Skip to contents

Fix a Free Parameter To Zero And Fit a Model Again

Source: R/fix_to_zero.R
fix_to_zero.Rd

It fixes a designated free parameter in a lavaan object to zero and refit the model.

Usage

fix_to_zero(fit, par_id, store_fit = FALSE, se_keep_bootstrap = FALSE)

Arguments

fit

A lavaan-class object.

par_id

An integer. The row number of the free parameter in the parameter table of fit to be fixed.

store_fit

Logical. If TRUE, fit will be stored in the output. Default is FALSE.

se_keep_bootstrap

Logical. If TRUE and fit used bootstrapping standard error (with se = "bootstrap"), bootstrapping will also be use in fitting the restricted model. If FALSE, the default, then se will be set to "standard" if it is "bootstrap" in fit, to speed up the computation.

Value

A fix_to_zero-class object, which is a list with these elements:

  • fit0 is the lavaan output of the refitted object. NA if the fit failed for some reasons. To be considered an acceptable solution, the optimization must converge, the solution passes lavaan's post check, the variance-covariance matrix of estimates successfully computed, and the increase in the model degree of freedom equal to the expected change.

  • fit1 is the original lavaan output if store_fit is TRUE. It is NULL if store_fit is FALSE, the default.

  • par_id is the row number of the designated free parameter in the parameter table.

  • call is the original call to this function.

  • ptable0 is the parameter table with the designated parameter fixed to zero. It can be used for diagnostic purpose if the fit failed.

  • fit0_error is the error message in refitting the model (ptable0), if any. If no error, it is NA.

  • vcov_ok is TRUE if the variance-covariance matrix of the estimates can be computed without error nor warning. FALSE otherwise.

  • vcov_msg is the message generated when using lavaan::lavInspect() to get the variance-covariance matrix of the parameter estimates of the refitted model. If TRUE, then no error nor warning. Can be used for diagnostic purposes.

  • converged: Whether refitting the modified model (ptable0) converged.

  • post_check_passed: Whether the solution of the modified model (ptable0) passed lavaan's post check.

  • post_check_msg: If the solution failed lavaan's post check, it stores the warning message. If the solution passes the check, it is NA.

  • fit_not_ok: If the fit failed for some reasons, the fit object, if available, is stored in this element rather than in fit0. such that the fit object can be retrieved for diagnostic purposed if necessary.

  • df_diff_one: Whether the difference in model degrees of freedom between the modified model and the original model is one. If a variance is fitted to zero, related covariance(s) is/are also fitted to zero and so the difference in model degrees of freedom can be legitimately greater than one.

  • se_force_standard: Whether se was forced to be "standard" even if it is "bootstrap" in fit. If FALSE, then either se is not "bootstrap" in fit or it was not changed in fitting the restricted model.

Details

It modifies the parameter table of a lavaan-class object and then fits the model again.

Users should usually call lrtp() directly instead of calling this function. It is exported for developers.

See also

lrtp()

Author

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448

Examples

library(lavaan)
data(data_sem16)
mod <-
"
f1 =~ x1 + x2 + x3
f2 =~ x4 + x5 + x6
"
fit <- sem(mod, data_sem16)
# Fix the factor covariance to zero
out <- fix_to_zero(fit, par_id = 15)
summary(out$fit0)
#> lavaan 0.6-19 ended normally after 49 iterations
#> 
#>   Estimator                                         ML
#>   Optimization method                           NLMINB
#>   Number of model parameters                        12
#> 
#>   Number of observations                           336
#> 
#> Model Test User Model:
#>                                                       
#>   Test statistic                                27.655
#>   Degrees of freedom                                 9
#>   P-value (Chi-square)                           0.001
#> 
#> Parameter Estimates:
#> 
#>   Standard errors                             Standard
#>   Information                                 Expected
#>   Information saturated (h1) model          Structured
#> 
#> Latent Variables:
#>                    Estimate  Std.Err  z-value  P(>|z|)
#>   f1 =~                                               
#>     x1                1.000                           
#>     x2                0.911    0.253    3.604    0.000
#>     x3                1.051    0.323    3.257    0.001
#>   f2 =~                                               
#>     x4                1.000                           
#>     x5                2.538    1.424    1.782    0.075
#>     x6                5.526    6.331    0.873    0.383
#> 
#> Covariances:
#>                    Estimate  Std.Err  z-value  P(>|z|)
#>   f1 ~~                                               
#>     f2                0.000                           
#> 
#> Variances:
#>                    Estimate  Std.Err  z-value  P(>|z|)
#>    .x1                1.010    0.119    8.457    0.000
#>    .x2                1.067    0.111    9.573    0.000
#>    .x3                0.700    0.114    6.159    0.000
#>    .x4                1.198    0.096   12.500    0.000
#>    .x5                1.037    0.182    5.705    0.000
#>    .x6                0.515    0.775    0.664    0.507
#>     f1                0.286    0.110    2.595    0.009
#>     f2                0.022    0.031    0.723    0.469
#>