This guide provides examples of how to use the functions in the MplusAutomation package. The package is designed to automate three major aspects of latent variable modeling in Mplus:
The guide tries to make few assumptions about the user’s familiarity with the R environment, as many Mplus user’s may be unfamiliar with R. Note that the MplusAutomation package was written to be compatible with Mplus version 5.21 or later. Although many functions are likely to work with earlier versions, there may be incompatibilities.
Note: some examples herein reference examples from the Mplus User’s Guide. The input and output files for these examples are assumed to reside in the directory:
C:\Program Files\Mplus\Mplus Examples\User's Guide Examples
If you have installed Mplus to a different location, please use the correct directory in the code below.
Clarifying the relationship between MplusAutomation and MplusPlease note that MplusAutomation does not provide a copy of the Mplus software, which is proprietary. All of the model estimation is handled by Mplus (think of it as the computation engine), while MplusAutomation provides convenience functions for interfacing with Mplus in R and working with model outputs using familiar R data structures such as data.frame objects.
Thus, you must have a working copy of Mplus to estimate latent variable models using MplusAutomation using the runModels() or mplusModeler() functions. If you would like to obtain a copy of Mplus, please follow the instructions on the statmodel.com website.
Note: You only need to install the MplusAutomation package once on a given machine. Once you follow these instructions, you can just use library(MplusAutomation) to load the package in future sessions.
The MplusAutomation package is compatible with R and Mplus on Windows, Mac OS X, and Linux platforms.
The package was initially built using R 2.15.1 and has been tested with modern versions up through R 4.0.2. To obtain R follow this link.
After installing R and launching a new session, type the following at the command line:
If you’re running on Windows, you may need to run R as an administrator (right-click, Run as administrator).
To load the MplusAutomation package so that its functions are available to use, type:
## Version: 1.0.0
## We work hard to write this free software. Please help us get credit by citing:
##
## Hallquist, M. N. & Wiley, J. F. (2018). MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus. Structural Equation Modeling, 25, 621-638. doi: 10.1080/10705511.2017.1402334.
##
## -- see citation("MplusAutomation").Note: Mac users using versions of R prior to 4.0.0 may experiences crashes if they have not setup Tcl/Tk properly within R. The default distribution of Tcl/Tk shipped with Mac OS X is not compatible with R. Instead, users should download and install the Tcl/Tk distribution here.
MplusAutomation on your machine
To verify the version of MplusAutomation loaded in your R session, type the following code which should provide a listing of all loaded packages.
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19043)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] MplusAutomation_1.0.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.1.1 xfun_0.24 bslib_0.2.5.1 pander_0.6.4
## [5] purrr_0.3.4 lattice_0.20-44 colorspace_2.0-1 vctrs_0.3.8
## [9] generics_0.1.0 htmltools_0.5.1.1 yaml_2.2.1 utf8_1.2.1
## [13] rlang_0.4.11 pkgdown_1.6.1 jquerylib_0.1.4 pillar_1.6.1
## [17] glue_1.4.2 DBI_1.1.1 gsubfn_0.7 lifecycle_1.0.0
## [21] plyr_1.8.6 stringr_1.4.0 munsell_0.5.0 gtable_0.3.0
## [25] ragg_1.1.3 coda_0.19-4 memoise_2.0.0 evaluate_0.14
## [29] knitr_1.33 fastmap_1.1.0 parallel_4.1.0 fastDummies_1.6.3
## [33] fansi_0.5.0 proto_1.0.0 Rcpp_1.0.6 xtable_1.8-4
## [37] scales_1.1.1 backports_1.2.1 checkmate_2.0.0 cachem_1.0.5
## [41] desc_1.3.0 jsonlite_1.7.2 systemfonts_1.0.2 fs_1.5.0
## [45] texreg_1.37.5 textshaping_0.3.5 ggplot2_3.3.5 digest_0.6.27
## [49] stringi_1.6.2 dplyr_1.0.7 grid_4.1.0 rprojroot_2.0.2
## [53] tools_4.1.0 magrittr_2.0.1 sass_0.4.0 tibble_3.1.2
## [57] crayon_1.4.1 pkgconfig_2.0.3 ellipsis_0.3.2 data.table_1.14.0
## [61] assertthat_0.2.1 rmarkdown_2.9 httr_1.4.2 R6_2.5.0
## [65] boot_1.3-28 compiler_4.1.0As is conventional for R packages, the latest version of MplusAutomation may be obtained from CRAN using the update.packages() function in an R session. As of 2021-07-01, the current version of the package is 1.0.0.
