasteur
#> 124 0.539243306 0.3391326 0.6734826 0.11501089 Pasteur
#> 125 0.867258388 0.3391326 0.6734826 0.11501089 Pasteur
#> 126 0.592287901 0.3391326 0.6734826 0.11501089 Pasteur
#> 127 -0.500540901 0.3391326 0.6734826 0.11501089 Pasteur
#> 128 -0.361193954 0.3391326 0.6734826 0.11501089 Pasteur
#> 129 0.626883588 0.3391326 0.6734826 0.11501089 Pasteur
#> 130 -0.437514518 0.3391326 0.6734826 0.11501089 Pasteur
#> 131 0.695972854 0.3391326 0.6734826 0.11501089 Pasteur
#> 132 0.424715775 0.3391326 0.6734826 0.11501089 Pasteur
#> 133 -0.203725744 0.3391326 0.6734826 0.11501089 Pasteur
#> 134 -0.441499507 0.3391326 0.6734826 0.11501089 Pasteur
#> 135 0.735619838 0.3391326 0.6734826 0.11501089 Pasteur
#> 136 0.783874697 0.3391326 0.6734826 0.11501089 Pasteur
#> 137 0.565709540 0.3391326 0.6734826 0.11501089 Pasteur
#> 138 0.258425494 0.3391326 0.6734826 0.11501089 Pasteur
#> 139 0.861093397 0.3391326 0.6734826 0.11501089 Pasteur
#> 140 -0.059757233 0.3391326 0.6734826 0.11501089 Pasteur
#> 141 -0.920340689 0.3391326 0.6734826 0.11501089 Pasteur
#> 142 0.845629236 0.3391326 0.6734826 0.11501089 Pasteur
#> 143 1.227427574 0.3391326 0.6734826 0.11501089 Pasteur
#> 144 1.054223601 0.3391326 0.6734826 0.11501089 Pasteur
#> 145 -1.246596805 0.3391326 0.6734826 0.11501089 Pasteur
#> 146 -0.473120468 0.3391326 0.6734826 0.11501089 Pasteur
#> 147 -0.560171503 0.3391326 0.6734826 0.11501089 Pasteur
#> 148 -0.365394462 0.3391326 0.6734826 0.11501089 Pasteur
#> 149 0.084744422 0.3391326 0.6734826 0.11501089 Pasteur
#> 150 0.910676146 0.3391326 0.6734826 0.11501089 Pasteur
#> 151 1.094189533 0.3391326 0.6734826 0.11501089 Pasteur
#> 152 -0.013149231 0.3391326 0.6734826 0.11501089 Pasteur
#> 153 -0.166472976 0.3391326 0.6734826 0.11501089 Pasteur
#> 154 0.008695459 0.3391326 0.6734826 0.11501089 Pasteur
#> 155 -0.094989494 0.3391326 0.6734826 0.11501089 Pasteur
#> 156 -0.457123143 0.3391326 0.6734826 0.11501089 Pasteur
#> 157 -0.915287109 0.3118280 0.6990509 0.09723667 Grant-White
#> 158 0.035963597 0.3118280 0.6990509 0.09723667 Grant-White
#> 159 0.355636604 0.3118280 0.6990509 0.09723667 Grant-White
#> 160 -0.387353871 0.3118280 0.6990509 0.09723667 Grant-White
#> 161 -0.622393942 0.3118280 0.6990509 0.09723667 Grant-White
#> 162 0.195944561 0.3118280 0.6990509 0.09723667 Grant-White
#> 163 1.353023831 0.3118280 0.6990509 0.09723667 Grant-White
#> 164 -0.341506254 0.3118280 0.6990509 0.09723667 Grant-White
#> 165 -0.199493575 0.3118280 0.6990509 0.09723667 Grant-White
#> 166 -0.689869149 0.3118280 0.6990509 0.09723667 Grant-White
#> 167 -0.463929554 0.3118280 0.6990509 0.09723667 Grant-White
#> 168 -0.423001505 0.3118280 0.6990509 0.09723667 Grant-White
#> 169 0.279743296 0.3118280 0.6990509 0.09723667 Grant-White
#> 170 -0.916908219 0.3118280 0.6990509 0.09723667 Grant-White
#> 171 0.589344501 0.3118280 0.6990509 0.09723667 Grant-White
#> 172 0.191474701 0.3118280 0.6990509 0.09723667 Grant-White
#> 173 0.935275715 0.3118280 0.6990509 0.09723667 Grant-White
#> 174 0.393715904 0.3118280 0.6990509 0.09723667 Grant-White
#> 175 0.086569994 0.3118280 0.6990509 0.09723667 Grant-White
#> 176 0.555606898 0.3118280 0.6990509 0.09723667 Grant-White
#> 177 -0.558217193 0.3118280 0.6990509 0.09723667 Grant-White
#> 178 0.766715894 0.3118280 0.6990509 0.09723667 Grant-White
#> 179 0.115548801 0.3118280 0.6990509 0.09723667 Grant-White
#> 180 0.901249191 0.3118280 0.6990509 0.09723667 Grant-White
#> 181 0.174316971 0.3118280 0.6990509 0.09723667 Grant-White
#> 182 -0.078980322 0.3118280 0.6990509 0.09723667 Grant-White
#> 183 -0.581882977 0.3118280 0.6990509 0.09723667 Grant-White
#> 184 -0.661179262 0.3118280 0.6990509 0.09723667 Grant-White
#> 185 -0.245341176 0.3118280 0.6990509 0.09723667 Grant-White
#> 186 -0.195801662 0.3118280 0.6990509 0.09723667 Grant-White
#> 187 -0.281221524 0.3118280 0.6990509 0.09723667 Grant-White
#> 188 -0.293909378 0.3118280 0.6990509 0.09723667 Grant-White
#> 189 -0.604192958 0.3118280 0.6990509 0.09723667 Grant-White
#> 190 -0.738437335 0.3118280 0.6990509 0.09723667 Grant-White
#> 191 -0.304109345 0.3118280 0.6990509 0.09723667 Grant-White
#> 192 0.104931733 0.3118280 0.6990509 0.09723667 Grant-White
#> 193 -0.025781487 0.3118280 0.6990509 0.09723667 Grant-White
#> 194 -0.897318824 0.3118280 0.6990509 0.09723667 Grant-White
#> 195 -0.892560027 0.3118280 0.6990509 0.09723667 Grant-White
#> 196 -0.078402465 0.3118280 0.6990509 0.09723667 Grant-White
#> 197 -0.379063934 0.3118280 0.6990509 0.09723667 Grant-White
#> 198 -0.324926380 0.3118280 0.6990509 0.09723667 Grant-White
#> 199 -0.684299797 0.3118280 0.6990509 0.09723667 Grant-White
#> 200 -0.304109345 0.3118280 0.6990509 0.09723667 Grant-White
#> 201 0.622793169 0.3118280 0.6990509 0.09723667 Grant-White
#> 202 -0.152835419 0.3118280 0.6990509 0.09723667 Grant-White
#> 203 -0.421902013 0.3118280 0.6990509 0.09723667 Grant-White
#> 204 -0.060883872 0.3118280 0.6990509 0.09723667 Grant-White
#> 205 -0.303298790 0.3118280 0.6990509 0.09723667 Grant-White
#> 206 0.425021826 0.3118280 0.6990509 0.09723667 Grant-White
#> 207 0.131478875 0.3118280 0.6990509 0.09723667 Grant-White
#> 208 -0.914998172 0.3118280 0.6990509 0.09723667 Grant-White
#> 209 0.324226132 0.3118280 0.6990509 0.09723667 Grant-White
#> 210 -0.086170767 0.3118280 0.6990509 0.09723667 Grant-White
#> 211 0.428424818 0.3118280 0.6990509 0.09723667 Grant-White
#> 212 0.188465179 0.3118280 0.6990509 0.09723667 Grant-White
#> 213 -0.306958111 0.3118280 0.6990509 0.09723667 Grant-White
#> 214 0.581736955 0.3118280 0.6990509 0.09723667 Grant-White
#> 215 -0.743485051 0.3118280 0.6990509 0.09723667 Grant-White
#> 216 0.263580524 0.3118280 0.6990509 0.09723667 Grant-White
#> 217 0.178786831 0.3118280 0.6990509 0.09723667 Grant-White
#> 218 -0.064832113 0.3118280 0.6990509 0.09723667 Grant-White
#> 219 0.499976414 0.3118280 0.6990509 0.09723667 Grant-White
#> 220 -0.092839593 0.3118280 0.6990509 0.09723667 Grant-White
#> 221 -0.263702931 0.3118280 0.6990509 0.09723667 Grant-White
#> 222 -0.983966325 0.3118280 0.6990509 0.09723667 Grant-White
#> 223 1.434912536 0.3118280 0.6990509 0.09723667 Grant-White
#> 224 -1.037582228 0.3118280 0.6990509 0.09723667 Grant-White
#> 225 -0.047569849 0.3118280 0.6990509 0.09723667 Grant-White
#> 226 1.084767775 0.3118280 0.6990509 0.09723667 Grant-White
#> 227 0.092011181 0.3118280 0.6990509 0.09723667 Grant-White
#> 228 -0.562687052 0.3118280 0.6990509 0.09723667 Grant-White
#> 229 -0.304919900 0.3118280 0.6990509 0.09723667 Grant-White
#> 230 1.038920175 0.3118280 0.6990509 0.09723667 Grant-White
#> 231 -0.789854287 0.3118280 0.6990509 0.09723667 Grant-White
#> 232 -0.602282928 0.3118280 0.6990509 0.09723667 Grant-White
#> 233 -0.894630830 0.3118280 0.6990509 0.09723667 Grant-White
#> 234 0.532614527 0.3118280 0.6990509 0.09723667 Grant-White
#> 235 -0.548955945 0.3118280 0.6990509 0.09723667 Grant-White
#> 236 -0.221514635 0.3118280 0.6990509 0.09723667 Grant-White
#> 237 -0.095849115 0.3118280 0.6990509 0.09723667 Grant-White
#> 238 -0.122235502 0.3118280 0.6990509 0.09723667 Grant-White
#> 239 0.892276864 0.3118280 0.6990509 0.09723667 Grant-White
#> 240 0.328439663 0.3118280 0.6990509 0.09723667 Grant-White
#> 241 1.217391042 0.3118280 0.6990509 0.09723667 Grant-White
#> 242 0.574513902 0.3118280 0.6990509 0.09723667 Grant-White
#> 243 0.160168762 0.3118280 0.6990509 0.09723667 Grant-White
#> 244 0.654909662 0.3118280 0.6990509 0.09723667 Grant-White
#> 245 -0.509777155 0.3118280 0.6990509 0.09723667 Grant-White
#> 246 1.201493560 0.3118280 0.6990509 0.09723667 Grant-White
#> 247 0.584874625 0.3118280 0.6990509 0.09723667 Grant-White
#> 248 0.075142371 0.3118280 0.6990509 0.09723667 Grant-White
#> 249 0.550976266 0.3118280 0.6990509 0.09723667 Grant-White
