Filter parameter
estimates table and similar tables
inlavaan by common fields such as
op (operator).
Arguments
- object
The output of
lavaan::parameterEstimates(),lavaan::standardizedSolution(), or alavaan.data.frameobject. May also work on anest_table-class object returned by functions likegroup_by_dvs()but there is no guarantee.- op
A character vector of the operators (
op) for filtering. Common operators are"~","~~","=~",":=", and "~1".- lhs
A character vector of names in the
lhscolumn.- rhs
A character vector of names in the
rhscolumn.- group
A vector of either the group numbers in the
groupcolumn of the labels of the groups. If labels are supplied, the original fit object must be supplied for extracting the group labels.- fit
The original fit object. Usd when
groupis a vector of the group labels.
Details
This functions accepts the
output of
lavaan::parameterEstimates() and
lavaan::standardizedSolution() and
filter the rows by commonly used
field.
Author
Shu Fai Cheung https://orcid.org/0000-0002-9871-9448
Examples
library(lavaan)
set.seed(5478374)
n <- 50
x <- runif(n) - .5
m <- .40 * x + rnorm(n, 0, sqrt(1 - .40))
y <- .30 * m + rnorm(n, 0, sqrt(1 - .30))
dat <- data.frame(x = x, y = y, m = m)
model <-
'
m ~ a*x
y ~ b*m
ab := a*b
'
fit <- sem(model, data = dat, fixed.x = FALSE)
model_gp <-
'
m ~ c(a1, a2)*x
y ~ c(b1, b2)*m
a1b1 := a1*b1
a2b2 := a2*b2
'
dat$gp <- sample(c("gp1", "gp2"), n, replace = TRUE)
fit_gp <- sem(model_gp, dat, group = "gp", warn = FALSE)
est <- parameterEstimates(fit)
est_gp <- parameterEstimates(fit_gp)
filter_by(est, op = "~")
#> lhs op rhs label est se z pvalue ci.lower ci.upper
#> 1 m ~ x a 0.569 0.343 1.66 0.097 -0.103 1.240
#> 2 y ~ m b 0.219 0.153 1.43 0.153 -0.081 0.519
filter_by(est, op = "~", lhs = "y")
#> lhs op rhs label est se z pvalue ci.lower ci.upper
#> 2 y ~ m b 0.219 0.153 1.43 0.153 -0.081 0.519
filter_by(est, rhs = c("m", "x"), op = "~")
#> lhs op rhs label est se z pvalue ci.lower ci.upper
#> 1 m ~ x a 0.569 0.343 1.66 0.097 -0.103 1.240
#> 2 y ~ m b 0.219 0.153 1.43 0.153 -0.081 0.519
filter_by(est_gp, group = 2)
#> lhs op rhs block group label est se z pvalue ci.lower ci.upper
#> 9 m ~ x 2 2 a2 0.574 0.417 1.375 0.169 -0.244 1.391
#> 10 y ~ m 2 2 b2 0.206 0.231 0.892 0.372 -0.246 0.658
#> 11 m ~~ m 2 2 0.389 0.104 3.742 0.000 0.185 0.593
#> 12 y ~~ y 2 2 0.619 0.166 3.742 0.000 0.295 0.944
#> 13 x ~~ x 2 2 0.080 0.000 NA NA 0.080 0.080
#> 14 m ~1 2 2 -0.144 0.119 -1.206 0.228 -0.377 0.090
#> 15 y ~1 2 2 -0.297 0.154 -1.933 0.053 -0.598 0.004
#> 16 x ~1 2 2 -0.042 0.000 NA NA -0.042 -0.042
# If the fit object is supplied, can filter
# by group label
filter_by(est_gp, group = "gp2", fit = fit_gp)
#> lhs op rhs block group label est se z pvalue ci.lower ci.upper
#> 1 m ~ x 1 1 a1 0.526 0.569 0.925 0.355 -0.589 1.640
#> 2 y ~ m 1 1 b1 0.211 0.201 1.050 0.294 -0.183 0.605
#> 3 m ~~ m 1 1 0.540 0.163 3.317 0.001 0.221 0.859
#> 4 y ~~ y 1 1 0.499 0.150 3.317 0.001 0.204 0.793
#> 5 x ~~ x 1 1 0.076 0.000 NA NA 0.076 0.076
#> 6 m ~1 1 1 -0.004 0.157 -0.024 0.981 -0.311 0.303
#> 7 y ~1 1 1 -0.168 0.151 -1.113 0.266 -0.463 0.128
#> 8 x ~1 1 1 -0.007 0.000 NA NA -0.007 -0.007
filter_by(est_gp, group = "gp2", fit = fit_gp, op = "~")
#> lhs op rhs block group label est se z pvalue ci.lower ci.upper
#> 1 m ~ x 1 1 a1 0.526 0.569 0.925 0.355 -0.589 1.640
#> 2 y ~ m 1 1 b1 0.211 0.201 1.050 0.294 -0.183 0.605
# Select user-defined parameters
filter_by(est_gp, op = ":=")
#> lhs op rhs block group label est se z pvalue ci.lower ci.upper
#> 17 a1b1 := a1*b1 0 0 a1b1 0.111 0.160 0.694 0.488 -0.203 0.425
#> 18 a2b2 := a2*b2 0 0 a2b2 0.118 0.158 0.748 0.454 -0.191 0.427
# Can be used with some other functions in semhelpinghands
# Piping can also be used
est_gp |> filter_by(op = "~", group = "gp2", fit = fit_gp) |>
add_sig()
#> lhs op rhs block group label est sig se z pvalue ci.lower ci.upper
#> 1 m ~ x 1 1 a1 0.526 0.569 0.925 0.355 -0.589 1.640
#> 2 y ~ m 1 1 b1 0.211 0.201 1.050 0.294 -0.183 0.605