R to Mplus
The helper function, prepareMplusData(), eases the task of transferring data from R to Mplus. Mplus requires that files not have a header row and that the variable names be specified within the Mplus input syntax. The prepareMplusData() function converts an R data.frame object (the typical way to represent two-dimensional data in R) to a tab-delimited file and it prints the corresponding Mplus syntax to the console. This syntax can then be pasted into the head of a new Mplus input file. Here are two basic examples of using the command:
prepareMplusData(
my_data,
filename = "C:/Data_Analysis/Prepare Mplus.dat",
keepCols=c("id", "item1", "item3", "item6"))
prepareMplusData(
my_other_data,
filename = "C:/Data_Analysis/Prepare Dropped Mplus.dat",
dropCols=c("baditem1", "baditem2", "baditem7"))The first call requests that only the variables specified by keepCols be included in the resultant Mplus data file. In like manner, the second call requests that all variables except those specified by dropCols by included in the data file. If neither keepCols nor dropCols is specified, then the entire data.frame is output.
For factors and character variables (categorical), prepareMplusData converts each level into an integer value (first level = 1, second level = 2, etc.) and prints out information about how the levels were converted so that you can use them properly inside the Mplus syntax. Alternatively, if you use the dummyCode argument for prepareMplusData, you will get a set of dummy codes that can be used in the Mplus syntax.
Here, in the mtcars dataset, we convert the number of gears (in the car) to a factor so that prepareMplusData will know to convert its unique values (3, 4, 5) into an integer-valued variable (1, 2, 3) that can be used in Mplus syntax. For the number of cylinders (cyl) and transmission type (am), we ask prepareMplusData to generate dummy codes for each level of these factors. Note that one dummy code is returned for each level, so no reference category is implied. Thus, it is up to you to drop one of the dummy codes in the Mplus syntax to denote the reference category and avoid multicollinearity.
## Converting the following variables to dummy codes: cyl, am##
## -----
## Factor: gear
## Conversion:
## level number
## 3 1
## 4 2
## 5 3
## -----## The file(s)
## 'mtcars.dat'
## currently exist(s) and will be overwritten## TITLE: Your title goes here
## DATA: FILE = "mtcars.dat";
## VARIABLE:
## NAMES = mpg disp hp drat wt qsec vs gear carb cyl_4 cyl_6 cyl_8 am_0 am_1;
## MISSING=.;A major purpose of the MplusAutomation package is to allow for easy runs of batches/groups of Mplus models. Oftentimes, one wants to compare a group of related models, such as testing for different forms of measurement invariance. Depending on the complexity of the models, Mplus can take several minutes to many hours to run each model. The routine is designed to run a group of related models located within a directory (or nested subdirectories).
Basic use of runModelsAs an example, say that we want to run all of the models used in the Mplus 5.1 Addendum: http://statmodel.com/download/examples1.pdf. The input files for these are located in: C:\Program Files\Mplus\Mplus Examples\Addendum Examples.
To run this batch, enter this at the command line:
runModels("C:/Program Files/Mplus/Mplus Examples/Addendum Examples")Note that you need to use forward slashes (“/”), not backslashes in the path name. Alternatively, you could use double backslashes (e.g., "C:\\Program Files" etc.).