#> 250 -0.886308302 0.3118280 0.6990509 0.09723667 Grant-White
#> 251 0.552075757 0.3118280 0.6990509 0.09723667 Grant-White
#> 252 1.415972940 0.3118280 0.6990509 0.09723667 Grant-White
#> 253 0.298650301 0.3118280 0.6990509 0.09723667 Grant-White
#> 254 -0.143028906 0.3118280 0.6990509 0.09723667 Grant-White
#> 255 0.245195154 0.3118280 0.6990509 0.09723667 Grant-White
#> 256 0.247072593 0.3118280 0.6990509 0.09723667 Grant-White
#> 257 0.817322291 0.3118280 0.6990509 0.09723667 Grant-White
#> 258 0.651771976 0.3118280 0.6990509 0.09723667 Grant-White
#> 259 1.338875623 0.3118280 0.6990509 0.09723667 Grant-White
#> 260 -1.160005528 0.3118280 0.6990509 0.09723667 Grant-White
#> 261 0.163306449 0.3118280 0.6990509 0.09723667 Grant-White
#> 262 -0.387353871 0.3118280 0.6990509 0.09723667 Grant-White
#> 263 -0.517128372 0.3118280 0.6990509 0.09723667 Grant-White
#> 264 0.065103160 0.3118280 0.6990509 0.09723667 Grant-White
#> 265 -0.115438510 0.3118280 0.6990509 0.09723667 Grant-White
#> 266 0.094049376 0.3118280 0.6990509 0.09723667 Grant-White
#> 267 0.396725409 0.3118280 0.6990509 0.09723667 Grant-White
#> 268 0.672356312 0.3118280 0.6990509 0.09723667 Grant-White
#> 269 1.165974090 0.3118280 0.6990509 0.09723667 Grant-White
#> 270 -0.483518949 0.3118280 0.6990509 0.09723667 Grant-White
#> 271 0.035024877 0.3118280 0.6990509 0.09723667 Grant-White
#> 272 0.741974248 0.3118280 0.6990509 0.09723667 Grant-White
#> 273 -0.170386603 0.3118280 0.6990509 0.09723667 Grant-White
#> 274 -0.205873481 0.3118280 0.6990509 0.09723667 Grant-White
#> 275 0.714777307 0.3118280 0.6990509 0.09723667 Grant-White
#> 276 -0.620772831 0.3118280 0.6990509 0.09723667 Grant-White
#> 277 -0.313626938 0.3118280 0.6990509 0.09723667 Grant-White
#> 278 -0.157466035 0.3118280 0.6990509 0.09723667 Grant-White
#> 279 0.118269386 0.3118280 0.6990509 0.09723667 Grant-White
#> 280 0.101111673 0.3118280 0.6990509 0.09723667 Grant-White
#> 281 -0.625403463 0.3118280 0.6990509 0.09723667 Grant-White
#> 282 0.486638761 0.3118280 0.6990509 0.09723667 Grant-White
#> 283 -0.178676540 0.3118280 0.6990509 0.09723667 Grant-White
#> 284 0.274013189 0.3118280 0.6990509 0.09723667 Grant-White
#> 285 -0.316347523 0.3118280 0.6990509 0.09723667 Grant-White
#> 286 -0.026752814 0.3118280 0.6990509 0.09723667 Grant-White
#> 287 0.245323318 0.3118280 0.6990509 0.09723667 Grant-White
#> 288 -0.356336853 0.3118280 0.6990509 0.09723667 Grant-White
#> 289 -0.581594057 0.3118280 0.6990509 0.09723667 Grant-White
#> 290 0.263002667 0.3118280 0.6990509 0.09723667 Grant-White
#> 291 -0.864680712 0.3118280 0.6990509 0.09723667 Grant-White
#> 292 -0.377964443 0.3118280 0.6990509 0.09723667 Grant-White
#> 293 -0.112717909 0.3118280 0.6990509 0.09723667 Grant-White
#> 294 0.114449326 0.3118280 0.6990509 0.09723667 Grant-White
#> 295 0.001287274 0.3118280 0.6990509 0.09723667 Grant-White
#> 296 0.597634438 0.3118280 0.6990509 0.09723667 Grant-White
#> 297 -0.252531637 0.3118280 0.6990509 0.09723667 Grant-White
#> 298 -0.472901881 0.3118280 0.6990509 0.09723667 Grant-White
#> 299 -0.187255397 0.3118280 0.6990509 0.09723667 Grant-White
#> 300 -0.542415283 0.3118280 0.6990509 0.09723667 Grant-White
#> 301 0.358774274 0.3118280 0.6990509 0.09723667 Grant-White
# Or without the model
get_fs(HolzingerSwineford1939[c("school", "x4", "x5", "x6")],
group = "school")
#> fs_f1 fs_f1_se f1_by_fs_f1 ev_fs_f1 school
#> 1 0.3074500370 0.2999315 0.8833584 0.08995892 Pasteur
#> 2 -0.7746062892 0.2999315 0.8833584 0.08995892 Pasteur
#> 3 -1.5843019574 0.2999315 0.8833584 0.08995892 Pasteur
#> 4 0.2739579120 0.2999315 0.8833584 0.08995892 Pasteur
#> 5 0.1440153923 0.2999315 0.8833584 0.08995892 Pasteur
#> 6 -1.0440895948 0.2999315 0.8833584 0.08995892 Pasteur
#> 7 1.0507357396 0.2999315 0.8833584 0.08995892 Pasteur
#> 8 0.1041882698 0.2999315 0.8833584 0.08995892 Pasteur
#> 9 0.7750146375 0.2999315 0.8833584 0.08995892 Pasteur
#> 10 0.4822117444 0.2999315 0.8833584 0.08995892 Pasteur
#> 11 -0.4511886490 0.2999315 0.8833584 0.08995892 Pasteur
#> 12 0.3522691973 0.2999315 0.8833584 0.08995892 Pasteur
#> 13 0.0657041070 0.2999315 0.8833584 0.08995892 Pasteur
#> 14 0.3259750264 0.2999315 0.8833584 0.08995892 Pasteur
#> 15 1.3008341323 0.2999315 0.8833584 0.08995892 Pasteur
#> 16 -0.2804588573 0.2999315 0.8833584 0.08995892 Pasteur
#> 17 -0.3604017581 0.2999315 0.8833584 0.08995892 Pasteur
#> 18 -0.7502293722 0.2999315 0.8833584 0.08995892 Pasteur
#> 19 1.2600468631 0.2999315 0.8833584 0.08995892 Pasteur
#> 20 0.2874908636 0.2999315 0.8833584 0.08995892 Pasteur
#> 21 -1.1394826729 0.2999315 0.8833584 0.08995892 Pasteur
#> 22 -0.3791151940 0.2999315 0.8833584 0.08995892 Pasteur
#> 23 1.0444979094 0.2999315 0.8833584 0.08995892 Pasteur
#> 24 -0.6248930150 0.2999315 0.8833584 0.08995892 Pasteur
#> 25 -0.3689426673 0.2999315 0.8833584 0.08995892 Pasteur
#> 26 -0.5663524842 0.2999315 0.8833584 0.08995892 Pasteur
#> 27 -0.9568515617 0.2999315 0.8833584 0.08995892 Pasteur
#> 28 -0.8137619455 0.2999315 0.8833584 0.08995892 Pasteur
#> 29 -0.5028199109 0.2999315 0.8833584 0.08995892 Pasteur
#> 30 -0.3054100713 0.2999315 0.8833584 0.08995892 Pasteur
#> 31 -0.3728773637 0.2999315 0.8833584 0.08995892 Pasteur
#> 32 -0.8529175744 0.2999315 0.8833584 0.08995892 Pasteur
#> 33 0.4101382620 0.2999315 0.8833584 0.08995892 Pasteur
#> 34 0.1848026386 0.2999315 0.8833584 0.08995892 Pasteur
#> 35 -0.9680814159 0.2999315 0.8833584 0.08995892 Pasteur
#> 36 -0.1436070439 0.2999315 0.8833584 0.08995892 Pasteur
#> 37 -0.0126071782 0.2999315 0.8833584 0.08995892 Pasteur
#> 38 -0.3531066368 0.2999315 0.8833584 0.08995892 Pasteur
#> 39 1.0665717701 0.2999315 0.8833584 0.08995892 Pasteur
#> 40 0.7993915544 0.2999315 0.8833584 0.08995892 Pasteur
#> 41 0.1110975387 0.2999315 0.8833584 0.08995892 Pasteur
#> 42 -0.2735495637 0.2999315 0.8833584 0.08995892 Pasteur
#> 43 -0.7167372327 0.2999315 0.8833584 0.08995892 Pasteur
#> 44 -0.6594424814 0.2999315 0.8833584 0.08995892 Pasteur
#> 45 0.9507364688 0.2999315 0.8833584 0.08995892 Pasteur
#> 46 -0.0478281107 0.2999315 0.8833584 0.08995892 Pasteur
#> 47 -1.7573348450 0.2999315 0.8833584 0.08995892 Pasteur
#> 48 -0.4620326417 0.2999315 0.8833584 0.08995892 Pasteur
#> 49 -1.0305566597 0.2999315 0.8833584 0.08995892 Pasteur
#> 50 0.7618675383 0.2999315 0.8833584 0.08995892 Pasteur
#> 51 -1.3414986833 0.2999315 0.8833584 0.08995892 Pasteur
#> 52 -0.0761397743 0.2999315 0.8833584 0.08995892 Pasteur
#> 53 -1.8231705136 0.2999315 0.8833584 0.08995892 Pasteur
#> 54 0.0094666323 0.2999315 0.8833584 0.08995892 Pasteur
#> 55 0.0436302463 0.2999315 0.8833584 0.08995892 Pasteur
#> 56 -0.0001315726 0.2999315 0.8833584 0.08995892 Pasteur
#> 57 -0.6571393750 0.2999315 0.8833584 0.08995892 Pasteur
#> 58 0.6121542642 0.2999315 0.8833584 0.08995892 Pasteur
#> 59 -1.0957208458 0.2999315 0.8833584 0.08995892 Pasteur
#> 60 -0.9289257761 0.2999315 0.8833584 0.08995892 Pasteur
#> 61 0.9553426588 0.2999315 0.8833584 0.08995892 Pasteur
#> 62 0.3647448029 0.2999315 0.8833584 0.08995892 Pasteur
#> 63 -1.1003270330 0.2999315 0.8833584 0.08995892 Pasteur
#> 64 0.4259742697 0.2999315 0.8833584 0.08995892 Pasteur
#> 65 0.1689666035 0.2999315 0.8833584 0.08995892 Pasteur
#> 66 0.6586050419 0.2999315 0.8833584 0.08995892 Pasteur
#> 67 -0.2952375720 0.2999315 0.8833584 0.08995892 Pasteur
#> 68 -0.4745082474 0.2999315 0.8833584 0.08995892 Pasteur
#> 69 -0.5613604418 0.2999315 0.8833584 0.08995892 Pasteur
#> 70 0.5632119383 0.2999315 0.8833584 0.08995892 Pasteur
#> 71 -0.7769093955 0.2999315 0.8833584 0.08995892 Pasteur
#> 72 -1.1434174113 0.2999315 0.8833584 0.08995892 Pasteur
#> 73 -0.7808440920 0.2999315 0.8833584 0.08995892 Pasteur
