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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

An object of class modsem_da or modsem_pi.

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. If FALSE, 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.

Methods (by class)

  • estimate_h0(modsem_da): Estimate baseline model for modsem_da objects

  • estimate_h0(modsem_pi): Estimate baseline model for modsem_pi objects

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] 676.5438
#> 
#> $df
#> [1] 1
#> 
#> $p
#> [1] 3.771603e-149
#> 
#> $diff.loglik
#> [1] 338.2719
#> 

# 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
# }