Sort a parameter
estimates table or a similar table
inlavaan by common fields such as
op (operator) and lhs (left-
hand side).
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.- by
A character vector of the columns for filtering. Default is
c("op", "lhs", "rhs").- op_priority
How rows are sorted by
op. Default isc("=~", "~", "~~", ":=", "~1", "|", "~*~"). Can set only a few of the operators, e.g.,c("~", "~~"). Other operators will be placed to the end with orders not changed.- number_rows
Whether the row names will be set to row numbers after sorting if the row names of
objectis equal to row numbers. Default isTRUE.
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)
model1 <-
'
m ~ a*x
y ~ b*m
ab := a*b
'
fit1 <- sem(model1, data = dat, fixed.x = FALSE)
model2 <-
'
m ~ a*x
y ~ b*m + x
ab := a*b
'
fit2 <- sem(model2, data = dat, fixed.x = FALSE)
parameterEstimates(fit1)
#> lhs op rhs label est se z pvalue ci.lower ci.upper
#> 1 m ~ x a 0.569 0.343 1.660 0.097 -0.103 1.240
#> 2 y ~ m b 0.219 0.153 1.430 0.153 -0.081 0.519
#> 3 m ~~ m 0.460 0.092 5.000 0.000 0.280 0.641
#> 4 y ~~ y 0.570 0.114 5.000 0.000 0.347 0.794
#> 5 x ~~ x 0.078 0.016 5.000 0.000 0.048 0.109
#> 6 ab := a*b ab 0.125 0.115 1.083 0.279 -0.101 0.350
parameterEstimates(fit2)
#> lhs op rhs label est se z pvalue ci.lower ci.upper
#> 1 m ~ x a 0.569 0.343 1.660 0.097 -0.103 1.240
#> 2 y ~ m b 0.214 0.157 1.360 0.174 -0.094 0.522
#> 3 y ~ x 0.056 0.392 0.142 0.887 -0.712 0.823
#> 4 m ~~ m 0.460 0.092 5.000 0.000 0.280 0.641
#> 5 y ~~ y 0.570 0.114 5.000 0.000 0.347 0.793
#> 6 x ~~ x 0.078 0.016 5.000 0.000 0.048 0.109
#> 7 ab := a*b ab 0.122 0.116 1.052 0.293 -0.105 0.348
out <- group_by_models(list(no_direct = fit1,
direct = fit2),
col_names = c("est", "pvalue"))
out
#> lhs op rhs est_no_direct est_direct pvalue_no_direct pvalue_direct
#> 1 m ~ x 0.569 0.569 0.097 0.097
#> 2 y ~ m 0.219 0.214 0.153 0.174
#> 3 y ~ x NA 0.056 NA 0.887
#> 4 m ~~ m 0.460 0.460 0.000 0.000
#> 5 x ~~ x 0.078 0.078 0.000 0.000
#> 6 y ~~ y 0.570 0.570 0.000 0.000
#> 7 ab := a*b 0.125 0.122 0.279 0.293
sort_by(out)
#> lhs op rhs est_no_direct est_direct pvalue_no_direct pvalue_direct
#> 1 m ~ x 0.569 0.569 0.097 0.097
#> 2 y ~ m 0.219 0.214 0.153 0.174
#> 3 y ~ x NA 0.056 NA 0.887
#> 4 m ~~ m 0.460 0.460 0.000 0.000
#> 5 x ~~ x 0.078 0.078 0.000 0.000
#> 6 y ~~ y 0.570 0.570 0.000 0.000
#> 7 ab := a*b 0.125 0.122 0.279 0.293
sort_by(out, op_priority = c("~", ":="))
#> lhs op rhs est_no_direct est_direct pvalue_no_direct pvalue_direct
#> 1 m ~ x 0.569 0.569 0.097 0.097
#> 2 y ~ m 0.219 0.214 0.153 0.174
#> 3 y ~ x NA 0.056 NA 0.887
#> 4 ab := a*b 0.125 0.122 0.279 0.293
#> 5 m ~~ m 0.460 0.460 0.000 0.000
#> 6 x ~~ x 0.078 0.078 0.000 0.000
#> 7 y ~~ y 0.570 0.570 0.000 0.000
sort_by(out, by = c("op", "rhs"))
#> lhs op rhs est_no_direct est_direct pvalue_no_direct pvalue_direct
#> 1 y ~ m 0.219 0.214 0.153 0.174
#> 2 m ~ x 0.569 0.569 0.097 0.097
#> 3 y ~ x NA 0.056 NA 0.887
#> 4 m ~~ m 0.460 0.460 0.000 0.000
#> 5 x ~~ x 0.078 0.078 0.000 0.000
#> 6 y ~~ y 0.570 0.570 0.000 0.000
#> 7 ab := a*b 0.125 0.122 0.279 0.293