#> 74 0.9270309952 0.2999315 0.8833584 0.08995892 Pasteur
#> 75 -0.5426470306 0.2999315 0.8833584 0.08995892 Pasteur
#> 76 -1.1065648385 0.2999315 0.8833584 0.08995892 Pasteur
#> 77 0.6233841094 0.2999315 0.8833584 0.08995892 Pasteur
#> 78 -1.7010974136 0.2999315 0.8833584 0.08995892 Pasteur
#> 79 -0.0013773330 0.2999315 0.8833584 0.08995892 Pasteur
#> 80 -1.2772946275 0.2999315 0.8833584 0.08995892 Pasteur
#> 81 -1.0344913561 0.2999315 0.8833584 0.08995892 Pasteur
#> 82 0.4345151789 0.2999315 0.8833584 0.08995892 Pasteur
#> 83 -1.5280645151 0.2999315 0.8833584 0.08995892 Pasteur
#> 84 -0.6101143231 0.2999315 0.8833584 0.08995892 Pasteur
#> 85 -1.5122284832 0.2999315 0.8833584 0.08995892 Pasteur
#> 86 1.2011204798 0.2999315 0.8833584 0.08995892 Pasteur
#> 87 -0.3258523027 0.2999315 0.8833584 0.08995892 Pasteur
#> 88 0.6687775411 0.2999315 0.8833584 0.08995892 Pasteur
#> 89 -1.6382363147 0.2999315 0.8833584 0.08995892 Pasteur
#> 90 0.3062042720 0.2999315 0.8833584 0.08995892 Pasteur
#> 91 -0.4745082474 0.2999315 0.8833584 0.08995892 Pasteur
#> 92 -0.6798846937 0.2999315 0.8833584 0.08995892 Pasteur
#> 93 -1.1865077366 0.2999315 0.8833584 0.08995892 Pasteur
#> 94 -0.5011882981 0.2999315 0.8833584 0.08995892 Pasteur
#> 95 0.7362448336 0.2999315 0.8833584 0.08995892 Pasteur
#> 96 0.4934415622 0.2999315 0.8833584 0.08995892 Pasteur
#> 97 0.9661866515 0.2999315 0.8833584 0.08995892 Pasteur
#> 98 1.7212764661 0.2999315 0.8833584 0.08995892 Pasteur
#> 99 -0.8199997483 0.2999315 0.8833584 0.08995892 Pasteur
#> 100 0.7369163271 0.2999315 0.8833584 0.08995892 Pasteur
#> 101 -0.5403439270 0.2999315 0.8833584 0.08995892 Pasteur
#> 102 0.0525570079 0.2999315 0.8833584 0.08995892 Pasteur
#> 103 0.4973762860 0.2999315 0.8833584 0.08995892 Pasteur
#> 104 0.5434412113 0.2999315 0.8833584 0.08995892 Pasteur
#> 105 1.4015048920 0.2999315 0.8833584 0.08995892 Pasteur
#> 106 0.5338429790 0.2999315 0.8833584 0.08995892 Pasteur
#> 107 1.5005470281 0.2999315 0.8833584 0.08995892 Pasteur
#> 108 -0.4353526184 0.2999315 0.8833584 0.08995892 Pasteur
#> 109 1.7269399974 0.2999315 0.8833584 0.08995892 Pasteur
#> 110 -0.1863115671 0.2999315 0.8833584 0.08995892 Pasteur
#> 111 0.7431541299 0.2999315 0.8833584 0.08995892 Pasteur
#> 112 0.3345159128 0.2999315 0.8833584 0.08995892 Pasteur
#> 113 0.3111963144 0.2999315 0.8833584 0.08995892 Pasteur
#> 114 0.6750153713 0.2999315 0.8833584 0.08995892 Pasteur
#> 115 -1.3822859470 0.2999315 0.8833584 0.08995892 Pasteur
#> 116 0.4299090164 0.2999315 0.8833584 0.08995892 Pasteur
#> 117 0.4368182853 0.2999315 0.8833584 0.08995892 Pasteur
#> 118 -0.6334339242 0.2999315 0.8833584 0.08995892 Pasteur
#> 119 -0.9153928519 0.2999315 0.8833584 0.08995892 Pasteur
#> 120 -0.2662544424 0.2999315 0.8833584 0.08995892 Pasteur
#> 121 -0.1238362896 0.2999315 0.8833584 0.08995892 Pasteur
#> 122 -0.1987871727 0.2999315 0.8833584 0.08995892 Pasteur
#> 123 1.5676284956 0.2999315 0.8833584 0.08995892 Pasteur
#> 124 -0.2906313821 0.2999315 0.8833584 0.08995892 Pasteur
#> 125 0.7125393874 0.2999315 0.8833584 0.08995892 Pasteur
#> 126 0.1324998831 0.2999315 0.8833584 0.08995892 Pasteur
#> 127 1.1488177518 0.2999315 0.8833584 0.08995892 Pasteur
#> 128 0.5559168169 0.2999315 0.8833584 0.08995892 Pasteur
#> 129 0.8572606191 0.2999315 0.8833584 0.08995892 Pasteur
#> 130 -0.9789254060 0.2999315 0.8833584 0.08995892 Pasteur
#> 131 1.4416206448 0.2999315 0.8833584 0.08995892 Pasteur
#> 132 0.4542859333 0.2999315 0.8833584 0.08995892 Pasteur
#> 133 -1.3845890506 0.2999315 0.8833584 0.08995892 Pasteur
#> 134 -0.2883282757 0.2999315 0.8833584 0.08995892 Pasteur
#> 135 0.4430560881 0.2999315 0.8833584 0.08995892 Pasteur
#> 136 1.2089899229 0.2999315 0.8833584 0.08995892 Pasteur
#> 137 1.1942112109 0.2999315 0.8833584 0.08995892 Pasteur
#> 138 0.6013102486 0.2999315 0.8833584 0.08995892 Pasteur
#> 139 0.2371053667 0.2999315 0.8833584 0.08995892 Pasteur
#> 140 1.0053422804 0.2999315 0.8833584 0.08995892 Pasteur
#> 141 0.8095640810 0.2999315 0.8833584 0.08995892 Pasteur
#> 142 -1.4408264861 0.2999315 0.8833584 0.08995892 Pasteur
#> 143 1.3821199900 0.2999315 0.8833584 0.08995892 Pasteur
#> 144 2.7284791267 0.2999315 0.8833584 0.08995892 Pasteur
#> 145 0.0123440494 0.2999315 0.8833584 0.08995892 Pasteur
#> 146 0.8277032180 0.2999315 0.8833584 0.08995892 Pasteur
#> 147 0.7862444827 0.2999315 0.8833584 0.08995892 Pasteur
#> 148 -1.1325734026 0.2999315 0.8833584 0.08995892 Pasteur
#> 149 1.7660956264 0.2999315 0.8833584 0.08995892 Pasteur
#> 150 -0.3712457509 0.2999315 0.8833584 0.08995892 Pasteur
#> 151 1.8944065607 0.2999315 0.8833584 0.08995892 Pasteur
#> 152 0.6098511578 0.2999315 0.8833584 0.08995892 Pasteur
#> 153 0.2654170028 0.2999315 0.8833584 0.08995892 Pasteur
#> 154 -1.1434174113 0.2999315 0.8833584 0.08995892 Pasteur
#> 155 -0.6005160981 0.2999315 0.8833584 0.08995892 Pasteur
#> 156 0.3562038937 0.2999315 0.8833584 0.08995892 Pasteur
#> 157 -0.3952560297 0.3152173 0.8801489 0.09936192 Grant-White
#> 158 -0.6339724772 0.3152173 0.8801489 0.09936192 Grant-White
#> 159 0.2006240287 0.3152173 0.8801489 0.09936192 Grant-White
#> 160 -0.3424279836 0.3152173 0.8801489 0.09936192 Grant-White
#> 161 0.4351919667 0.3152173 0.8801489 0.09936192 Grant-White
#> 162 0.3115124621 0.3152173 0.8801489 0.09936192 Grant-White
#> 163 2.1561291304 0.3152173 0.8801489 0.09936192 Grant-White
#> 164 -0.2901419159 0.3152173 0.8801489 0.09936192 Grant-White
#> 165 -0.0836930350 0.3152173 0.8801489 0.09936192 Grant-White
#> 166 -0.0180739626 0.3152173 0.8801489 0.09936192 Grant-White
#> 167 -0.3482023390 0.3152173 0.8801489 0.09936192 Grant-White
#> 168 -2.1021597427 0.3152173 0.8801489 0.09936192 Grant-White
#> 169 -0.6455211880 0.3152173 0.8801489 0.09936192 Grant-White
#> 170 -1.4615522765 0.3152173 0.8801489 0.09936192 Grant-White
#> 171 1.0262088017 0.3152173 0.8801489 0.09936192 Grant-White
#> 172 -1.0506495591 0.3152173 0.8801489 0.09936192 Grant-White
#> 173 0.4308707217 0.3152173 0.8801489 0.09936192 Grant-White
#> 174 0.9582254779 0.3152173 0.8801489 0.09936192 Grant-White
#> 175 -0.2535530327 0.3152173 0.8801489 0.09936192 Grant-White
#> 176 1.4214142722 0.3152173 0.8801489 0.09936192 Grant-White
#> 177 -0.9540052265 0.3152173 0.8801489 0.09936192 Grant-White
#> 178 1.0029529231 0.3152173 0.8801489 0.09936192 Grant-White
#> 179 1.2184135369 0.3152173 0.8801489 0.09936192 Grant-White
#> 180 -1.2498710090 0.3152173 0.8801489 0.09936192 Grant-White
#> 181 -0.5198466305 0.3152173 0.8801489 0.09936192 Grant-White
#> 182 -0.0471041875 0.3152173 0.8801489 0.09936192 Grant-White
#> 183 0.4393404762 0.3152173 0.8801489 0.09936192 Grant-White
#> 184 -1.0311730096 0.3152173 0.8801489 0.09936192 Grant-White
#> 185 -0.9502259332 0.3152173 0.8801489 0.09936192 Grant-White
#> 186 -0.1141763435 0.3152173 0.8801489 0.09936192 Grant-White
#> 187 -0.4004884068 0.3152173 0.8801489 0.09936192 Grant-White
#> 188 0.1050635903 0.3152173 0.8801489 0.09936192 Grant-White
#> 189 -0.3354112770 0.3152173 0.8801489 0.09936192 Grant-White
#> 190 -1.5556596125 0.3152173 0.8801489 0.09936192 Grant-White
#> 191 -0.8442006782 0.3152173 0.8801489 0.09936192 Grant-White
#> 192 0.0780283916 0.3152173 0.8801489 0.09936192 Grant-White
#> 193 0.2011659712 0.3152173 0.8801489 0.09936192 Grant-White
#> 194 -2.5263954621 0.3152173 0.8801489 0.09936192 Grant-White
#> 195 -0.6914909578 0.3152173 0.8801489 0.09936192 Grant-White
#> 196 2.0234378663 0.3152173 0.8801489 0.09936192 Grant-White
#> 197 0.9733807823 0.3152173 0.8801489 0.09936192 Grant-White
#> 198 0.4204058784 0.3152173 0.8801489 0.09936192 Grant-White
#> 199 -0.9260589225 0.3152173 0.8801489 0.09936192 Grant-White
#> 200 -0.5669003212 0.3152173 0.8801489 0.09936192 Grant-White
#> 201 0.1202188947 0.3152173 0.8801489 0.09936192 Grant-White