Sometimes it is useful to organize models into one or more subdirectories where each subdirectory contains models for a particular outcome or analytic approach. For example, if one were contrasting latent class analysis (LCA) with confirmatory factor analysis (CFA), one could place all LCA models in a single directory and place CFA models in a different directory. This might yield a file structure like this:
ComparingLCAvCFA/LCA/1-class LCA.inp
ComparingLCAvCFA/LCA/2-class LCA.inp
ComparingLCAvCFA/LCA/3-class LCA.inp
ComparingLCAvCFA/CFA/1-factor CFA.inp
ComparingLCAvCFA/CFA/2-factor CFA.inp
ComparingLCAvCFA/CFA/3-factor CFA.inpIn this case, all Mplus files for the larger project are housed within a parent directory, ComparingLCAvCFA. To run all models within ComparingLCAvCFA, including models within any subdirectories (including LCA and CFA), use the recursive parameter of runModels.
runModels(
"C:/Data_Analysis/ComparingLCAvCFA",
recursive=TRUE)The logFile parameter of runModels allows the user to specify a text file containing the results of runModels. Included in the log file are the parameters passed to the function, the date when the batch started, which models were run (and which were skipped), and any actions taken if the R process was interrupted (e.g., terminating the Mplus process). By default, runModels() will create a log file in the same directory as the models to be run, directory, called Mplus Run Models.log. To change the name or location of this file, specify the logFile parameter, such as in this example:
runModels(
"C:/Data_Analysis/ComparingLCAvCFA",
recursive=TRUE,
logFile="C:/CFALCA-Comparison-Log.txt")|Here, the file C:/CFALCA-Comparison-Log.txt will be created in the directory C:/.
To specify that no log file should be created, pass logFile=NULL to runModels().
runModels(
"C:/Data_Analysis/ComparingLCAvCFA",
recursive=TRUE,
logFile=NULL)Sometimes it is useful to skip models that have already been run to avoid the computing time associated with running all input files within a directory. The parameter allows one to specify which models should be re-run, where models that have an output with the same filename as the input file are considered to have been run. By default, replaceOutfile="always", meaning that all input files will be run, regardless of whether they have a matching output file.
To skip any model that already has an existing output file, pass replaceOutfile="never" to runModels(), such as in this example:
runModels(
"C:/Data_Analysis/ComparingLCAvCFA",
recursive=TRUE,
replaceOutfile="never")Oftentimes, after a model or group of models has been run, it is necessary to modify some aspects of the parameterization to improve model fit or address estimation problems. In such cases, output files are inspected and the corresponding input files are modified. In such cases, one may only want to run models that have been updated, but not to re-run models that completed successfully. This can be accomplished by passing replaceOutfile="modifiedDate" to runModels(). The "modifiedDate" determines whether there is an existing output file for a given input file. If there is, it checks to see whether the date the input file was modified is newer than the output file. If the input file is newer, then the model is run. Otherwise, it is skipped. Here is an example:
runModels(
"C:/Data_Analysis/ComparingLCAvCFA",
recursive=TRUE,
replaceOutfile="modifiedDate")R output in the console
When models are run by the Mplus Windows program (MplusWin.exe), a separate DOS window appears that documents the TECH8 progress of the model, which represents the progress toward maximum-likelihood convergence for the model (including random starts and final stage optimizations for some models). To display the same TECH8 output for models run by runModels(), pass showOutput=TRUE to the runModels() function.
runModels(
"C:/Data_Analysis/ComparingLCAvCFA",
recursive=TRUE,
showOutput=TRUE)If the R session was started through the R GUI (Rgui.exe), the output will be displayed within the R window. If the R session was started using Rterm (the terminal), a separate DOS window will display the output, as occurs using the built-in Mplus Windows program.
By default, the output is not shown, showOutput=FALSE.
runModels()
A wrapper function, runModels_Interactive(), is included in the MplusAutomation package, which provides a simple dialog box for specifying which models to run. To start the interface, type the following:
The picture below documents the appearance of this interface:
Figure. Example of using runModels() in an interactive graphical interface.
Although one can provide parameters to the function to set the initial states of the interface, it is rarely necessary to do so, so the syntax above should suffice.
Summarizing model fit statistics in tabular formOnce summary statistics for a group of models have been extracted, it is often useful to display them in tabular form to compare fit among models, sorted by a particular criterion (e.g., AIC).
The MplusAutomation package provides three routines for tabulating model summary statistics. At this time, there are three table-generating functions, which are detailed below: showSummaryTable(), HTMLSummaryTable(), LatexSummaryTable().
