Groups parameter estimates or other information such as p-values into a table with groups as columns and parameters as rows.
Usage
group_by_groups(
object,
...,
col_names = "est",
group_first = TRUE,
group_labels = NULL,
fit = NULL,
use_standardizedSolution = FALSE
)Arguments
- object
A 'lavaan'-class object or the output of
lavaan::parameterEstimates()orlavaan::standardizedSolution().- ...
Optional arguments to be passed to
lavaan::parameterEstimates(). Ignored if object is an output oflavaan::parameterEstimates()orlavaan::standardizedSolution().- col_names
A vector of the column names in the parameter estimate tables to be included. Default is
"est".- group_first
If
TRUE, the columns will be grouped by groups first and then by columns in the parameter estimates tables. Default isTRUE.- group_labels
A character vector of group labels. Will be assigned to group id = 1, 2, 3, etc. If not provided. will try to be retrieved from
objectif it is a lavaan::lavaan object.- fit
Optional. A lavaan::lavaan object. If
objectis a parameter estimates table andgroup_labelsisNULL, it will try to retrieve the group labels fromfitis supplied.- use_standardizedSolution
If
TRUEandobjectis not an estimates table, thenlavaan::standardizedSolution()will be used to generate the table. IfFALSE, the default, thenlavaan::parameterEstimates()will be used if necessary.
Author
Shu Fai Cheung https://orcid.org/0000-0002-9871-9448
Examples
library(lavaan)
set.seed(5478374)
n <- 100
x <- runif(n) - .5
m <- .40 * x + rnorm(n, 0, sqrt(1 - .40))
y <- .30 * m + rnorm(n, 0, sqrt(1 - .30))
city <- sample(c("City Alpha", "City Beta"), 100,
replace = TRUE)
dat <- data.frame(x = x, y = y, m = m, city = city)
model <-
'
m ~ c(a1, a2)*x
y ~ c(b1, b2)*m
a1b1 := a1*b1
a2b2 := a2*b2
'
fit <- sem(model, data = dat, fixed.x = FALSE,
group = "city")
(est <- parameterEstimates(fit))
#> lhs op rhs block group label est se z pvalue ci.lower ci.upper
#> 1 m ~ x 1 1 a1 0.636 0.454 1.401 0.161 -0.253 1.526
#> 2 y ~ m 1 1 b1 0.469 0.153 3.055 0.002 0.168 0.769
#> 3 m ~~ m 1 1 0.530 0.107 4.950 0.000 0.320 0.740
#> 4 y ~~ y 1 1 0.636 0.128 4.950 0.000 0.384 0.887
#> 5 x ~~ x 1 1 0.052 0.011 4.950 0.000 0.032 0.073
#> 6 m ~1 1 1 -0.144 0.108 -1.335 0.182 -0.355 0.067
#> 7 y ~1 1 1 -0.092 0.117 -0.786 0.432 -0.322 0.138
#> 8 x ~1 1 1 -0.062 0.033 -1.882 0.060 -0.126 0.003
#> 9 m ~ x 2 2 a2 0.418 0.271 1.540 0.124 -0.114 0.950
#> 10 y ~ m 2 2 b2 0.277 0.147 1.880 0.060 -0.012 0.565
#> 11 m ~~ m 2 2 0.413 0.082 5.050 0.000 0.253 0.574
#> 12 y ~~ y 2 2 0.478 0.095 5.050 0.000 0.292 0.663
#> 13 x ~~ x 2 2 0.110 0.022 5.050 0.000 0.067 0.153
#> 14 m ~1 2 2 -0.179 0.091 -1.961 0.050 -0.357 0.000
#> 15 y ~1 2 2 -0.125 0.100 -1.253 0.210 -0.320 0.070
#> 16 x ~1 2 2 0.051 0.046 1.103 0.270 -0.040 0.142
#> 17 a1b1 := a1*b1 0 0 a1b1 0.298 0.234 1.274 0.203 -0.161 0.757
#> 18 a2b2 := a2*b2 0 0 a2b2 0.116 0.097 1.191 0.234 -0.075 0.306
# Group them by groups
group_by_groups(fit)
#> lhs op rhs est_City.Beta est_City.Alpha
#> 1 m ~ x 0.636 0.418
#> 2 y ~ m 0.469 0.277
#> 3 m ~~ m 0.530 0.413
#> 4 x ~~ x 0.052 0.110
#> 5 y ~~ y 0.636 0.478
#> 6 m ~1 -0.144 -0.179
#> 7 x ~1 -0.062 0.051
#> 8 y ~1 -0.092 -0.125
# Can also work on a parameter estimates table
# To have group labels, need to supply the fit object
group_by_groups(est, fit = fit)
#> lhs op rhs est_City.Beta est_City.Alpha
#> 1 m ~ x 0.636 0.418
#> 2 y ~ m 0.469 0.277
#> 3 m ~~ m 0.530 0.413
#> 4 x ~~ x 0.052 0.110
#> 5 y ~~ y 0.636 0.478
#> 6 m ~1 -0.144 -0.179
#> 7 x ~1 -0.062 0.051
#> 8 y ~1 -0.092 -0.125
# Can be used with some other functions in semhelpinghands
# when used on a parameter estimates table
group_by_groups(filter_by(est, op = "~"), fit = fit)
#> lhs op rhs est_City.Beta est_City.Alpha
#> 1 m ~ x 0.636 0.418
#> 2 y ~ m 0.469 0.277
# Also support piping
est |> filter_by(op = "~") |>
group_by_groups(fit = fit)
#> lhs op rhs est_City.Beta est_City.Alpha
#> 1 m ~ x 0.636 0.418
#> 2 y ~ m 0.469 0.277