#> 202 0.6210804306 0.3152173 0.8801489 0.09936192 Grant-White
#> 203 -1.3421940527 0.3152173 0.8801489 0.09936192 Grant-White
#> 204 0.1625820976 0.3152173 0.8801489 0.09936192 Grant-White
#> 205 -0.0323180992 0.3152173 0.8801489 0.09936192 Grant-White
#> 206 0.2444403061 0.3152173 0.8801489 0.09936192 Grant-White
#> 207 -0.7443190039 0.3152173 0.8801489 0.09936192 Grant-White
#> 208 -0.4379884220 0.3152173 0.8801489 0.09936192 Grant-White
#> 209 -1.8529784508 0.3152173 0.8801489 0.09936192 Grant-White
#> 210 -0.8256352699 0.3152173 0.8801489 0.09936192 Grant-White
#> 211 1.2003900978 0.3152173 0.8801489 0.09936192 Grant-White
#> 212 -0.3328743349 0.3152173 0.8801489 0.09936192 Grant-White
#> 213 0.0199679685 0.3152173 0.8801489 0.09936192 Grant-White
#> 214 1.6758280024 0.3152173 0.8801489 0.09936192 Grant-White
#> 215 -0.7190680830 0.3152173 0.8801489 0.09936192 Grant-White
#> 216 -0.2050463207 0.3152173 0.8801489 0.09936192 Grant-White
#> 217 1.9621400656 0.3152173 0.8801489 0.09936192 Grant-White
#> 218 -0.9345287129 0.3152173 0.8801489 0.09936192 Grant-White
#> 219 -0.3534347427 0.3152173 0.8801489 0.09936192 Grant-White
#> 220 -1.9580925486 0.3152173 0.8801489 0.09936192 Grant-White
#> 221 -1.3602175025 0.3152173 0.8801489 0.09936192 Grant-White
#> 222 0.0859562303 0.3152173 0.8801489 0.09936192 Grant-White
#> 223 -0.2340765190 0.3152173 0.8801489 0.09936192 Grant-White
#> 224 0.6780569596 0.3152173 0.8801489 0.09936192 Grant-White
#> 225 -0.4295186317 0.3152173 0.8801489 0.09936192 Grant-White
#> 226 -0.6920329003 0.3152173 0.8801489 0.09936192 Grant-White
#> 227 -0.7158307215 0.3152173 0.8801489 0.09936192 Grant-White
#> 228 -0.1960345878 0.3152173 0.8801489 0.09936192 Grant-White
#> 229 -0.3676788793 0.3152173 0.8801489 0.09936192 Grant-White
#> 230 1.7742566022 0.3152173 0.8801489 0.09936192 Grant-White
#> 231 -0.6792418741 0.3152173 0.8801489 0.09936192 Grant-White
#> 232 0.0760333654 0.3152173 0.8801489 0.09936192 Grant-White
#> 233 1.4989512714 0.3152173 0.8801489 0.09936192 Grant-White
#> 234 -0.7881352546 0.3152173 0.8801489 0.09936192 Grant-White
#> 235 -1.3564381627 0.3152173 0.8801489 0.09936192 Grant-White
#> 236 -0.3424279836 0.3152173 0.8801489 0.09936192 Grant-White
#> 237 1.1080670102 0.3152173 0.8801489 0.09936192 Grant-White
#> 238 1.0476803416 0.3152173 0.8801489 0.09936192 Grant-White
#> 239 1.0224294726 0.3152173 0.8801489 0.09936192 Grant-White
#> 240 0.3823639473 0.3152173 0.8801489 0.09936192 Grant-White
#> 241 0.5644730554 0.3152173 0.8801489 0.09936192 Grant-White
#> 242 0.7880342609 0.3152173 0.8801489 0.09936192 Grant-White
#> 243 0.4308707217 0.3152173 0.8801489 0.09936192 Grant-White
#> 244 0.4038355230 0.3152173 0.8801489 0.09936192 Grant-White
#> 245 -0.4437627683 0.3152173 0.8801489 0.09936192 Grant-White
#> 246 0.0812657691 0.3152173 0.8801489 0.09936192 Grant-White
#> 247 0.1483379252 0.3152173 0.8801489 0.09936192 Grant-White
#> 248 0.5392221863 0.3152173 0.8801489 0.09936192 Grant-White
#> 249 0.5359848088 0.3152173 0.8801489 0.09936192 Grant-White
#> 250 -0.1702417405 0.3152173 0.8801489 0.09936192 Grant-White
#> 251 0.4361030987 0.3152173 0.8801489 0.09936192 Grant-White
#> 252 2.2284336635 0.3152173 0.8801489 0.09936192 Grant-White
#> 253 1.3648069594 0.3152173 0.8801489 0.09936192 Grant-White
#> 254 0.6513529802 0.3152173 0.8801489 0.09936192 Grant-White
#> 255 1.6943933841 0.3152173 0.8801489 0.09936192 Grant-White
#> 256 -0.1574506784 0.3152173 0.8801489 0.09936192 Grant-White
#> 257 -0.2768089380 0.3152173 0.8801489 0.09936192 Grant-White
#> 258 1.8047399107 0.3152173 0.8801489 0.09936192 Grant-White
#> 259 -0.0328600509 0.3152173 0.8801489 0.09936192 Grant-White
#> 260 0.2154100812 0.3152173 0.8801489 0.09936192 Grant-White
#> 261 0.6358664831 0.3152173 0.8801489 0.09936192 Grant-White
#> 262 -0.4412257995 0.3152173 0.8801489 0.09936192 Grant-White
#> 263 0.2438983636 0.3152173 0.8801489 0.09936192 Grant-White
#> 264 0.9739226982 0.3152173 0.8801489 0.09936192 Grant-White
#> 265 1.0061903098 0.3152173 0.8801489 0.09936192 Grant-White
#> 266 0.9596785882 0.3152173 0.8801489 0.09936192 Grant-White
#> 267 2.1052961106 0.3152173 0.8801489 0.09936192 Grant-White
#> 268 0.9501249128 0.3152173 0.8801489 0.09936192 Grant-White
#> 269 1.1403346218 0.3152173 0.8801489 0.09936192 Grant-White
#> 270 -0.4152744951 0.3152173 0.8801489 0.09936192 Grant-White
#> 271 0.5026333389 0.3152173 0.8801489 0.09936192 Grant-White
#> 272 0.0051818802 0.3152173 0.8801489 0.09936192 Grant-White
#> 273 -0.3096184562 0.3152173 0.8801489 0.09936192 Grant-White
#> 274 -0.3857023185 0.3152173 0.8801489 0.09936192 Grant-White
#> 275 -0.8874750488 0.3152173 0.8801489 0.09936192 Grant-White
#> 276 -1.1134004701 0.3152173 0.8801489 0.09936192 Grant-White
#> 277 -0.2283021636 0.3152173 0.8801489 0.09936192 Grant-White
#> 278 0.1678144746 0.3152173 0.8801489 0.09936192 Grant-White
#> 279 -1.1662284537 0.3152173 0.8801489 0.09936192 Grant-White
#> 280 -0.4527744745 0.3152173 0.8801489 0.09936192 Grant-White
#> 281 -0.6952703137 0.3152173 0.8801489 0.09936192 Grant-White
#> 282 1.1655855175 0.3152173 0.8801489 0.09936192 Grant-White
#> 283 -0.4908164323 0.3152173 0.8801489 0.09936192 Grant-White
#> 284 0.4541265645 0.3152173 0.8801489 0.09936192 Grant-White
#> 285 -0.7591050564 0.3152173 0.8801489 0.09936192 Grant-White
#> 286 -0.4623281857 0.3152173 0.8801489 0.09936192 Grant-White
#> 287 1.3363186770 0.3152173 0.8801489 0.09936192 Grant-White
#> 288 -0.7823609350 0.3152173 0.8801489 0.09936192 Grant-White
#> 289 0.1140753232 0.3152173 0.8801489 0.09936192 Grant-White
#> 290 -0.2611117177 0.3152173 0.8801489 0.09936192 Grant-White
#> 291 -0.5849237710 0.3152173 0.8801489 0.09936192 Grant-White
#> 292 -1.4087242662 0.3152173 0.8801489 0.09936192 Grant-White
#> 293 -0.2430882519 0.3152173 0.8801489 0.09936192 Grant-White
#> 294 -0.1760160958 0.3152173 0.8801489 0.09936192 Grant-White
#> 295 -0.7448609198 0.3152173 0.8801489 0.09936192 Grant-White
#> 296 -0.1341948089 0.3152173 0.8801489 0.09936192 Grant-White
#> 297 -0.7480982973 0.3152173 0.8801489 0.09936192 Grant-White
#> 298 -0.9345287129 0.3152173 0.8801489 0.09936192 Grant-White
#> 299 0.8873740285 0.3152173 0.8801489 0.09936192 Grant-White
#> 300 -0.0566578363 0.3152173 0.8801489 0.09936192 Grant-White
#> 301 0.5830384728 0.3152173 0.8801489 0.09936192 Grant-White
# Fixed external latent prior (shared across groups) for regression scores;
# conceptually similar to mirt::fscores(mean, cov)
fit <- cfa("visual =~ x1 + x2 + x3",
data = HolzingerSwineford1939,
group = "school", group.equal = c("loadings", "intercepts"))
get_fs(fit, prior_mean = c(visual = -0.12), prior_cov = 0.33)
#> fs_visual fs_visual_se visual_by_fs_visual ev_fs_visual school
#> 1 -0.8661444157 0.2460880 0.7578508 0.06055928 Pasteur
#> 2 -0.1702694303 0.2460880 0.7578508 0.06055928 Pasteur
#> 3 -0.3296920230 0.2460880 0.7578508 0.06055928 Pasteur
#> 4 0.2799417046 0.2460880 0.7578508 0.06055928 Pasteur
#> 5 -0.7047116341 0.2460880 0.7578508 0.06055928 Pasteur
#> 6 -0.1351731231 0.2460880 0.7578508 0.06055928 Pasteur
#> 7 -0.7538261380 0.2460880 0.7578508 0.06055928 Pasteur
#> 8 -0.1903321424 0.2460880 0.7578508 0.06055928 Pasteur
#> 9 -0.4470125320 0.2460880 0.7578508 0.06055928 Pasteur
#> 10 -0.8320432414 0.2460880 0.7578508 0.06055928 Pasteur
#> 11 -0.3236677575 0.2460880 0.7578508 0.06055928 Pasteur
#> 12 0.1876927826 0.2460880 0.7578508 0.06055928 Pasteur
#> 13 0.5737488481 0.2460880 0.7578508 0.06055928 Pasteur
#> 14 -0.2474880790 0.2460880 0.7578508 0.06055928 Pasteur
#> 15 0.4584285626 0.2460880 0.7578508 0.06055928 Pasteur
#> 16 -0.1522102556 0.2460880 0.7578508 0.06055928 Pasteur
#> 17 -0.5081722451 0.2460880 0.7578508 0.06055928 Pasteur