As their names suggest, these functions can create tables for on-screen display (showSummaryTable()), as an HTML file containing the table (HTMLSummaryTable()), or as a LaTex-formatted table (LatexSummaryTable() ).
The showSummaryTable() function is designed to display a summary table of model fit statistics on the screen. The function expects a model list created by extractModelSummaries() and allows the user to specify which columns should be included in the table.
Here is a simple example of using showSummaryTable() by specifying which columns to keep in the table:
showSummaryTable(
summaryStats,
keepCols = c("Title", "LL", "AIC", "BIC", "CFI"),
sortBy = "AIC")And another example specifying that all columns in the model list should be displayed except those specified:
showSummaryTable(
summaryStats,
dropCols = c("InputInstructions", "Observations", "Parameters"),
sortBy = "CFI")The HTMLSummaryTable() function creates an HTML file containing a summary table of model fit statistics. Its syntax is very similar to showSummaryTable(), including parameters such as dropCols, keepCols, and sortBy. Two parameters distinguish it from other summary functions: filename and display.
The filename parameter specifies the path and filename of the HTML file to be created. display specifies whether to display the HTML summary table in the web browser after it is created. Here is a simple of using the function:
HTMLSummaryTable(
summaryStats,
filename = "C:/MyModelSummary.html",
display = TRUE,
keepCols = c("Title", "LL", "AIC", "BIC", "AICC"),
sortBy = "AIC")One major strength of R is its ability to be interwoven with LaTeX, an advanced typesetting language. The most frequently used approach for combining R and LaTex is Sweave (https://cran.r-project.org/doc/Rnews/Rnews_2002-3.pdf), a built-in R function that runs R code embedded in a LaTeX document, thereby permitting the creation of advanced automated reports.
Mplus model fit summary tables can be formatted in LaTeX using the LatexSummaryTable() function. Unlike showSummaryTable() and HTMLSummaryTable(), LatexSummaryTable() returns a value, specifically the LaTeX syntax for the summary table. Here is a simple example of the function
myLatexTable <- LatexSummaryTable(
summaryStats,
keepCols = c("Title", "BIC", "Parameters"),
sortBy = "Parameters",
caption = "Comparing CFA vs. LCA according to number of parameters",
label="CFALCATab")Note that LatexSummaryTable() supports two distinct parameters relative to other summary table functions: caption() and label(). These allow the user to set the caption and label properties of the table, which are used in LaTex for displaying a caption with the table and for allowing the table to be easily referenced in other parts of document, respectively. See https://en.wikibooks.org/wiki/LaTeX/Tables#The_table_environment_-_captioning_etc for further details about LaTeX tables.
The LaTeX syntax for a summary table could be included in an Sweave document in the following way:
<<echo=TRUE, results=tex>>=
myLatexTable <- LatexSummaryTable(
summaryStats,
keepCols = c("Title", "BIC", "Parameters"),
sortBy = "Parameters",
caption = "Comparing CFA vs. LCA according to number of parameters",
label = "CFALCATab")
print(myLatexTable)
@
See the Sweave manual for more details about combining LaTeX with R.
An important part of interpreting results from latent variable models is the comparison of model fit indices and parameter estimates across related sets of models. For example, when an additional covariate is included in the model, how do the other parameter estimates change? The compareModels() function is designed to compare model fit indices and/or parameter estimates across two models.
This function also computes chi-square difference tests for nested models estimated with the ML, MLM, MLR, WLS, or WLSM estimators using the diffTest parameter.
To use compareModels(), I recommend using readModels() to extract various fit statistics and parameters from two or more models. You can pass in the results of extractModelSummaries() or extractModelParameters() to compareModels(), but the output will be limited to summaries or parameters, respectively.
