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Add Significant Test Results

Source: R/add_sig.R
add_sig.Rd

It inserts columns to denote whether a parameter is significant.

Usage

add_sig(object, ..., standardized = FALSE, na_str = "", use = "pvalue")

Arguments

object

A lavaan object or the output of lavaan::parameterEstimates() or lavaan::standardizedSolution(). May also work on an est_table-class object returned by functions like group_by_dvs() but there is no guarantee.

...

Optional arguments to be passed to lavaan::parameterEstimates() or lavaan::standardizedSolution().

standardized

Whether standardized solution is needed. If TRUE, lavaan::standardizedSolution() will be called. If FALSE, 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 in ci.lower and ci.upper will be used. If "boot.ci" is in the vector and the columns boot.ci.lower and boot.ci.upper are available, these columns will be used. Note that ci.lower and ci.upper can also be bootstrap confidence intervals in some tables if se = "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.

See also

lavaan::parameterEstimates() and lavaan::standardizedSolution()

Author

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

Examples


library(lavaan)
#> This is lavaan 0.6-17
#> 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