Skip to contents

Compare Model Fit of Nested Dyadic Invariance Models

Source: R/outputInvarCompTab.R
outputInvarCompTab.Rd

outputInvarCompTab() is used to compare the model fit of nested dyadic invariance models in order from most parsimonious (residual) to least parsimonious (configural)

Usage

outputInvarCompTab(mods, gtTab = FALSE, writeTo = NULL, fileName = NULL)

Arguments

mods

A list of nested lavaan dyadic invariance models, in the order of residual, intercept, loading, configural

gtTab

A logical input indicating whether to generate the output in gt::gt() table object format (TRUE). By default (FALSE), the output is generated in tibble::tibble() format. Users can also apply the writeTo argument if they wish to export the gt:gt() table object.

writeTo

A character string specifying a directory path to where the gt::gt() table object should be saved. If set to ".", the file will be written to the current working directory. The default is NULL, and examples use a temporary directory created by tempdir(). writeTo is only relevant if gtTab = TRUE.

fileName

A character string specifying a desired base name for the output gt::gt() file. If a fileName is not provided (i.e., fileName = NULL), then a default will be used (i.e., "dySEM_table"). The resulting base name will automatically be appended with a .rtf file extension. fileName is only relevant if gtTab = TRUE and writeTo is specified.

Value

A list. More specifically, a tibble()—if gtTab = FALSE (default)—or gt::gt() object—if gtTab = TRUE—of model fit statistics for each model, as well as the difference in fit statistics between each model and the previous model

Details

  • If gtTab = TRUE and writeTo is specified, then output will simultaneously be saved as a .rtf file to the user's specified directory.

  • If output file is successfully saved, a confirmation message will be printed to the console.

  • If a file with the same name already exists in the user's chosen directory, it will be overwritten.

Examples


dvn <- scrapeVarCross(dat = commitmentQ, x_order = "spi",
x_stem = "sat.g", x_delim1 = ".", x_delim2="_", distinguish_1="1", distinguish_2="2")

sat.residual.script <- scriptCFA(dvn, lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts", "residuals"), constr_dy_struct = "none")

sat.intercept.script <- scriptCFA(dvn, lvname = "Sat",
constr_dy_meas = c("loadings", "intercepts"), constr_dy_struct = "none")

sat.loading.script <- scriptCFA(dvn, lvname = "Sat",
constr_dy_meas = c("loadings"), constr_dy_struct = "none")

sat.config.script <- scriptCFA(dvn, lvname = "Sat",
constr_dy_meas = "none", constr_dy_struct = "none")

sat.residual.fit <- lavaan::cfa(sat.residual.script, data = commitmentQ,
std.lv = FALSE, auto.fix.first= FALSE, meanstructure = TRUE)

sat.intercept.fit <- lavaan::cfa(sat.intercept.script, data = commitmentQ,
std.lv = FALSE, auto.fix.first= FALSE, meanstructure = TRUE)

sat.loading.fit <- lavaan::cfa(sat.loading.script, data = commitmentQ,
std.lv = FALSE, auto.fix.first= FALSE, meanstructure = TRUE)

sat.config.fit <- lavaan::cfa(sat.config.script, data = commitmentQ,
std.lv = FALSE, auto.fix.first= FALSE, meanstructure = TRUE)

mods <- list(sat.residual.fit, sat.intercept.fit, sat.loading.fit, sat.config.fit)

outputInvarCompTab(mods,
gtTab = TRUE, writeTo = tempdir(), fileName = "dCFA_Invar")
#> Output stored in: /tmp/Rtmpu7vXEu/dCFA_Invar.rtf



  
mod
      chisq
      df
      pvalue
      aic
      bic
      rmsea
      cfi
      chisq_diff
      df_diff
      p_diff
      aic_diff
      bic_diff
      rmsea_diff
      cfi_diff
    
residual
78.842
42
0.000
3855.175
3918.308
0.087
0.970
NA
NA
NA
NA
NA
NA
NA
intercept
60.321
37
0.009
3846.654
3923.512
0.074
0.981
-18.521
-5
0.002
-8.521
5.204
-0.013
0.011
loading
58.810
33
0.004
3853.143
3940.981
0.082
0.979
-1.511
-4
0.825
6.489
17.469
0.008
-0.002
configural
53.026
29
0.004
3855.359
3954.176
0.085
0.981
-5.784
-4
0.216
2.216
13.196
0.002
0.001