#> 18 -0.0770187318 0.2460880 0.7578508 0.06055928 Pasteur
#> 19 0.5627157155 0.2460880 0.7578508 0.06055928 Pasteur
#> 20 0.4062682214 0.2460880 0.7578508 0.06055928 Pasteur
#> 21 -0.0459497806 0.2460880 0.7578508 0.06055928 Pasteur
#> 22 0.8444544127 0.2460880 0.7578508 0.06055928 Pasteur
#> 23 -0.2424655900 0.2460880 0.7578508 0.06055928 Pasteur
#> 24 -0.4640496724 0.2460880 0.7578508 0.06055928 Pasteur
#> 25 -0.4620663771 0.2460880 0.7578508 0.06055928 Pasteur
#> 26 0.2408515785 0.2460880 0.7578508 0.06055928 Pasteur
#> 27 0.7381939298 0.2460880 0.7578508 0.06055928 Pasteur
#> 28 -0.1301675700 0.2460880 0.7578508 0.06055928 Pasteur
#> 29 0.6148660991 0.2460880 0.7578508 0.06055928 Pasteur
#> 30 -0.7508377521 0.2460880 0.7578508 0.06055928 Pasteur
#> 31 -0.5723405940 0.2460880 0.7578508 0.06055928 Pasteur
#> 32 -1.0235601413 0.2460880 0.7578508 0.06055928 Pasteur
#> 33 -0.3377198338 0.2460880 0.7578508 0.06055928 Pasteur
#> 34 -0.1311524028 0.2460880 0.7578508 0.06055928 Pasteur
#> 35 -0.6344987619 0.2460880 0.7578508 0.06055928 Pasteur
#> 36 -0.8220321431 0.2460880 0.7578508 0.06055928 Pasteur
#> 37 0.4453918847 0.2460880 0.7578508 0.06055928 Pasteur
#> 38 -0.2765365489 0.2460880 0.7578508 0.06055928 Pasteur
#> 39 0.5075633242 0.2460880 0.7578508 0.06055928 Pasteur
#> 40 0.1305302102 0.2460880 0.7578508 0.06055928 Pasteur
#> 41 -0.4520180851 0.2460880 0.7578508 0.06055928 Pasteur
#> 42 -0.6816299704 0.2460880 0.7578508 0.06055928 Pasteur
#> 43 -0.8681210674 0.2460880 0.7578508 0.06055928 Pasteur
#> 44 -0.3557587429 0.2460880 0.7578508 0.06055928 Pasteur
#> 45 0.1556017972 0.2460880 0.7578508 0.06055928 Pasteur
#> 46 0.7893256009 0.2460880 0.7578508 0.06055928 Pasteur
#> 47 0.8364266019 0.2460880 0.7578508 0.06055928 Pasteur
#> 48 -0.6465336632 0.2460880 0.7578508 0.06055928 Pasteur
#> 49 -0.0007985378 0.2460880 0.7578508 0.06055928 Pasteur
#> 50 -0.5643330410 0.2460880 0.7578508 0.06055928 Pasteur
#> 51 0.4794897372 0.2460880 0.7578508 0.06055928 Pasteur
#> 52 -0.0038410533 0.2460880 0.7578508 0.06055928 Pasteur
#> 53 0.2448656552 0.2460880 0.7578508 0.06055928 Pasteur
#> 54 -0.3487496446 0.2460880 0.7578508 0.06055928 Pasteur
#> 55 0.5938049244 0.2460880 0.7578508 0.06055928 Pasteur
#> 56 -0.1442027026 0.2460880 0.7578508 0.06055928 Pasteur
#> 57 0.1255515586 0.2460880 0.7578508 0.06055928 Pasteur
#> 58 0.5286211770 0.2460880 0.7578508 0.06055928 Pasteur
#> 59 -0.2745396395 0.2460880 0.7578508 0.06055928 Pasteur
#> 60 -0.3788234628 0.2460880 0.7578508 0.06055928 Pasteur
#> 61 0.3250726897 0.2460880 0.7578508 0.06055928 Pasteur
#> 62 0.3270931788 0.2460880 0.7578508 0.06055928 Pasteur
#> 63 -0.3808472736 0.2460880 0.7578508 0.06055928 Pasteur
#> 64 -0.5121962794 0.2460880 0.7578508 0.06055928 Pasteur
#> 65 0.2889948716 0.2460880 0.7578508 0.06055928 Pasteur
#> 66 0.0984425466 0.2460880 0.7578508 0.06055928 Pasteur
#> 67 0.6248874975 0.2460880 0.7578508 0.06055928 Pasteur
#> 68 0.3671950391 0.2460880 0.7578508 0.06055928 Pasteur
#> 69 -0.8490803818 0.2460880 0.7578508 0.06055928 Pasteur
#> 70 -0.7528412973 0.2460880 0.7578508 0.06055928 Pasteur
#> 71 -1.0897051443 0.2460880 0.7578508 0.06055928 Pasteur
#> 72 0.4153147447 0.2460880 0.7578508 0.06055928 Pasteur
#> 73 -0.0439193340 0.2460880 0.7578508 0.06055928 Pasteur
#> 74 0.2639096626 0.2460880 0.7578508 0.06055928 Pasteur
#> 75 -0.2615029538 0.2460880 0.7578508 0.06055928 Pasteur
#> 76 -0.1933138925 0.2460880 0.7578508 0.06055928 Pasteur
#> 77 -0.0599815914 0.2460880 0.7578508 0.06055928 Pasteur
#> 78 -0.7157075652 0.2460880 0.7578508 0.06055928 Pasteur
#> 79 0.6740086372 0.2460880 0.7578508 0.06055928 Pasteur
#> 80 -0.1331526341 0.2460880 0.7578508 0.06055928 Pasteur
#> 81 -0.3407251556 0.2460880 0.7578508 0.06055928 Pasteur
#> 82 0.7111187976 0.2460880 0.7578508 0.06055928 Pasteur
#> 83 -0.0178625717 0.2460880 0.7578508 0.06055928 Pasteur
#> 84 -0.2905952611 0.2460880 0.7578508 0.06055928 Pasteur
#> 85 -0.9182712197 0.2460880 0.7578508 0.06055928 Pasteur
#> 86 0.0974407701 0.2460880 0.7578508 0.06055928 Pasteur
#> 87 0.2097524120 0.2460880 0.7578508 0.06055928 Pasteur
#> 88 0.1014648044 0.2460880 0.7578508 0.06055928 Pasteur
#> 89 0.1937339841 0.2460880 0.7578508 0.06055928 Pasteur
#> 90 -0.1221397592 0.2460880 0.7578508 0.06055928 Pasteur
#> 91 -0.0258668029 0.2460880 0.7578508 0.06055928 Pasteur
#> 92 0.2318356131 0.2460880 0.7578508 0.06055928 Pasteur
#> 93 -0.2073659610 0.2460880 0.7578508 0.06055928 Pasteur
#> 94 -0.8039696544 0.2460880 0.7578508 0.06055928 Pasteur
#> 95 -0.7207503119 0.2460880 0.7578508 0.06055928 Pasteur
#> 96 0.6289012316 0.2460880 0.7578508 0.06055928 Pasteur
#> 97 -0.8851954015 0.2460880 0.7578508 0.06055928 Pasteur
#> 98 0.1074485544 0.2460880 0.7578508 0.06055928 Pasteur
#> 99 -0.5512930413 0.2460880 0.7578508 0.06055928 Pasteur
#> 100 0.1265297555 0.2460880 0.7578508 0.06055928 Pasteur
#> 101 0.6710099590 0.2460880 0.7578508 0.06055928 Pasteur
#> 102 -0.1131337515 0.2460880 0.7578508 0.06055928 Pasteur
#> 103 -0.2224095137 0.2460880 0.7578508 0.06055928 Pasteur
#> 104 -0.4109144482 0.2460880 0.7578508 0.06055928 Pasteur
#> 105 0.9758067324 0.2460880 0.7578508 0.06055928 Pasteur
#> 106 0.2017448590 0.2460880 0.7578508 0.06055928 Pasteur
#> 107 -0.5112180824 0.2460880 0.7578508 0.06055928 Pasteur
#> 108 0.5276160865 0.2460880 0.7578508 0.06055928 Pasteur
#> 109 -0.2685492537 0.2460880 0.7578508 0.06055928 Pasteur
#> 110 -0.2625014085 0.2460880 0.7578508 0.06055928 Pasteur
#> 111 0.8966047962 0.2460880 0.7578508 0.06055928 Pasteur
#> 112 0.0964423154 0.2460880 0.7578508 0.06055928 Pasteur
#> 113 0.7441979376 0.2460880 0.7578508 0.06055928 Pasteur
#> 114 -0.2645285334 0.2460880 0.7578508 0.06055928 Pasteur
#> 115 0.5065718556 0.2460880 0.7578508 0.06055928 Pasteur
#> 116 -0.6615908301 0.2460880 0.7578508 0.06055928 Pasteur
#> 117 -0.0178559281 0.2460880 0.7578508 0.06055928 Pasteur
#> 118 0.4584488203 0.2460880 0.7578508 0.06055928 Pasteur
#> 119 0.2538952425 0.2460880 0.7578508 0.06055928 Pasteur
#> 120 -0.4349776229 0.2460880 0.7578508 0.06055928 Pasteur
#> 121 -0.4089142248 0.2460880 0.7578508 0.06055928 Pasteur
#> 122 -0.0900487738 0.2460880 0.7578508 0.06055928 Pasteur
#> 123 0.8845595870 0.2460880 0.7578508 0.06055928 Pasteur
#> 124 0.5837768824 0.2460880 0.7578508 0.06055928 Pasteur
#> 125 0.7211332177 0.2460880 0.7578508 0.06055928 Pasteur
#> 126 0.3751959485 0.2460880 0.7578508 0.06055928 Pasteur
#> 127 -0.5883995374 0.2460880 0.7578508 0.06055928 Pasteur
#> 128 -0.5021715592 0.2460880 0.7578508 0.06055928 Pasteur
#> 129 0.4142993540 0.2460880 0.7578508 0.06055928 Pasteur
#> 130 -0.4209424903 0.2460880 0.7578508 0.06055928 Pasteur
#> 131 0.7191466084 0.2460880 0.7578508 0.06055928 Pasteur
#> 132 0.3862357247 0.2460880 0.7578508 0.06055928 Pasteur
#> 133 -0.2895934845 0.2460880 0.7578508 0.06055928 Pasteur
#> 134 -0.2665085069 0.2460880 0.7578508 0.06055928 Pasteur
#> 135 0.3069899433 0.2460880 0.7578508 0.06055928 Pasteur
#> 136 0.5687164015 0.2460880 0.7578508 0.06055928 Pasteur
#> 137 0.6860604822 0.2460880 0.7578508 0.06055928 Pasteur
#> 138 0.1395631194 0.2460880 0.7578508 0.06055928 Pasteur
#> 139 0.8103834537 0.2460880 0.7578508 0.06055928 Pasteur
#> 140 -0.0539506900 0.2460880 0.7578508 0.06055928 Pasteur
#> 141 -0.6475321179 0.2460880 0.7578508 0.06055928 Pasteur
#> 142 0.8073781319 0.2460880 0.7578508 0.06055928 Pasteur
#> 143 0.8133618820 0.2460880 0.7578508 0.06055928 Pasteur
#> 144 0.7652388467 0.2460880 0.7578508 0.06055928 Pasteur
#> 145 -0.9844397998 0.2460880 0.7578508 0.06055928 Pasteur
#> 146 -0.3697938833 0.2460880 0.7578508 0.06055928 Pasteur
#> 147 -0.5553034537 0.2460880 0.7578508 0.06055928 Pasteur
#> 148 -0.3868512736 0.2460880 0.7578508 0.06055928 Pasteur
