Fit a Model to a List of Datasets

Get the output of sim_data() and fit a model to each of the stored datasets.

Usage

fit_model(
  data_all = NULL,
  model = NULL,
  fit_function = "lavaan",
  arg_data_name = "data",
  arg_model_name = "model",
  arg_group_name = "group",
  ...,
  fit_out = NULL,
  parallel = FALSE,
  progress = FALSE,
  ncores = max(1, parallel::detectCores(logical = FALSE) - 1)
)

Arguments

data_all: The output of sim_data(), or a sim_data class object.
model: The model to be fitted. If NULL, the default, the model stored in data_all, which should be the data generation model, will be used.
fit_function: The function to be used to fit the model. Can also be a string: "lavaan" (the default) for lavaan::sem(), and "lm" or many_lm for lmhelprs::many_lm(). Other functions can also be used if necessary.
arg_data_name: The name of the argument of fit_function expecting the dataset. Default is "data".
arg_model_name: The name of the argument of fit_function expecting the model definition. Default is "model".
arg_group_name: The name of the argument of fit_function expecting the name of the group variable. Used only for multigroup models. Default is "group".
...: Optional arguments to be passed to fit_function when fitting the model.
fit_out: If set to a fit_out object (the output of fit_model()), then all missing arguments will be retrieved from fit_out. That is, users can use fit_model(data_all = new_data, fit_out = old_out) to re-fit a model originally fitted in old_out on a new list of dataset (new_data). No need to include all other arguments.
parallel: If TRUE, parallel processing will be used to fit the models. Default is FALSE.
progress: If TRUE, the progress of model fitting will be displayed. Default is `FALSE.
ncores: The number of CPU cores to use if parallel processing is used.

Value

An object of the class fit_out, which is a list of the output of fit_function (lavaan::sem() by default). If an error occurred when fitting the model to a dataset, then this element will be the error message from the fit function.

Details

By default, the function fit_model()

extracts the model stored in the output of sim_data(),
fits the model to each dataset simulated using fit_function, default to "lavaan" and lavaan::sem() will be called,
and returns the results.

If the datasets were generated from a multigroup model when calling sim_data(), a multigroup model is fitted.

The role of `fit_model()`

This function is used by the all-in-one function power4test(). Users usually do not call this function directly, though developers can use this function to customize the model fitting step in power analysis.

Examples


# Specify the population model

mod <-
"m ~ x
 y ~ m + x"

# Specify the effect sizes (population parameter values)

es <-
"
y ~ m: m
m ~ x: m
y ~ x: n
"

# Generate several simulated datasets

data_all <- sim_data(nrep = 5,
                     model = mod,
                     pop_es = es,
                     n = 100,
                     iseed = 1234)

# Fit the population model to each datasets

fit_all <- fit_model(data_all)
fit_all[[1]]
#> lavaan 0.6-20 ended normally after 1 iteration
#> 
#>   Estimator                                         ML
#>   Optimization method                           NLMINB
#>   Number of model parameters                         5
#> 
#>   Number of observations                           100
#> 
#> Model Test User Model:
#>                                                       
#>   Test statistic                                 0.000
#>   Degrees of freedom                                 0

# Fit the population model using the MLR estimator

fit_all_mlr <- fit_model(data_all,
                         estimator = "MLR")
fit_all_mlr[[1]]
#> lavaan 0.6-20 ended normally after 1 iteration
#> 
#>   Estimator                                         ML
#>   Optimization method                           NLMINB
#>   Number of model parameters                         5
#> 
#>   Number of observations                           100
#> 
#> Model Test User Model:
#>                                               Standard      Scaled
#>   Test Statistic                                 0.000       0.000
#>   Degrees of freedom                                 0           0

# Fit a model different from the population model,
# with the MLR estimator

mod2 <-
"m ~ x
 y ~ m"

fit_all_mlr2 <- fit_model(data_all,
                          mod2,
                          estimator = "MLR")
fit_all_mlr2[[1]]
#> lavaan 0.6-20 ended normally after 2 iterations
#> 
#>   Estimator                                         ML
#>   Optimization method                           NLMINB
#>   Number of model parameters                         4
#> 
#>   Number of observations                           100
#> 
#> Model Test User Model:
#>                                               Standard      Scaled
#>   Test Statistic                                 0.975       1.128
#>   Degrees of freedom                                 1           1
#>   P-value (Chi-square)                           0.323       0.288
#>   Scaling correction factor                                  0.864
#>     Yuan-Bentler correction (Mplus variant)