mplusParallel.automation (version 0.0.1.1)

mplusParallel_automation: Parallel automation of running Mplus models using R.

Description

This function provides a parallelized automation for Mplus when using R as the data generation method. When data_gen is specified, include any arguments from the function that need to be set in the global enviornment.

Usage

mplusParallel_automation(
  k,
  k.start = 1,
  data_gen = NA,
  seed = 123,
  ncores = "default",
  run = TRUE,
  useCores = TRUE,
  cores_per_analysis = "default",
  Par_plan = "cluster",
  rec = FALSE,
  results = NULL,
  multi_con = FALSE,
  con_index = c(),
  specific_sums = NULL,
  specific_params = NULL,
  item = NULL,
  params_ext = c("unstandardized"),
  modV1s = NULL,
  ops = NULL,
  modV2s = NULL,
  custom_auto = NULL,
  retry = TRUE,
  max_retry = 5,
  folder = ""
)

Value

Function returns a dataframe of all the desired parameters for each replication.

Arguments

k

Number of replications desired.

k.start

Defaults to 1. Specifies the replication to start on. Useful if the simulation stopped on a specific replication and resuming without loss of work.

data_gen

Can take either a single dataframe in the 'folder' or a data generation function. When it is a data generation function any arguments for data generation should be specified in this function.

seed

Seed defaults to 123 but can be any integer. This ensures every replication's data is generated using a different seed but is reproducible.

ncores

Defaults to the number of cores on the machine - 1.

run

Logical. Defaults to T. When T the Mplus models will be run. When F models will not be run and the output files will be read in only.

useCores

Logical. When TRUE, the mplus files will be adjusted to use the number of cores on the machine. This can speed up simulation run times.

cores_per_analysis

Applies when useCores is TRUE. Default is ncores/2. If you experience issues or crashes due to memory or core use, set this lower. In testing the default will use most of a computer's CPU power but no break the simulation.

Par_plan

Plan for parallel processing. Defaults to 'cluster'. Can take any argument from the 'future' package

rec

Logical. Indicates if the files are in subdirectories.

results

Indicates which results to collect. Supports summaries, parameters, and modindicies or any named list argument output by mplus automation. When using summaries, parameters, or modindicies mnore specific output is available.

multi_con

Logical. Indicates whether multiple conditions are run in a singular instance. Default is F.

con_index

A character vector. Specifies the indices for conditions to be tracked.

specific_sums

Extracts specific output when results is 'summaries'.

specific_params

Extracts specific parameters when results is 'parameters'.

item

Extracts specific items when results is 'parameters'.

params_ext

When results is 'parameters', specifies parameter type for extraction. Can take any type but defaults to 'unstandardized'. If you do not desire unstandardized parameters read in an output file to determine the name of the parameters of interest and use this as the named argument.

modV1s

Used for specific output when results is 'mod_indicies'.

ops

Operator for modV1s, e.g., 'BY' for factor loadings.

modV2s

Second variable for modV1s.

custom_auto

User-defined function for running and reading in models. Only functions that return single dataframes each run are currently supported.

retry

Logical. Defaults to TRUE. Retries with a new seed if chi is not returned by the model.

max_retry

Defaults to 5. Specifies how many times a new seed should be attempted.

folder

User-defined path to the root folder of where your Mplus files are located.

Examples

if (FALSE) {
# Loading the package
library(mplusParallel.automation)

# Data Generation
n_people <- 500
n_items <- 12
data <- matrix(sample(1:5, n_people * n_items, replace = TRUE), ncol = n_items)

# Writing an example input file
inp_content <- "
TITLE: TEST
DATA: FILE IS exdat.csv;
VARIABLE:
  Names ARE
i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i11 i12;
USEVARIABLES ARE i1-i12;
ANALYSIS:
TYPE = GENERAL;
PROCESSORS=6;
OUTPUT:
STANDARDIZED;
MODINDICES (ALL);
MODEL:
  trait1 BY
i1 (a1)
i2 (a2)
i3 (a3)
i4 (a4)
i5 (a5)
i6 (a6);
trait2 BY
i7 (a7)
i8 (a8)
i9 (a9)
i10 (a10)
i11 (a11)
i12 (a12);
i1-i12 (e);
trait1 @ 1
trait2 @ 1
"
writeLines(inp_content, "example_model_simple.inp")

# Running the function
res <- mplusParallel_automation(k=5, data_gen = data_gen,
results = 'parameters', specific_params = c('trait1.by', 'trait2.by'), folder = 'user_defined_path')

# Clean up
removeParFolders()
}