It inserts columns to denote whether a parameter is significant.
Arguments
- object
A 'lavaan'-class object or the output of
lavaan::parameterEstimates()orlavaan::standardizedSolution(). May also work on anest_table-class object returned by functions likegroup_by_dvs()but there is no guarantee.- ...
Optional arguments to be passed to
lavaan::parameterEstimates()orlavaan::standardizedSolution().- standardized
Whether standardized solution is needed. If
TRUE,lavaan::standardizedSolution()will be called. IfFALSE, the default,lavaan::parameterEstimates()will be called. Ignored if a table if estimates is supplied.- na_str
The string to be used for parameters with no significant tests. For example, fixed parameters. Default is
"".- use
A character vector of one or more strings. If
"pvalue"is in the vector, p-values will be used. If"ci"is in the vector, confidence intervals appeared inci.lowerandci.upperwill be used. If"boot.ci"is in the vector and the columnsboot.ci.lowerandboot.ci.upperare available, these columns will be used. Note thatci.lowerandci.uppercan also be bootstrap confidence intervals in some tables ifse = "boot"is used.
Value
The output of
lavaan::parameterEstimates() or
lavaan::standardizedSolution(),
with one or two columns inserted
after the parameter estimates to
denote the significant test results.
Details
The function calls
lavaan::parameterEstimates() or
lavaan::standardizedSolution() and
checks the columns pvalue,
ci.lower and ci.upper, and/or
boot.ci.lower and boot.ci.upper
and then inserts columns to denote
for each parameter estimate whether
it is significant based on the
requested criteria.
Author
Shu Fai Cheung https://orcid.org/0000-0002-9871-9448
Examples
library(lavaan)
#> This is lavaan 0.6-19
#> lavaan is FREE software! Please report any bugs.
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)
# Add "*" based on 'pvalue'
add_sig(fit)
#> lhs op rhs label est sig 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
# Add "*" for standardized solution
add_sig(fit, standardized = TRUE)
#> lhs op rhs label est.std sig se z pvalue ci.lower ci.upper
#> 1 m ~ x a 0.229 0.134 1.706 0.088 -0.034 0.491
#> 2 y ~ m b 0.198 0.136 1.459 0.145 -0.068 0.464
#> 3 m ~~ m 0.948 *** 0.061 15.466 0.000 0.828 1.068
#> 4 y ~~ y 0.961 *** 0.054 17.840 0.000 0.855 1.066
#> 5 x ~~ x 1.000 0.000 NA NA 1.000 1.000
#> 6 ab := a*b ab 0.045 0.041 1.096 0.273 -0.036 0.126
# Add "*" based on confidence interval
add_sig(fit, use = "ci")
#> lhs op rhs label est ci 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 Sig 0.092 5.000 0.000 0.280 0.641
#> 4 y ~~ y 0.570 Sig 0.114 5.000 0.000 0.347 0.794
#> 5 x ~~ x 0.078 Sig 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
# Add "*" for standardized solution based on confidence interval
add_sig(fit, standardized = TRUE, use = "ci")
#> lhs op rhs label est.std ci se z pvalue ci.lower ci.upper
#> 1 m ~ x a 0.229 0.134 1.706 0.088 -0.034 0.491
#> 2 y ~ m b 0.198 0.136 1.459 0.145 -0.068 0.464
#> 3 m ~~ m 0.948 Sig 0.061 15.466 0.000 0.828 1.068
#> 4 y ~~ y 0.961 Sig 0.054 17.840 0.000 0.855 1.066
#> 5 x ~~ x 1.000 0.000 NA NA 1.000 1.000
#> 6 ab := a*b ab 0.045 0.041 1.096 0.273 -0.036 0.126
# Add "*" for standardized solution based on confidence interval
# and 'pvalue'.
add_sig(fit, standardized = TRUE, use = c("ci", "pvalue"))
#> lhs op rhs label est.std sig ci se z pvalue ci.lower ci.upper
#> 1 m ~ x a 0.229 0.134 1.706 0.088 -0.034 0.491
#> 2 y ~ m b 0.198 0.136 1.459 0.145 -0.068 0.464
#> 3 m ~~ m 0.948 *** Sig 0.061 15.466 0.000 0.828 1.068
#> 4 y ~~ y 0.961 *** Sig 0.054 17.840 0.000 0.855 1.066
#> 5 x ~~ x 1.000 0.000 NA NA 1.000 1.000
#> 6 ab := a*b ab 0.045 0.041 1.096 0.273 -0.036 0.126
# Can also accept a parameter estimates table
est <- parameterEstimates(fit)
add_sig(est)
#> lhs op rhs label est sig 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
# So it can be used with some other functions in semhelpinghands
add_sig(filter_by(est, op = "~"))
#> lhs op rhs label est sig 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
# Piping can also be used
est |> filter_by(op = "~") |>
add_sig()
#> lhs op rhs label est sig 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