#> 149 0.0332790570 0.2460880 0.7578508 0.06055928 Pasteur
#> 150 0.7943311540 0.2460880 0.7578508 0.06055928 Pasteur
#> 151 0.8444577267 0.2460880 0.7578508 0.06055928 Pasteur
#> 152 -0.0058210268 0.2460880 0.7578508 0.06055928 Pasteur
#> 153 -0.1271453123 0.2460880 0.7578508 0.06055928 Pasteur
#> 154 -0.0529522353 0.2460880 0.7578508 0.06055928 Pasteur
#> 155 -0.3969028952 0.2460880 0.7578508 0.06055928 Pasteur
#> 156 -0.5152016012 0.2460880 0.7578508 0.06055928 Pasteur
#> 157 -1.0105865497 0.2051125 0.8500167 0.04207115 Grant-White
#> 158 -0.1639869772 0.2051125 0.8500167 0.04207115 Grant-White
#> 159 0.1464895899 0.2051125 0.8500167 0.04207115 Grant-White
#> 160 -0.6395921201 0.2051125 0.8500167 0.04207115 Grant-White
#> 161 -0.6703227143 0.2051125 0.8500167 0.04207115 Grant-White
#> 162 0.0102507587 0.2051125 0.8500167 0.04207115 Grant-White
#> 163 1.0423360918 0.2051125 0.8500167 0.04207115 Grant-White
#> 164 -0.6283440905 0.2051125 0.8500167 0.04207115 Grant-White
#> 165 -0.4987987089 0.2051125 0.8500167 0.04207115 Grant-White
#> 166 -0.8637203668 0.2051125 0.8500167 0.04207115 Grant-White
#> 167 -0.7965090547 0.2051125 0.8500167 0.04207115 Grant-White
#> 168 -0.6909779191 0.2051125 0.8500167 0.04207115 Grant-White
#> 169 -0.3181655436 0.2051125 0.8500167 0.04207115 Grant-White
#> 170 -0.9272746177 0.2051125 0.8500167 0.04207115 Grant-White
#> 171 0.2985310257 0.2051125 0.8500167 0.04207115 Grant-White
#> 172 -0.0855505439 0.2051125 0.8500167 0.04207115 Grant-White
#> 173 0.6588751951 0.2051125 0.8500167 0.04207115 Grant-White
#> 174 0.0729074909 0.2051125 0.8500167 0.04207115 Grant-White
#> 175 -0.0189387402 0.2051125 0.8500167 0.04207115 Grant-White
#> 176 0.2018321995 0.2051125 0.8500167 0.04207115 Grant-White
#> 177 -0.4659538877 0.2051125 0.8500167 0.04207115 Grant-White
#> 178 0.4414633016 0.2051125 0.8500167 0.04207115 Grant-White
#> 179 -0.0560401262 0.2051125 0.8500167 0.04207115 Grant-White
#> 180 0.5241774642 0.2051125 0.8500167 0.04207115 Grant-White
#> 181 -0.1718988642 0.2051125 0.8500167 0.04207115 Grant-White
#> 182 -0.2853207224 0.2051125 0.8500167 0.04207115 Grant-White
#> 183 -0.8688727113 0.2051125 0.8500167 0.04207115 Grant-White
#> 184 -0.9388206576 0.2051125 0.8500167 0.04207115 Grant-White
#> 185 -0.5100467336 0.2051125 0.8500167 0.04207115 Grant-White
#> 186 -0.8555069865 0.2051125 0.8500167 0.04207115 Grant-White
#> 187 -0.4437787572 0.2051125 0.8500167 0.04207115 Grant-White
#> 188 -0.4343257699 0.2051125 0.8500167 0.04207115 Grant-White
#> 189 -0.7432841403 0.2051125 0.8500167 0.04207115 Grant-White
#> 190 -0.7879993982 0.2051125 0.8500167 0.04207115 Grant-White
#> 191 -0.3941879956 0.2051125 0.8500167 0.04207115 Grant-White
#> 192 -0.3199834895 0.2051125 0.8500167 0.04207115 Grant-White
#> 193 -0.4933237017 0.2051125 0.8500167 0.04207115 Grant-White
#> 194 -0.9658941954 0.2051125 0.8500167 0.04207115 Grant-White
#> 195 -0.8320939831 0.2051125 0.8500167 0.04207115 Grant-White
#> 196 -0.2093229080 0.2051125 0.8500167 0.04207115 Grant-White
#> 197 -0.6344168671 0.2051125 0.8500167 0.04207115 Grant-White
#> 198 -0.6179935846 0.2051125 0.8500167 0.04207115 Grant-White
#> 199 -0.7715761206 0.2051125 0.8500167 0.04207115 Grant-White
#> 200 -0.3941879956 0.2051125 0.8500167 0.04207115 Grant-White
#> 201 0.3572309424 0.2051125 0.8500167 0.04207115 Grant-White
#> 202 -0.5292066503 0.2051125 0.8500167 0.04207115 Grant-White
#> 203 -0.6946349755 0.2051125 0.8500167 0.04207115 Grant-White
#> 204 0.0254538599 0.2051125 0.8500167 0.04207115 Grant-White
#> 205 -0.4358439616 0.2051125 0.8500167 0.04207115 Grant-White
#> 206 0.2945742102 0.2051125 0.8500167 0.04207115 Grant-White
#> 207 -0.2321169775 0.2051125 0.8500167 0.04207115 Grant-White
#> 208 -0.9725876401 0.2051125 0.8500167 0.04207115 Grant-White
#> 209 0.3085588739 0.2051125 0.8500167 0.04207115 Grant-White
#> 210 -0.2941530317 0.2051125 0.8500167 0.04207115 Grant-White
#> 211 -0.1001329721 0.2051125 0.8500167 0.04207115 Grant-White
#> 212 -0.0365804603 0.2051125 0.8500167 0.04207115 Grant-White
#> 213 -0.5733012352 0.2051125 0.8500167 0.04207115 Grant-White
#> 214 -0.0143824212 0.2051125 0.8500167 0.04207115 Grant-White
#> 215 -0.9597985152 0.2051125 0.8500167 0.04207115 Grant-White
#> 216 -0.0244349120 0.2051125 0.8500167 0.04207115 Grant-White
#> 217 -0.0760975616 0.2051125 0.8500167 0.04207115 Grant-White
#> 218 -0.1500023184 0.2051125 0.8500167 0.04207115 Grant-White
#> 219 0.5348030818 0.2051125 0.8500167 0.04207115 Grant-White
#> 220 -0.3826402167 0.2051125 0.8500167 0.04207115 Grant-White
#> 221 -0.2090019893 0.2051125 0.8500167 0.04207115 Grant-White
#> 222 -0.8165911327 0.2051125 0.8500167 0.04207115 Grant-White
#> 223 0.7592328168 0.2051125 0.8500167 0.04207115 Grant-White
#> 224 -0.9126692810 0.2051125 0.8500167 0.04207115 Grant-White
#> 225 -0.4473900064 0.2051125 0.8500167 0.04207115 Grant-White
#> 226 0.8352517957 0.2051125 0.8500167 0.04207115 Grant-White
#> 227 -0.1928767219 0.2051125 0.8500167 0.04207115 Grant-White
#> 228 -0.5617551904 0.2051125 0.8500167 0.04207115 Grant-White
#> 229 -0.3525320296 0.2051125 0.8500167 0.04207115 Grant-White
#> 230 0.8240037710 0.2051125 0.8500167 0.04207115 Grant-White
#> 231 -0.8913916692 0.2051125 0.8500167 0.04207115 Grant-White
#> 232 -0.7885971676 0.2051125 0.8500167 0.04207115 Grant-White
#> 233 -0.5586976375 0.2051125 0.8500167 0.04207115 Grant-White
#> 234 0.6351589644 0.2051125 0.8500167 0.04207115 Grant-White
#> 235 -0.7305179240 0.2051125 0.8500167 0.04207115 Grant-White
#> 236 -0.3352112332 0.2051125 0.8500167 0.04207115 Grant-White
#> 237 -0.3336701331 0.2051125 0.8500167 0.04207115 Grant-White
#> 238 -0.6496199683 0.2051125 0.8500167 0.04207115 Grant-White
#> 239 0.8267404051 0.2051125 0.8500167 0.04207115 Grant-White
#> 240 -0.1278042793 0.2051125 0.8500167 0.04207115 Grant-White
#> 241 0.9632789856 0.2051125 0.8500167 0.04207115 Grant-White
#> 242 0.4709508157 0.2051125 0.8500167 0.04207115 Grant-White
#> 243 -0.3072172682 0.2051125 0.8500167 0.04207115 Grant-White
#> 244 0.5372417006 0.2051125 0.8500167 0.04207115 Grant-White
#> 245 -0.8077570794 0.2051125 0.8500167 0.04207115 Grant-White
#> 246 0.6451902857 0.2051125 0.8500167 0.04207115 Grant-White
#> 247 0.2027297182 0.2051125 0.8500167 0.04207115 Grant-White
#> 248 -0.2412261375 0.2051125 0.8500167 0.04207115 Grant-White
#> 249 0.3341142151 0.2051125 0.8500167 0.04207115 Grant-White
#> 250 -1.0476879357 0.2051125 0.8500167 0.04207115 Grant-White
#> 251 0.3304571588 0.2051125 0.8500167 0.04207115 Grant-White
#> 252 0.9842797517 0.2051125 0.8500167 0.04207115 Grant-White
#> 253 -0.0490469273 0.2051125 0.8500167 0.04207115 Grant-White
#> 254 -0.2236073210 0.2051125 0.8500167 0.04207115 Grant-White
#> 255 -0.3732083989 0.2051125 0.8500167 0.04207115 Grant-White
#> 256 0.0756441249 0.2051125 0.8500167 0.04207115 Grant-White
#> 257 0.5865115385 0.2051125 0.8500167 0.04207115 Grant-White
#> 258 0.3201295564 0.2051125 0.8500167 0.04207115 Grant-White
#> 259 0.9070176878 0.2051125 0.8500167 0.04207115 Grant-White
#> 260 -1.0808342452 0.2051125 0.8500167 0.04207115 Grant-White
#> 261 -0.0901051239 0.2051125 0.8500167 0.04207115 Grant-White
#> 262 -0.6395921201 0.2051125 0.8500167 0.04207115 Grant-White
#> 263 -0.5885060704 0.2051125 0.8500167 0.04207115 Grant-White
#> 264 -0.4291716864 0.2051125 0.8500167 0.04207115 Grant-White
#> 265 -0.2950505554 0.2051125 0.8500167 0.04207115 Grant-White
#> 266 0.0278924788 0.2051125 0.8500167 0.04207115 Grant-White
#> 267 0.0239374023 0.2051125 0.8500167 0.04207115 Grant-White
#> 268 0.6615889257 0.2051125 0.8500167 0.04207115 Grant-White
#> 269 0.8598867146 0.2051125 0.8500167 0.04207115 Grant-White
#> 270 -0.7578894770 0.2051125 0.8500167 0.04207115 Grant-White
