kutils (version 0.93)

compareCFA: compareCFA

Description

Compare CFA Tables

Usage

compareCFA(models, fitmeas = c("chisq", "df", "pvalue", "rmsea", "cfi", "tli",
  "srmr", "aic", "bic"), nesting = NULL, scaled = TRUE, chidif = TRUE,
  file = NULL)

Arguments

models

list of lavaan cfa models to be compared. The models can be named in the elements of the list.

fitmeas

vector of fit measures on which to compare the models. By default, fitmeas = c("chisq", "df", "pvalue", "rmsea", "cfi", "tli", "srmr", "aic", "bic", "srmr", "aic", "bic", "srmr_mplus"). Fit measures that are request but not found are ignored.

nesting

character string indicating the nesting structure of the models. Must only contain model names, ">", and "+" separated by spaces. The model to the left of a ">" is the parent model for all models to the right of the same ">", up until another ">" is reached. When multiple models are nested in the same parent, they are separated by a "+".

scaled

should scaled versions of the fit measures requested be used if available? The scaled statistic is determined by the model estimation method. The defaul value is TRUE.

chidif

should the nested models be compared by using the anova function? The anova function may pass the model comparison on to another lavaan function. The results are added to the last three columns of the comparison table. The default value is TRUE.

file

Default is NULL, no file created. If output file is desired, provide a character string for the file name.

Examples

## These run longer than 5 seconds
library(lavaan)
library(xtable)
set.seed(123)
genmodel <- "f1 =~ .7*v1 + .7*v2 + .7*v3 + .7*v4 + .7*v5 + .7*v6
f1 ~~ 1*f1"
genmodel2 <- "f1 =~ .7*v1 + .7*v2 + .7*v3 + .7*v4 + .7*v5 + .2*v6
f1 ~~ 1*f1"

dat1 <- simulateData(genmodel, sample.nobs = 300)
dat2 <- simulateData(genmodel2, sample.nobs = 300)
dat1$group <- 0
dat2$group <- 1
dat <- rbind(dat1, dat2)

congModel <- "
              f1 =~ 1*v1 + v2 + v3 + v4 + v5 + v6
    		  f1 ~~ f1
    		  f1 ~0*1
    		 "
weakModel <- "
              f1 =~ 1*v1 + c(L2,L2)*v2 + c(L3,L3)*v3 + c(L4,L4)*v4 + c(L5,L5)*v5 + c(L6,L6)*v6
    		  f1 ~~ f1
    		  f1 ~0*1
    		"
partialweakModel <- "
              f1 =~ 1*v1 + c(L2,L2)*v2 + c(L3,L3)*v3 + c(L4,L4)*v4 + c(L5,L5)*v5 + v6
    		  f1 ~~ f1
    		  f1 ~0*1
    		"
partialweakModel2 <- "
              f1 =~ 1*v1 + c(L2,L2)*v2 + c(L3,L3)*v3 + c(L4,L4)*v4 + v5 + v6
    		  f1 ~~ f1
    		  f1 ~0*1
    		"
partialstrongModel1 <- "
              f1 =~ 1*v1 + c(L2,L2)*v2 + c(L3,L3)*v3 + c(L4,L4)*v4 + c(L5,L5)*v5 + v6
    		  f1 ~~ f1
    		  f1 ~ c(0,NA)*1
    		  v1 ~ c(I1,I1)*1
    		  v2 ~ c(I2,I2)*1
    		  v3 ~ c(I3,I3)*1
    		  v4 ~ c(I4,I4)*1
    		  v5 ~ c(I5,I5)*1
    		  v6 ~ c(I6,I6)*1
    		"
cc1 <- cfa(congModel, data=dat, group="group", meanstructure=TRUE, estimator = "MLR")
cc2 <- cfa(weakModel, data=dat, group="group", meanstructure=TRUE, estimator = "MLR")
cc21 <- cfa(partialweakModel, data=dat, group="group", meanstructure=TRUE, estimator = "MLR")
cc3 <- cfa(partialstrongModel1, data=dat, group="group", meanstructure=TRUE, estimator = "MLR")

models <- list(cc1, cc2, cc21, cc3)
compareCFA(models, nesting = NULL)

models <- list("Configural" = cc1, "Metric" = cc2, "PartialMetric" = cc21, "Scalar" = cc3)
compareCFA(models, nesting = "Configural > Metric + PartialMetric > Scalar")

compareCFA(models, fitmeas = c("chisq", "df", "cfi", "rmsea", "tli"),
nesting = "Configural > Metric + PartialMetric > Scalar")

## Creates output file
## compareCFA(models, fitmeas = c("chisq", "df", "cfi", "rmsea", "tli"),
## nesting = "Configural > Metric + PartialMetric > Scalar", file = "table.tex")