library(lavaan)
lower <- '
323.800
272.989 291.549
271.535 264.022 298.744
0.005 -0.046 0.134 0.242
'
covmat <- getCov(lower)
colnames(covmat) <- c('total1','total2','total3','gender')
rownames(covmat) <- colnames(covmat)
cond.model <- "
final =~ 1*total1 + 1*total2 + 1*total3
slope =~ (-2)*total1 + (-1)*total2 + 0*total3
final ~ gender
slope ~ gender
total1 ~~ a*total1
total2 ~~ a*total2
total3 ~~ a*total3
"
cond.fit <- sem(cond.model, sample.cov = covmat, sample.nobs = 324)
summary(cond.fit, fit.measures = T, standardized = T)
## lavaan 0.6.15 ended normally after 78 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 8
## Number of equality constraints 2
##
## Number of observations 324
##
## Model Test User Model:
##
## Test statistic 2.821
## Degrees of freedom 3
## P-value (Chi-square) 0.420
##
## Model Test Baseline Model:
##
## Test statistic 1078.363
## 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) -3619.032
## Loglikelihood unrestricted model (H1) -3617.621
##
## Akaike (AIC) 7250.063
## Bayesian (BIC) 7272.748
## Sample-size adjusted Bayesian (SABIC) 7253.717
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.092
## P-value H_0: RMSEA <= 0.050 0.705
## P-value H_0: RMSEA >= 0.080 0.093
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.015
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## final =~
## total1 1.000 16.231 0.912
## total2 1.000 16.231 0.936
## total3 1.000 16.231 0.947
## slope =~
## total1 -2.000 -4.262 -0.240
## total2 -1.000 -2.131 -0.123
## total3 0.000 0.000 0.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## final ~
## gender 0.395 1.922 0.205 0.837 0.024 0.012
## slope ~
## gender 0.267 0.503 0.530 0.596 0.125 0.061
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .final ~~
## .slope -1.138 4.438 -0.256 0.798 -0.033 -0.033
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .total1 (a) 30.533 2.399 12.728 0.000 30.533 0.096
## .total2 (a) 30.533 2.399 12.728 0.000 30.533 0.102
## .total3 (a) 30.533 2.399 12.728 0.000 30.533 0.104
## .final 263.394 22.781 11.562 0.000 1.000 1.000
## .slope 4.523 1.964 2.304 0.021 0.996 0.996
© Copyright 2024
@Yi Feng
and
@Gregory R. Hancock.