#> 271 -0.3884132391 0.2051125 0.8500167 0.04207115 Grant-White
#> 272 0.6920197706 0.2051125 0.8500167 0.04207115 Grant-White
#> 273 -0.2698178670 0.2051125 0.8500167 0.04207115 Grant-White
#> 274 -0.5492869892 0.2051125 0.8500167 0.04207115 Grant-White
#> 275 0.4177259013 0.2051125 0.8500167 0.04207115 Grant-White
#> 276 -0.7536346463 0.2051125 0.8500167 0.04207115 Grant-White
#> 277 -0.6617884202 0.2051125 0.8500167 0.04207115 Grant-White
#> 278 -0.3969246297 0.2051125 0.8500167 0.04207115 Grant-White
#> 279 -0.1430091195 0.2051125 0.8500167 0.04207115 Grant-White
#> 280 -0.2293574349 0.2051125 0.8500167 0.04207115 Grant-White
#> 281 -0.6213526306 0.2051125 0.8500167 0.04207115 Grant-White
#> 282 0.3578287118 0.2051125 0.8500167 0.04207115 Grant-White
#> 283 -0.2749931200 0.2051125 0.8500167 0.04207115 Grant-White
#> 284 -0.1822264666 0.2051125 0.8500167 0.04207115 Grant-White
#> 285 -0.5748194269 0.2051125 0.8500167 0.04207115 Grant-White
#> 286 -0.2235844174 0.2051125 0.8500167 0.04207115 Grant-White
#> 287 -0.1071261710 0.2051125 0.8500167 0.04207115 Grant-White
#> 288 -0.4559243005 0.2051125 0.8500167 0.04207115 Grant-White
#> 289 -0.8308738066 0.2051125 0.8500167 0.04207115 Grant-White
#> 290 -0.1004327263 0.2051125 0.8500167 0.04207115 Grant-White
#> 291 -0.8655383128 0.2051125 0.8500167 0.04207115 Grant-White
#> 292 -0.6380739235 0.2051125 0.8500167 0.04207115 Grant-White
#> 293 -0.3820195437 0.2051125 0.8500167 0.04207115 Grant-White
#> 294 -0.0523830649 0.2051125 0.8500167 0.04207115 Grant-White
#> 295 -0.4851120654 0.2051125 0.8500167 0.04207115 Grant-White
#> 296 0.3037062787 0.2051125 0.8500167 0.04207115 Grant-White
#> 297 -0.5188790479 0.2051125 0.8500167 0.04207115 Grant-White
#> 298 -0.4939461137 0.2051125 0.8500167 0.04207115 Grant-White
#> 299 -0.3181672777 0.2051125 0.8500167 0.04207115 Grant-White
#> 300 -0.9081129669 0.2051125 0.8500167 0.04207115 Grant-White
#> 301 0.3636017293 0.2051125 0.8500167 0.04207115 Grant-White
# Product-score indicator for the ind60 x dem60 interaction (single-group
# lavaan models only, v1); see compute_fs_prod() for the derivation
get_fs(PoliticalDemocracy[c("x1", "x2", "x3", "y1", "y2", "y3", "y4")],
model = " ind60 =~ x1 + x2 + x3
dem60 =~ y1 + y2 + y3 + y4 ",
product = "ind60:dem60")
#> fs_ind60 fs_dem60 fs_ind60_se fs_dem60_se ind60_by_fs_ind60
#> 1 -0.54258816 -2.74640573 0.1245694 0.6307323 0.9553858
#> 2 0.12647664 -2.85646114 0.1245694 0.6307323 0.9553858
#> 3 0.73408891 2.74401728 0.1245694 0.6307323 0.9553858
#> 4 1.25253604 3.10856431 0.1245694 0.6307323 0.9553858
#> 5 0.83355267 1.92455641 0.1245694 0.6307323 0.9553858
#> 6 0.22426801 1.02292332 0.1245694 0.6307323 0.9553858
#> 7 0.12517739 1.00406461 0.1245694 0.6307323 0.9553858
#> 8 0.11783867 -0.37216403 0.1245694 0.6307323 0.9553858
#> 9 0.25175134 -1.24897911 0.1245694 0.6307323 0.9553858
#> 10 0.39938631 2.85267059 0.1245694 0.6307323 0.9553858
#> 11 0.67497777 1.41959595 0.1245694 0.6307323 0.9553858
#> 12 0.56462020 1.08769844 0.1245694 0.6307323 0.9553858
#> 13 1.31236592 1.54090232 0.1245694 0.6307323 0.9553858
#> 14 0.23246021 1.77370863 0.1245694 0.6307323 0.9553858
#> 15 0.58638481 2.45676871 0.1245694 0.6307323 0.9553858
#> 16 0.38404785 2.35887573 0.1245694 0.6307323 0.9553858
#> 17 0.05076465 0.04034088 0.1245694 0.6307323 0.9553858
#> 18 -0.01747337 -1.86718064 0.1245694 0.6307323 0.9553858
#> 19 0.90920762 3.61477756 0.1245694 0.6307323 0.9553858
#> 20 1.12553557 0.88355273 0.1245694 0.6307323 0.9553858
#> 21 0.97202590 3.62673300 0.1245694 0.6307323 0.9553858
#> 22 0.87820036 -3.02428925 0.1245694 0.6307323 0.9553858
#> 23 0.57540754 -1.51695438 0.1245694 0.6307323 0.9553858
#> 24 0.66221224 2.76341635 0.1245694 0.6307323 0.9553858
#> 25 0.92358281 2.00507336 0.1245694 0.6307323 0.9553858
#> 26 -0.89353051 -0.92008050 0.1245694 0.6307323 0.9553858
#> 27 -0.13984744 -1.19025576 0.1245694 0.6307323 0.9553858
#> 28 -0.53828496 -1.01247764 0.1245694 0.6307323 0.9553858
#> 29 -0.80834865 0.10456709 0.1245694 0.6307323 0.9553858
#> 30 -1.25324343 -0.71847055 0.1245694 0.6307323 0.9553858
#> 31 -0.33373641 -1.61401581 0.1245694 0.6307323 0.9553858
#> 32 -1.17441075 -3.27250363 0.1245694 0.6307323 0.9553858
#> 33 -0.12409974 -1.17530231 0.1245694 0.6307323 0.9553858
#> 34 -0.04239173 -0.53796274 0.1245694 0.6307323 0.9553858
#> 35 -0.34010528 0.74552889 0.1245694 0.6307323 0.9553858
#> 36 -0.58953870 1.61018662 0.1245694 0.6307323 0.9553858
#> 37 0.17453657 -0.28144814 0.1245694 0.6307323 0.9553858
#> 38 -0.54457243 0.37694690 0.1245694 0.6307323 0.9553858
#> 39 -1.05196602 -0.62919501 0.1245694 0.6307323 0.9553858
#> 40 -0.05504697 -0.03346842 0.1245694 0.6307323 0.9553858
#> 41 -0.12364358 -0.38394102 0.1245694 0.6307323 0.9553858
#> 42 -0.59058710 1.35347275 0.1245694 0.6307323 0.9553858
#> 43 -0.11968796 0.89227782 0.1245694 0.6307323 0.9553858
#> 44 -1.07176064 -2.08481096 0.1245694 0.6307323 0.9553858
#> 45 -1.09139097 -2.07944291 0.1245694 0.6307323 0.9553858
#> 46 -0.83287255 1.59590721 0.1245694 0.6307323 0.9553858
#> 47 -1.14519896 -1.53352201 0.1245694 0.6307323 0.9553858
#> 48 -0.56115378 2.08138051 0.1245694 0.6307323 0.9553858
#> 49 0.06493340 -1.04044137 0.1245694 0.6307323 0.9553858
#> 50 0.15671638 1.72618633 0.1245694 0.6307323 0.9553858
#> 51 0.34626130 -1.24967043 0.1245694 0.6307323 0.9553858
#> 52 -0.45158373 -2.31742576 0.1245694 0.6307323 0.9553858
#> 53 0.43233465 -1.07533341 0.1245694 0.6307323 0.9553858
#> 54 0.25779725 -0.02904676 0.1245694 0.6307323 0.9553858
#> 55 0.51730650 -2.78207923 0.1245694 0.6307323 0.9553858
#> 56 0.20104991 -2.49001474 0.1245694 0.6307323 0.9553858
#> 57 0.25318620 -2.52145147 0.1245694 0.6307323 0.9553858
#> 58 0.72354623 1.86717109 0.1245694 0.6307323 0.9553858
#> 59 0.24619740 -0.93321102 0.1245694 0.6307323 0.9553858
#> 60 1.21681210 3.19853937 0.1245694 0.6307323 0.9553858
#> 61 0.18167599 -3.15685030 0.1245694 0.6307323 0.9553858
#> 62 -1.16605067 -3.41334680 0.1245694 0.6307323 0.9553858
#> 63 -0.86491026 -3.11864398 0.1245694 0.6307323 0.9553858
#> 64 0.10990059 -0.47238885 0.1245694 0.6307323 0.9553858
#> 65 -0.07376176 2.95292007 0.1245694 0.6307323 0.9553858
#> 66 -0.28782931 -1.96509718 0.1245694 0.6307323 0.9553858
#> 67 -0.02508160 2.96218478 0.1245694 0.6307323 0.9553858
#> 68 -1.31843215 -1.59567027 0.1245694 0.6307323 0.9553858
#> 69 -0.40462357 -1.79146161 0.1245694 0.6307323 0.9553858
#> 70 -0.55568363 -1.01578892 0.1245694 0.6307323 0.9553858
#> 71 -0.71308015 0.08818212 0.1245694 0.6307323 0.9553858
#> 72 0.31014319 1.70765911 0.1245694 0.6307323 0.9553858
#> 73 0.79092897 1.86102556 0.1245694 0.6307323 0.9553858
#> 74 0.08770237 3.12885767 0.1245694 0.6307323 0.9553858
#> 75 -0.14138149 -2.41398025 0.1245694 0.6307323 0.9553858
#> ind60_by_fs_dem60 dem60_by_fs_ind60 dem60_by_fs_dem60 ev_fs_ind60
#> 1 0.181827 0.005867694 0.8688887 0.01551752
#> 2 0.181827 0.005867694 0.8688887 0.01551752
#> 3 0.181827 0.005867694 0.8688887 0.01551752
#> 4 0.181827 0.005867694 0.8688887 0.01551752
#> 5 0.181827 0.005867694 0.8688887 0.01551752
#> 6 0.181827 0.005867694 0.8688887 0.01551752
#> 7 0.181827 0.005867694 0.8688887 0.01551752
#> 8 0.181827 0.005867694 0.8688887 0.01551752
#> 9 0.181827 0.005867694 0.8688887 0.01551752
#> 10 0.181827 0.005867694 0.8688887 0.01551752
#> 11 0.181827 0.005867694 0.8688887 0.01551752
#> 12 0.181827 0.005867694 0.8688887 0.01551752
#> 13 0.181827 0.005867694 0.8688887 0.01551752
#> 14 0.181827 0.005867694 0.8688887 0.01551752
#> 15 0.181827 0.005867694 0.8688887 0.01551752
#> 16 0.181827 0.005867694 0.8688887 0.01551752
#> 17 0.181827 0.005867694 0.8688887 0.01551752
#> 18 0.181827 0.005867694 0.8688887 0.01551752
