Likelihood Ratio Test P-Values

Compute the likelihood ratio test (LRT) p-values for free parameters in a lavaan output.

Usage

lrtp(
  fit,
  op = c("~", "~~"),
  no_variances = TRUE,
  no_error_variances = TRUE,
  no_error_covariances = TRUE,
  se_keep_bootstrap = FALSE,
  LRT_method = "default",
  scaled.shifted = TRUE,
  fallback_method = "satorra.2000",
  progress = TRUE,
  parallel = FALSE,
  ncores = parallel::detectCores(logical = FALSE) - 1,
  load_balancing = TRUE,
  ...
)

Arguments

fit: A lavaan-class object.
op: A character vector of lavaan model syntax operators. Free parameters of these operators will be included, unless excluded by other arguments. Default is c("~", "~~").
no_variances: Logical. If TRUE, the default, then all free variances are excluded. (Error variances are handled by no_error_variances.)
no_error_variances: Logical, If TRUE, the default, then all free error variances are excluded.
no_error_covariances: Logical. If TRUE, the default, then all free error covariances are excluded.
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.
LRT_method: String. Passed to the method argument of lavaan::lavTestLRT(). Default is "default", and let lavaan::lavTestLRT() decide the method based on fit.
scaled.shifted: Logical. Used when the method used in lavaan::lavTestLRT() is "satorra.2000". Default is TRUE and a scaled and shifted test statistic is used, the same default of lavaan::lavTestLRT().
fallback_method: The default method of lavaan::lavTestLRT(), "satorra.bentler.2001", may sometimes fail. If failed, this function will call lavaan::lavTestLRT() again using fallback_method. which is "satorra.2000" by default.
progress: Logical. If TRUE, the default, a progress bar will be displayed to show the progress (using the pbapply package).
parallel: Logical. If TRUE, parallel processing will be used to compute the LRT p-values for selected parameters. Default is FALSE. Set it to TRUE if the number of selected parameters is large.
ncores: Integer. The number of CPU cores to use if parallel is TRUE. Default is the number of physical cores (determined by parallel::detectCores()) minus 1.
load_balancing: Logical. If TRUE, the default, and parallel is TRUE, then load balancing will be used. May shorten the total processing time if the time to compute LRT p-values vary a lot across parameters.
...: Optional arguments to be passed to lavaan::parameterEstimates().

Value

An lrt-class object, which is a data-frame-like object similar to the output of lavaan::parameterEstimates(), with a column LRTp for the LRT p-values, as well as other columns such as the chi-square difference in the test. it has a print method, print.lrtp().

Details

It finds free parameters in a lavaan-class object, computes the likelihood ratio test (LRT) p-value for each of them when fixed to zero, and returns a parameter estimates table with the LRT p-values included.

By default, it only computes LRT p-values for regression paths and covariances, except for error covariances. This default can be overridden using arguments such as op, no_variances, no_error_variances, and no_error_covariances.

Technical Details

It first identify the parameters to be processed, and then call lrt() on each of them. Please refer to https://sfcheung.github.io/semlrtp/articles/internal_workflow.html for the internal workflow.

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
f3 =~ x7 + x8 + x9
f4 =~ x10 + x11 + x12
f2 ~~ f1
f3 ~ f1 + f2
f4 ~ f3
"
fit <- sem(mod, data_sem16)
lrtp(fit)
#> 
#> Parameter Estimates:
#> 
#>   Standard errors                             Standard
#>   Information                                 Expected
#>   Information saturated (h1) model          Structured
#> 
#> Regressions:
#>                    Estimate  Std.Err    Chisq     LRTp
#>   f3 ~                                                
#>     f1                0.248    0.097   10.343    0.001
#>     f2                0.292    0.199    3.778    0.052
#>   f4 ~                                                
#>     f3                2.118    0.684   86.205    0.000
#> 
#> Covariances:
#>                    Estimate  Std.Err    Chisq     LRTp
#>   f1 ~~                                               
#>     f2                0.039    0.022    9.717    0.002
#> 
lrtp(fit, op = "~")
#> 
#> Parameter Estimates:
#> 
#>   Standard errors                             Standard
#>   Information                                 Expected
#>   Information saturated (h1) model          Structured
#> 
#> Regressions:
#>                    Estimate  Std.Err    Chisq     LRTp
#>   f3 ~                                                
#>     f1                0.248    0.097   10.343    0.001
#>     f2                0.292    0.199    3.778    0.052
#>   f4 ~                                                
#>     f3                2.118    0.684   86.205    0.000
#>