Estimate baseline model for modsem
models
estimate_h0.Rd
Estimates a baseline model (H0) from a given model (H1). The baseline model is estimated by removing all interaction terms from the model.
Usage
estimate_h0(object, warn_no_interaction = TRUE, ...)
# S3 method for class 'modsem_da'
estimate_h0(object, warn_no_interaction = TRUE, ...)
# S3 method for class 'modsem_pi'
estimate_h0(object, warn_no_interaction = TRUE, reduced = TRUE, ...)
Arguments
- object
- warn_no_interaction
Logical. If `TRUE`, a warning is issued if no interaction terms are found in the model.
- ...
Additional arguments passed to the `modsem_da` function, overriding the arguments in the original model.
- reduced
Should the baseline model be a reduced version of the model? If
TRUE
, the latent product term and its (product) indicators are kept in the model, but the interaction coefficients are constrained to zero. IfFALSE
, the interaction terms are removed completely from the model. Note that the models will no longer be nested, if the interaction terms are removed from the model completely.
Examples
# \dontrun{
m1 <- "
# Outer Model
X =~ x1 + x2 + x3
Y =~ y1 + y2 + y3
Z =~ z1 + z2 + z3
# Inner model
Y ~ X + Z + X:Z
"
# LMS approach
est_h1 <- modsem(m1, oneInt, "lms")
est_h0 <- estimate_h0(est_h1, calc.se=FALSE) # std.errors are not needed
compare_fit(est_h1 = est_h1, est_h0 = est_h0)
#> $D
#> [1] 676.5418
#>
#> $df
#> [1] 1
#>
#> $p
#> [1] 3.775457e-149
#>
#> $diff.loglik
#> [1] 338.2709
#>
# Double centering approach
est_h1 <- modsem(m1, oneInt, method = "dblcent")
est_h0 <- estimate_h0(est_h1, oneInt)
compare_fit(est_h1 = est_h1, est_h0 = est_h0)
#>
#> Chi-Squared Difference Test
#>
#> Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
#> est_h1 111 53735 54071 122.92
#> est_h0 112 54393 54724 783.18 660.26 0.57413 1 < 2.2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# Constrained approach
est_h1 <- modsem(m1, oneInt, method = "ca")
est_h0 <- estimate_h0(est_h1, oneInt)
compare_fit(est_h1 = est_h1, est_h0 = est_h0)
#>
#> Chi-Squared Difference Test
#>
#> Df AIC BIC Chisq Chisq diff RMSEA Df diff Pr(>Chisq)
#> est_h1 56 48747 48937 60.401
#> est_h0 57 49404 49589 720.150 659.75 0.57391 1 < 2.2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# }