Here is a brief example of how one might use compareModels().
parallelModels <- readModels("10_14_Harsh_SelfCon_Impul")
compareModels(parallelModels[["backport.from.grand.model.out"]],
parallelModels[["backport.from.grand.model.slopesonw1.out"]],
show = c("diff", "pdiff", "summaries", "unique"),
equalityMargin = c(param = .05, pvalue = .02),
sort = "type", diffTest = TRUE, showNS = FALSE)Mplus model comparison
----------------------
------
Model 1: 10_14_Harsh_SelfCon_Impul/backport from grand model.out
Model 2: 10_14_Harsh_SelfCon_Impul/backport from grand model slopesonw1.out
------
Model Summary Comparison
------------------------
m1 m2
Title Harsh Impul Self-Control Trivariate Harsh Impul Self-Control Trivariate
LGCM with W1 Covariates - No Direct LGCM with W1 Covariates - Direct
Influence of W1 on Slopes and Influence of W1 on Slopes and
Intercepts Intercepts
Observations 2187 2187
Estimator MLR MLR
Parameters 87 103
LL -69851.911 -69820.77
AIC 139877.821 139847.539
BIC 140372.876 140433.639
ChiSqM_Value 280.781 222.538
ChiSqM_DF 129 113
CFI 0.984 0.988
TLI 0.977 0.981
SRMR 0.025 0.022
MLR Chi-Square Difference test for nested models based on loglikelihood
-----------------------------------------------------------------------
Difference Test Scaling Correction: 1.033437
Chi-square difference: 60.2668
Diff degrees of freedom: 16
P-value: 0
Note: The chi-square difference test assumes that these models are nested.
It is up to you to verify this assumption.
MLR Chi-Square Difference test for nested models
--------------------------------------------
Difference Test Scaling Correction: 1.03125
Chi-square difference: 60.44
Diff degrees of freedom: 16
P-value: 0
Note: The chi-square difference test assumes that these models are nested.
It is up to you to verify this assumption.
=========
Model parameter comparison
--------------------------
Parameter estimates that differ between models (param. est. diff > 0.05)
----------------------------------------------
paramHeader param m1_est m2_est . m1_se m2_se . m1_est_se m2_est_se . m1_pval m2_pval
SCC_S1.ON IMP_I 0.155 0.073 | 0.036 0.050 | 4.255 1.478 | 0.000 0.139
HPC_I.WITH IMP_I 0.128 0.182 | 0.066 0.069 | 1.927 2.634 | 0.054 0.008
SCC_I.WITH HPC_I 1.391 1.455 | 0.189 0.183 | 7.372 7.949 | 0.000 0.000
SCC_I.WITH IMP_I 0.213 0.352 | 0.127 0.134 | 1.682 2.624 | 0.093 0.009
SCC_S1.WITH SCC_S2 -0.584 -0.649 | 0.160 0.157 | -3.638 -4.141 | 0.000 0.000
Intercepts A11XEMOTSP 4.523 4.592 | 0.568 0.568 | 7.964 8.084 | 0.000 0.000
Intercepts HPC_I 5.272 5.528 | 0.225 0.243 | 23.446 22.723 | 0.000 0.000
Intercepts HPC_S 0.725 0.583 | 0.092 0.092 | 7.865 6.358 | 0.000 0.000
Intercepts IMP_I -0.661 -0.544 | 0.155 0.167 | -4.263 -3.252 | 0.000 0.001
Intercepts SCC_I 4.113 4.299 | 0.390 0.409 | 10.547 10.515 | 0.000 0.000
Intercepts SCC_S2 4.811 5.008 | 0.382 0.418 | 12.604 11.987 | 0.000 0.000
Residual.Variances A10CHARP 2.376 2.299 | 0.158 0.156 | 15.016 14.719 | 0.000 0.000
Residual.Variances A10CSELF 6.871 6.544 | 0.554 0.534 | 12.405 12.245 | 0.000 0.000
Residual.Variances A12CSELF 6.298 6.206 | 0.406 0.401 | 15.531 15.493 | 0.000 0.000
Residual.Variances A14CHARP 2.019 1.959 | 0.154 0.150 | 13.149 13.075 | 0.000 0.000
Residual.Variances A14CSELF 2.547 2.654 | 0.903 0.896 | 2.821 2.961 | 0.005 0.003
Residual.Variances HPC_I 2.634 2.764 | 0.166 0.162 | 15.846 17.079 | 0.000 0.000
Residual.Variances SCC_I 7.086 7.433 | 0.574 0.552 | 12.351 13.463 | 0.000 0.000
Residual.Variances SCC_S1 0.606 0.680 | 0.140 0.131 | 4.312 5.203 | 0.000 0.000
P-values that differ between models (p-value diff > 0.02)
-----------------------------------
paramHeader param m1_est m2_est . m1_se m2_se . m1_est_se m2_est_se . m1_pval m2_pval
