Refit a 'lavaan'-Model by Several Estimators

Refit a model in 'lavaan' by several lavaan-supported estimators

Usage

compare_estimators(object, estimators = NULL)

se_ratios(fit_list, reference = NULL)

Arguments

object: A lavaan object.
estimators: A character vector of the estimator supported by the estimator argument of lavaan::lavaan() and its wrappers, such as lavaan::sem() and lavaan::cfa().
fit_list: The output of compare_estimators().
reference: The name of the reference method (ratios will be equal to one). Must be one of the estimator used on compare_estimators(). If NULL, the first estimator will be used.

Value

A list of lavaan outputs, each of them is an update of the original output using one of the estimators.

Details

The function simply uses lapply() and update() to rerun the analysis once for each of the estimator using update(object, estimator = "x", x being the estimator.

The results can then be compared using group_by_models().

Functions

compare_estimators(): Refit the model with different estimators.
se_ratios(): A wrapper of group_by_models() that computes the ratios of standard errors of different methods to those of one method.

Author

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

Examples


library(lavaan)
set.seed(5478374)
n <- 50
x <- runif(n) - .5
m <- .40 * x + rnorm(n, 0, sqrt(1 - .40))
y <- .30 * m + rnorm(n, 0, sqrt(1 - .30))
dat <- data.frame(x = x, y = y, m = m)
model <-
'
m ~ a*x
y ~ b*m
ab := a*b
'

fit <- sem(model, data = dat, fixed.x = FALSE)

# Refit the model by three different estimators
fit_more <- compare_estimators(fit, estimator = c("GLS", "MLR", "ML"))

# Use group_by_models to compare the estimates
group_by_models(fit_more, col_names = c("est", "pvalue"))
#>   lhs op rhs est_GLS est_MLR est_ML pvalue_GLS pvalue_MLR pvalue_ML
#> 1   m  ~   x   0.569   0.569  0.569      0.100      0.062     0.097
#> 2   y  ~   m   0.219   0.219  0.219      0.157      0.116     0.153
#> 3   m ~~   m   0.470   0.460  0.460      0.000      0.000     0.000
#> 4   x ~~   x   0.080   0.078  0.078      0.000      0.000     0.000
#> 5   y ~~   y   0.581   0.570  0.570      0.000      0.000     0.000
#> 6  ab := a*b   0.125   0.125  0.125      0.288      0.259     0.279

# Use se_ratios to compare standard errors
se_ratios(fit_more, reference = "ML")
#>   lhs op rhs se_GLS se_MLR se_ML ratio_GLS ratio_MLR ratio_ML
#> 1   m  ~   x  0.346  0.305 0.343     1.011     0.889        1
#> 2   y  ~   m  0.155  0.139 0.153     1.010     0.909        1
#> 3   m ~~   m  0.095  0.089 0.092     1.031     0.966        1
#> 4   x ~~   x  0.016  0.011 0.016     1.030     0.719        1
#> 5   y ~~   y  0.118  0.111 0.114     1.030     0.973        1
#> 6  ab := a*b  0.117  0.110 0.115     1.020     0.960        1