A set of wrapper functions
to extract information from a
lavaan-class object and return
a named vector.
Usage
vec_rsquare(object)
vec_sample_vcov(object)
vec_sample_var(object)
vec_est_var(object)
vec_est_se(object)
vec_def_var(object)
vec_def_se(object)
vec_lavTestLRT(
object,
...,
method = "default",
A.method = "delta",
scaled.shifted = TRUE,
H1 = TRUE,
model.names = NULL
)
vec_lavTestScore(
object,
add = NULL,
release = NULL,
univariate = TRUE,
information = "expected"
)
vec_lavTestWald(object, constraints = NULL, prefix = NULL)
vec_compRelSEM(object, ...)Arguments
- object
A
lavaan-class object.- ...
Additional arguments to be passed to the original function.
- method
An argument to be passed to
lavaan::lavTestLRT(). Please refer to the help page oflavaan::lavTestLRT().- A.method
An argument to be passed
lavaan::lavTestLRT(). Please refer to the help page oflavaan::lavTestLRT().- scaled.shifted
An argument to be passed to
lavaan::lavTestLRT(). Please refer to the help page oflavaan::lavTestLRT().- H1
An argument to be passed to
lavaan::lavTestLRT(). Please refer to the help page oflavaan::lavTestLRT().- model.names
An argument to be passed to
lavaan::lavTestLRT(). Please refer to the help page oflavaan::lavTestLRT(). Unlikelavaan::lavTestLRT(), this argument is required, for the sake of naming the vector to be returned.- add
An argument to be passed to
lavaan::lavTestScore(). Please refer to the help page oflavaan::lavTestScore().- release
An argument to be passed to
lavaan::lavTestScore(). Please refer to the help page oflavaan::lavTestScore().- univariate
An argument to be passed to
lavaan::lavTestScore(). Please refer to the help page oflavaan::lavTestScore().- information
An argument to be passed to
lavaan::lavTestScore(). Please refer to the help page oflavaan::lavTestScore().- constraints
An argument to be passed to
lavaan::lavTestWald(). Please refer to the help page oflavaan::lavTestWald().- prefix
Optional. A character string to be added as a prefix to names of the output. Default is
NULL.
Details
This set of wrapper
functions are for functions like
lavaan::bootstrapLavaan() that
require users to supply a function
that receives
a lavaan-class object and returns
a vector of values.
All wrappers functions are designed to have the same form of output: a named numeric vector.
Many of the tasks of this set of wrappers can be performed by writing our own functions. The wrapper functions are developed just to save the coding time for some commonly requested information.
The wrapper functions are designed to be as simple to use as possible, with as few arguments as possible. If advanced control is needed, users are recommended to write their own wrappers.
Functions
vec_rsquare(): Get R-squares in a model.vec_sample_vcov(): Get sample variances and covariances.vec_sample_var(): Get sample variances.vec_est_var(): Sampling variances of free parameters.vec_est_se(): Standard errors of free parameters.vec_def_var(): Sampling variances of user-defined parameters.vec_def_se(): Standard errors of user-defined parameters.vec_lavTestLRT(): Get sample variances.vec_lavTestScore(): Do score tests.vec_lavTestWald(): Do a Wald test.vec_compRelSEM(): Composite reliability.