HPC_S.ON IMP_I 0.049 0.028 | 0.011 0.015 | 4.243 1.894 | 0.000 0.058
SCC_S1.ON IMP_I 0.155 0.073 | 0.036 0.050 | 4.255 1.478 | 0.000 0.139
HPC_I.WITH IMP_I 0.128 0.182 | 0.066 0.069 | 1.927 2.634 | 0.054 0.008
SCC_I.WITH IMP_I 0.213 0.352 | 0.127 0.134 | 1.682 2.624 | 0.093 0.009
Parameters unique to model 1: 0
-----------------------------
None
Parameters unique to model 2: 16
-----------------------------
paramHeader param m2_est m2_se m2_est_se m2_pval
HPC_S.ON W1PHARPUN 0.038 0.008 4.884 0.000
SCC_S1.ON W1PSELF 0.034 0.014 2.389 0.017
14 filtered from output (fixed and/or n.s.)
HPC_S.ON.IMPLW1, HPC_S.ON.W1PNEGEM, HPC_S.ON.W1PSELF, SCC_S1.ON.IMPLW1,
SCC_S1.ON.W1PHARPUN, SCC_S1.ON.W1PNEGEM, SCC_S2.ON.IMPLW1,
SCC_S2.ON.W1PHARPUN, SCC_S2.ON.W1PNEGEM, SCC_S2.ON.W1PSELF,
IMP_S.ON.IMPLW1, IMP_S.ON.W1PHARPUN, IMP_S.ON.W1PNEGEM, IMP_S.ON.W1PSELF
==============As can be discerned above, the example compareModels() call above compares two nested models from the list returned by readModels(). The show argument requests a comparison of parameter value difference ("diff"), p-value differences ("pdiff"), summary statistics ("summaries"), and parameters unique to each model ("unique").
Parameter estimate differences must exceed .05 to be displayed, and p-value differences must exceed .02 (this is specified by the equalityMargin argument).
The parameter comparisons are sorted by type (factor loadings, regressions, covariances, residual variances, etc.). A chi-square difference test is requested using diffTest=TRUE. And non-significant parameters are removed from the model comparison (showNS=FALSE).
createModels())
The third major focus of the MplusAutomation package is to provide tools that automate the process of creating input files for a related group of models. Perhaps the simplest example of a related group of models is latent class analysis, where one runs a certain model with different numbers of classes, but the input files are otherwise the same.
The createModels() function converts a single Mplus template file into a set of related Mplus input files. The template language is a simple extension of the Mplus language that allows dynamic values to be inserted into Mplus syntax files while reusing most of the code. The basic notion for template files is that multiple input files are created by looping over one or more variables (called “iterators”) and substituting specific values that change for each model, thereby allowing for the creation of related input files that share much of the code. As a basic example, one might iterate over a set of outcome variables (e.g., positive emotions, negative emotions, and conscientiousness) in a growth model where the dataset remains fixed, but the outcome variables change.
Mplus template syntax files are divided into two sections: the init section and the body section. The init section consists of definitions for variables to be inserted in the Mplus syntax, instructions for determining the filename and directory for created input files, and the variables to loop over to create multiple Mplus input files. The body section consists of Mplus syntax with template tags included where certain variables will change (e.g., the number of classes, names of outcome variables, etc.). Tags in Mplus syntax language are demarcated by double brackets. For example, the tag [[outcomeName]] requests that the value of provided in the init section be inserted into the Mplus syntax file.
Before we get into details, a particularly simple example may make the general concepts more tangible. This example is adapted from Mplus User’s Guide Example 7.3: LCA with binary latent class indicators using automatic starting values with random starts.
