This is lavaan 0.6-19
lavaan is FREE software! Please report any bugs.
# Example covariance matrices for two groupsgroup1_cov <-matrix(c(1.0, 0.5, 0.3,0.5, 1.0, 0.4,0.3, 0.4, 1.0), nrow =3, byrow =TRUE)group2_cov <-matrix(c(1.2, 0.6, 0.4,0.6, 1.1, 0.5,0.4, 0.5, 1.3), nrow =3, byrow =TRUE)# Add row and column names to match variable names in the modelvar_names <-c("y1", "y2", "y3")colnames(group1_cov) <- var_namesrownames(group1_cov) <- var_namescolnames(group2_cov) <- var_namesrownames(group2_cov) <- var_names# Example means for each groupgroup1_means <-c(2.5, 3.0, 2.8)group2_means <-c(2.7, 3.1, 2.9)names(group1_means) <- var_namesnames(group2_means) <- var_names# Sample sizes for each groupgroup1_n <-100group2_n <-150# Model specificationmodel <-' factor1 =~ y1 + y2 + y3'# Fit the model using summary data and include group meansfit_configural <-sem(model,sample.cov =list(group1_cov, group2_cov),sample.mean =list(group1_means, group2_means), # Add group meanssample.nobs =list(group1_n, group2_n),group.equal =NULL)# Summary of the resultssummary(fit_configural, fit.measures =TRUE)
lavaan 0.6-19 ended normally after 38 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 18
Number of observations per group:
Group 1 100
Group 2 150
Model Test User Model:
Test statistic 0.000
Degrees of freedom 0
Test statistic for each group:
Group 1 0.000
Group 2 0.000
Model Test Baseline Model:
Test statistic 127.014
Degrees of freedom 6
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000
Tucker-Lewis Index (TLI) 1.000
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1038.184
Loglikelihood unrestricted model (H1) -1038.184
Akaike (AIC) 2112.368
Bayesian (BIC) 2175.755
Sample-size adjusted Bayesian (SABIC) 2118.693
Root Mean Square Error of Approximation:
RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.000
P-value H_0: RMSEA <= 0.050 NA
P-value H_0: RMSEA >= 0.080 NA
Standardized Root Mean Square Residual:
SRMR 0.000
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Group 1 [Group 1]:
Latent Variables:
Estimate Std.Err z-value P(>|z|)
factor1 =~
y1 1.000
y2 1.333 0.401 3.328 0.001
y3 0.800 0.215 3.714 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.y1 2.500 0.099 25.126 0.000
.y2 3.000 0.099 30.151 0.000
.y3 2.800 0.099 28.141 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.y1 0.619 0.135 4.579 0.000
.y2 0.330 0.189 1.747 0.081
.y3 0.752 0.125 6.011 0.000
factor1 0.371 0.150 2.472 0.013
Group 2 [Group 2]:
Latent Variables:
Estimate Std.Err z-value P(>|z|)
factor1 =~
y1 1.000
y2 1.250 0.283 4.422 0.000
y3 0.833 0.173 4.821 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.y1 2.700 0.089 30.288 0.000
.y2 3.100 0.085 36.321 0.000
.y3 2.900 0.093 31.255 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.y1 0.715 0.129 5.564 0.000
.y2 0.348 0.159 2.186 0.029
.y3 0.960 0.130 7.370 0.000
factor1 0.477 0.148 3.227 0.001