#> 19 0.181827 0.005867694 0.8688887 0.01551752
#> 20 0.181827 0.005867694 0.8688887 0.01551752
#> 21 0.181827 0.005867694 0.8688887 0.01551752
#> 22 0.181827 0.005867694 0.8688887 0.01551752
#> 23 0.181827 0.005867694 0.8688887 0.01551752
#> 24 0.181827 0.005867694 0.8688887 0.01551752
#> 25 0.181827 0.005867694 0.8688887 0.01551752
#> 26 0.181827 0.005867694 0.8688887 0.01551752
#> 27 0.181827 0.005867694 0.8688887 0.01551752
#> 28 0.181827 0.005867694 0.8688887 0.01551752
#> 29 0.181827 0.005867694 0.8688887 0.01551752
#> 30 0.181827 0.005867694 0.8688887 0.01551752
#> 31 0.181827 0.005867694 0.8688887 0.01551752
#> 32 0.181827 0.005867694 0.8688887 0.01551752
#> 33 0.181827 0.005867694 0.8688887 0.01551752
#> 34 0.181827 0.005867694 0.8688887 0.01551752
#> 35 0.181827 0.005867694 0.8688887 0.01551752
#> 36 0.181827 0.005867694 0.8688887 0.01551752
#> 37 0.181827 0.005867694 0.8688887 0.01551752
#> 38 0.181827 0.005867694 0.8688887 0.01551752
#> 39 0.181827 0.005867694 0.8688887 0.01551752
#> 40 0.181827 0.005867694 0.8688887 0.01551752
#> 41 0.181827 0.005867694 0.8688887 0.01551752
#> 42 0.181827 0.005867694 0.8688887 0.01551752
#> 43 0.181827 0.005867694 0.8688887 0.01551752
#> 44 0.181827 0.005867694 0.8688887 0.01551752
#> 45 0.181827 0.005867694 0.8688887 0.01551752
#> 46 0.181827 0.005867694 0.8688887 0.01551752
#> 47 0.181827 0.005867694 0.8688887 0.01551752
#> 48 0.181827 0.005867694 0.8688887 0.01551752
#> 49 0.181827 0.005867694 0.8688887 0.01551752
#> 50 0.181827 0.005867694 0.8688887 0.01551752
#> 51 0.181827 0.005867694 0.8688887 0.01551752
#> 52 0.181827 0.005867694 0.8688887 0.01551752
#> 53 0.181827 0.005867694 0.8688887 0.01551752
#> 54 0.181827 0.005867694 0.8688887 0.01551752
#> 55 0.181827 0.005867694 0.8688887 0.01551752
#> 56 0.181827 0.005867694 0.8688887 0.01551752
#> 57 0.181827 0.005867694 0.8688887 0.01551752
#> 58 0.181827 0.005867694 0.8688887 0.01551752
#> 59 0.181827 0.005867694 0.8688887 0.01551752
#> 60 0.181827 0.005867694 0.8688887 0.01551752
#> 61 0.181827 0.005867694 0.8688887 0.01551752
#> 62 0.181827 0.005867694 0.8688887 0.01551752
#> 63 0.181827 0.005867694 0.8688887 0.01551752
#> 64 0.181827 0.005867694 0.8688887 0.01551752
#> 65 0.181827 0.005867694 0.8688887 0.01551752
#> 66 0.181827 0.005867694 0.8688887 0.01551752
#> 67 0.181827 0.005867694 0.8688887 0.01551752
#> 68 0.181827 0.005867694 0.8688887 0.01551752
#> 69 0.181827 0.005867694 0.8688887 0.01551752
#> 70 0.181827 0.005867694 0.8688887 0.01551752
#> 71 0.181827 0.005867694 0.8688887 0.01551752
#> 72 0.181827 0.005867694 0.8688887 0.01551752
#> 73 0.181827 0.005867694 0.8688887 0.01551752
#> 74 0.181827 0.005867694 0.8688887 0.01551752
#> 75 0.181827 0.005867694 0.8688887 0.01551752
#> ecov_fs_dem60_fs_ind60 ev_fs_dem60 fs_ind60:fs_dem60 fs_ind60:fs_dem60_se
#> 1 0.005632564 0.3978232 0.84766209 0.4836628
#> 2 0.005632564 0.3978232 -1.00378073 0.4836628
#> 3 0.005632564 0.3978232 1.37184752 0.4836628
#> 4 0.005632564 0.3978232 3.25108370 0.4836628
#> 5 0.005632564 0.3978232 0.96171401 0.4836628
#> 6 0.005632564 0.3978232 -0.41309615 0.4836628
#> 7 0.005632564 0.3978232 -0.51681895 0.4836628
#> 8 0.005632564 0.3978232 -0.68636044 0.4836628
#> 9 0.005632564 0.3978232 -0.95693729 0.4836628
#> 10 0.005632564 0.3978232 0.49681244 0.4836628
#> 11 0.005632564 0.3978232 0.31569057 0.4836628
#> 12 0.005632564 0.3978232 -0.02836862 0.4836628
#> 13 0.005632564 0.3978232 1.37972256 0.4836628
#> 14 0.005632564 0.3978232 -0.23018846 0.4836628
#> 15 0.005632564 0.3978232 0.79810673 0.4836628
#> 16 0.005632564 0.3978232 0.26341602 0.4836628
#> 17 0.005632564 0.3978232 -0.64045724 0.4836628
#> 18 0.005632564 0.3978232 -0.60987920 0.4836628
#> 19 0.005632564 0.3978232 2.64407816 0.4836628
#> 20 0.005632564 0.3978232 0.35196489 0.4836628
#> 21 0.005632564 0.3978232 2.88277329 0.4836628
#> 22 0.005632564 0.3978232 -3.29843704 0.4836628
#> 23 0.005632564 0.3978232 -1.51537211 0.4836628
#> 24 0.005632564 0.3978232 1.18746300 0.4836628
#> 25 0.005632564 0.3978232 1.20934616 0.4836628
#> 26 0.005632564 0.3978232 0.17961487 0.4836628
#> 27 0.005632564 0.3978232 -0.47605091 0.4836628
#> 28 0.005632564 0.3978232 -0.09750365 0.4836628
#> 29 0.005632564 0.3978232 -0.72703179 0.4836628
#> 30 0.005632564 0.3978232 0.25791336 0.4836628
#> 31 0.005632564 0.3978232 -0.10384929 0.4836628
#> 32 0.005632564 0.3978232 3.20075831 0.4836628
#> 33 0.005632564 0.3978232 -0.49665041 0.4836628
#> 34 0.005632564 0.3978232 -0.61969996 0.4836628
#> 35 0.005632564 0.3978232 -0.89606344 0.4836628
#> 36 0.005632564 0.3978232 -1.59177245 0.4836628
#> 37 0.005632564 0.3978232 -0.69162812 0.4836628
#> 38 0.005632564 0.3978232 -0.84778002 0.4836628
#> 39 0.005632564 0.3978232 0.01938665 0.4836628
#> 40 0.005632564 0.3978232 -0.64066279 0.4836628
#> 41 0.005632564 0.3978232 -0.59503329 0.4836628
#> 42 0.005632564 0.3978232 -1.44184867 0.4836628
#> 43 0.005632564 0.3978232 -0.74930004 0.4836628
#> 44 0.005632564 0.3978232 1.59191320 0.4836628
#> 45 0.005632564 0.3978232 1.62698008 0.4836628
#> 46 0.005632564 0.3978232 -1.97169243 0.4836628
#> 47 0.005632564 0.3978232 1.11368267 0.4836628
#> 48 0.005632564 0.3978232 -1.81047968 0.4836628
#> 49 0.005632564 0.3978232 -0.71006453 0.4836628
#> 50 0.005632564 0.3978232 -0.37198346 0.4836628
#> 51 0.005632564 0.3978232 -1.07521763 0.4836628
#> 52 0.005632564 0.3978232 0.40400663 0.4836628
#> 53 0.005632564 0.3978232 -1.10740902 0.4836628
#> 54 0.005632564 0.3978232 -0.64999330 0.4836628
#> 55 0.005632564 0.3978232 -2.08169280 0.4836628
#> 56 0.005632564 0.3978232 -1.14312236 0.4836628
#> 57 0.005632564 0.3978232 -1.28090185 0.4836628
#> 58 0.005632564 0.3978232 0.70847948 0.4836628
#> 59 0.005632564 0.3978232 -0.87225925 0.4836628
#> 60 0.005632564 0.3978232 3.24951627 0.4836628
#> 61 0.005632564 0.3978232 -1.21602903 0.4836628
#> 62 0.005632564 0.3978232 3.33763021 0.4836628
#> 63 0.005632564 0.3978232 2.05484206 0.4836628
#> 64 0.005632564 0.3978232 -0.69442094 0.4836628
#> 65 0.005632564 0.3978232 -0.86031770 0.4836628
#> 66 0.005632564 0.3978232 -0.07689257 0.4836628
#> 67 0.005632564 0.3978232 -0.71680146 0.4836628
#> 68 0.005632564 0.3978232 1.46127786 0.4836628
#> 69 0.005632564 0.3978232 0.08236246 0.4836628
#> 70 0.005632564 0.3978232 -0.07804786 0.4836628
#> 71 0.005632564 0.3978232 -0.70538605 0.4836628
#> 72 0.005632564 0.3978232 -0.11288628 0.4836628
#> 73 0.005632564 0.3978232 0.82943391 0.4836628
#> 74 0.005632564 0.3978232 -0.36809690 0.4836628
#> 75 0.005632564 0.3978232 -0.30121299 0.4836628
#> fs_ind60:fs_dem60_ld
#> 1 0.8311908
#> 2 0.8311908
#> 3 0.8311908
#> 4 0.8311908
#> 5 0.8311908
#> 6 0.8311908
#> 7 0.8311908
#> 8 0.8311908
#> 9 0.8311908
#> 10 0.8311908
#> 11 0.8311908
#> 12 0.8311908
#> 13 0.8311908
#> 14 0.8311908
#> 15 0.8311908
#> 16 0.8311908
#> 17 0.8311908
#> 18 0.8311908
#> 19 0.8311908
#> 20 0.8311908
#> 21 0.8311908
#> 22 0.8311908
#> 23 0.8311908
#> 24 0.8311908
#> 25 0.8311908
#> 26 0.8311908
#> 27 0.8311908
#> 28 0.8311908
#> 29 0.8311908
#> 30 0.8311908
#> 31 0.8311908
#> 32 0.8311908
#> 33 0.8311908
#> 34 0.8311908
#> 35 0.8311908
#> 36 0.8311908
#> 37 0.8311908
#> 38 0.8311908
#> 39 0.8311908
#> 40 0.8311908
#> 41 0.8311908
#> 42 0.8311908
#> 43 0.8311908
#> 44 0.8311908
#> 45 0.8311908
#> 46 0.8311908
#> 47 0.8311908
#> 48 0.8311908
#> 49 0.8311908
#> 50 0.8311908
#> 51 0.8311908
#> 52 0.8311908
#> 53 0.8311908
#> 54 0.8311908
#> 55 0.8311908
#> 56 0.8311908
#> 57 0.8311908
#> 58 0.8311908
#> 59 0.8311908
#> 60 0.8311908
#> 61 0.8311908
#> 62 0.8311908
#> 63 0.8311908
#> 64 0.8311908
#> 65 0.8311908
#> 66 0.8311908
#> 67 0.8311908
#> 68 0.8311908
#> 69 0.8311908
#> 70 0.8311908
#> 71 0.8311908
#> 72 0.8311908
#> 73 0.8311908
#> 74 0.8311908
#> 75 0.8311908