Author
Shu Fai Cheung https://orcid.org/0000-0002-9871-9448
Examples
# From the help page of lavaan::cfa().
library(lavaan)
HS.model <- '
visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9
'
fit <- cfa(HS.model, data = HolzingerSwineford1939)
vec_rsquare(fit)
#> x1_rsq x2_rsq x3_rsq x4_rsq x5_rsq x6_rsq x7_rsq x8_rsq
#> 0.5957994 0.1794378 0.3377148 0.7251923 0.7311369 0.7022609 0.3243470 0.5227933
#> x9_rsq
#> 0.4422373
vec_sample_vcov(fit)
#> x1~~x1 x1~~x2 x1~~x3 x1~~x4 x1~~x5 x1~~x6
#> 1.35836985 0.40737133 0.57989932 0.50483500 0.44061554 0.45480813
#> x1~~x7 x1~~x8 x1~~x9 x2~~x2 x2~~x3 x2~~x4
#> 0.08476543 0.26383768 0.45833855 1.38178387 0.45106394 0.20892337
#> x2~~x5 x2~~x6 x2~~x7 x2~~x8 x2~~x9 x3~~x3
#> 0.21109108 0.24754457 -0.09675102 0.10965939 0.24400945 1.27486486
#> x3~~x4 x3~~x5 x3~~x6 x3~~x7 x3~~x8 x3~~x9
#> 0.20816961 0.11229629 0.24410898 0.08834184 0.21232264 0.37385257
#> x4~~x4 x4~~x5 x4~~x6 x4~~x7 x4~~x8 x4~~x9
#> 1.35066451 1.09775278 0.89551571 0.21974259 0.12559335 0.24336274
#> x5~~x5 x5~~x6 x5~~x7 x5~~x8 x5~~x9 x6~~x6
#> 1.65978576 1.01452404 0.14299955 0.18060521 0.29524133 1.19635838
#> x6~~x7 x6~~x8 x6~~x9 x7~~x7 x7~~x8 x7~~x9
#> 0.14407839 0.16543100 0.23599695 1.18313946 0.53524522 0.37329722
#> x8~~x8 x8~~x9 x9~~x9
#> 1.02198281 0.45731656 1.01500387
vec_sample_var(fit)
#> x1_var x2_var x3_var x4_var x5_var x6_var x7_var x8_var
#> 1.358370 1.381784 1.274865 1.350665 1.659786 1.196358 1.183139 1.021983
#> x9_var
#> 1.015004
vec_est_var(fit)
#> visual=~x2 visual=~x3 textual=~x5 textual=~x6
#> 0.009933136 0.011904927 0.004279791 0.003074576
#> speed=~x8 speed=~x9 x1~~x1 x2~~x2
#> 0.027220569 0.022851594 0.012905170 0.010347644
#> x3~~x3 x4~~x4 x5~~x5 x6~~x6
#> 0.008212561 0.002276988 0.003409716 0.001852008
#> x7~~x7 x8~~x8 x9~~x9 visual~~visual
#> 0.006622958 0.005504763 0.005003714 0.021159312
#> textual~~textual speed~~speed visual~~textual visual~~speed
#> 0.012567721 0.007432025 0.005405761 0.003167033
#> textual~~speed
#> 0.002431936
vec_est_se(fit)
#> visual=~x2 visual=~x3 textual=~x5 textual=~x6
#> 0.09966512 0.10910970 0.06542011 0.05544886
#> speed=~x8 speed=~x9 x1~~x1 x2~~x2
#> 0.16498657 0.15116744 0.11360092 0.10172337
#> x3~~x3 x4~~x4 x5~~x5 x6~~x6
#> 0.09062318 0.04771779 0.05839278 0.04303497
#> x7~~x7 x8~~x8 x9~~x9 visual~~visual
#> 0.08138156 0.07419409 0.07073694 0.14546241
#> textual~~textual speed~~speed visual~~textual visual~~speed
#> 0.11210585 0.08620919 0.07352388 0.05627640
#> textual~~speed
#> 0.04931466
HS.model.sem1 <- '
visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9
textual ~ a * visual
speed ~ b * textual
ab := a * b
'
fit_sem1 <- sem(HS.model.sem1, data = HolzingerSwineford1939)
HS.model.sem2 <- '
visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9
textual ~ a * visual
speed ~ b * textual + cp * visual
ab := a * b
'
fit_sem2 <- sem(HS.model.sem2, data = HolzingerSwineford1939)
vec_def_var(fit_sem1)
#> ab
#> 0.0009906484
vec_def_se(fit_sem1)
#> ab
#> 0.03147457
vec_lavTestLRT(fit_sem1, fit_sem2,
model.names = c("No Direct", "Direct"))
#> chisq_diff.Direct_vs_No Direct df_diff.Direct_vs_No Direct
#> 1.810531e+01 1.000000e+00
#> chisq_p.Direct_vs_No Direct
#> 2.090176e-05
vec_lavTestScore(fit_sem1,
add = "speed ~ visual")
#> score_chisq score_df score_p speed~visual.chisq
#> 1.584567e+01 1.000000e+00 6.872365e-05 1.584567e+01
#> speed~visual.df speed~visual.p
#> 1.000000e+00 6.872365e-05
vec_lavTestWald(fit_sem2,
constraints = "cp == 0")
#> wald_stat wald_df wald_p
#> 1.444631e+01 1.000000e+00 1.442117e-04
if (requireNamespace("semTools")) {
vec_compRelSEM(fit)
}
#> visual_rel textual_rel speed_rel
#> 0.612 0.885 0.686