[[init]]
iterators = classes;
classes = 1:7;
filename = "[[classes]]-class LCA 7.3.inp";
outputDirectory = "C:/MplusAutomation/LCA Outputs";
[[/init]]
TITLE: this is an example of a LCA with binary
latent class indicators using automatic
starting values with random starts
DATA: FILE IS ex7.3.dat;
VARIABLE: NAMES ARE u1-u4 x1-x10;
USEVARIABLES = u1-u4;
CLASSES = c ([[classes]]);
CATEGORICAL = u1-u4;
AUXILIARY = x1-x10 (e);
ANALYSIS: TYPE = MIXTURE;
OUTPUT: TECH1 TECH8 TECH10;The above template file instructs the createModels() function to loop over a variable called classes. The classes variable is defined as the integers from 1-7 (the colon in 1:7 indicates a sequence). Files should be named according to the number of classes, so when classes = 5, then filename = "[[classes]]-class LCA 7.3.inp"; will evaluate to 5-class LCA 7.3.inp. All files will be saved in the directory C:/MplusAutomation/LCA Outputs. Note that R`` uses forward slashes, not backslashes, to indicate directory paths.
In the body section of this simple example, the only thing that changes is the definition CLASSES = c([[classes]]). And because classes was defined as the integers from 1-7, 7 Mplus input files will be created by createModels() with the major difference being the number of classes. As createModels() loops over the classes variable, the current value of classes is inserted in the body section.
The init section consists of variable definitions that are used to specify which variables are iterators, the number of iterations/models to loop over, the filenames and directories for the input files created by createModels(), and the fields to be inserted in the body section where template tags are specified. Variable definitions in the iterator section use the syntax variable = value;. Variable names are case-sensitive and value fields can span multiple lines. All definitions must be terminated by a semicolon. In cases where the variable’s value is a series of items (e.g., outcome1, outcome2, outcome3, these should be specified as a space-separated list (either using spaces or tabs). For example, if one wants to link a particular variable to an iterator and for the value of that list to be included in the body section, the definitions might look something like:
[[init]]
iterators = outcome;
outcome = 1:4;
outcomeNames#outcome = Conscien Extraver Agreeabl Openness;
filename = "CFA for [[outcomeNames#outcome]].inp";
outputDirectory = C:/;
[[/init]]Here, four input files will be created corresponding to four different outcome variables: Conscien, Extraver, Agreeabl, and Openness (which are all presumably defined in the body section of the template). The main point here is that the outcomeNames variable is defined as a four-item list, not as a single value. In cases where the values of a variable need to contain spaces, be sure to include the values in double quotes. For example, we might modify the outcomeNames field to be:
outcomeNames#outcome = "Conscientiousness Score" "Extraversion Score" "Agreeableness Score" "Openness Score";This point deserves emphasis: spaces are assumed to specify distinct values unless they are enclosed in double quotes. This behavior is similar to the Mplus language, where syntax such as VARIABLES = var1 var2 var3; defines three distinct variable names. Even where a given variable in the init section has only one value (such as filename above), double quotes must be used if the spaces are to be included in the created files, rather than being interpreted as a list.
Three variables must be defined for all Mplus template files.
First, the iterators variable defines which variables in the init section are iterators (i.e., integer variables to be looped over to create the input files). Iterators will be looped over in the order specified by the definition of iterators. Here is an example of three iterators that will create a total of 60 files (453).
[[init]]
iterators = outcome model classes;
outcome = 1:4;
model = 1:5;
classes = 2 3 4;
filename = "[[classes]]-class Model.inp";
outputDirectory = C:/Data/[[outcome]]/[[model]];
[[/init]]In the above example, the program will loop over outcome, model, and classes in that order. So the first file to be created would be outcome=1, model=1, classes=1, the second model would be outcome=1, model=1, classes=2, etc. For the more technical reader, iterators are processed recursively from left to right (here, classes within model within outcome). The top-most iterator is outcome and the bottom-most is classes. Don’t worry too much about this, though. In most cases, the ordering does not matter much and one generally will not hvae to think about how the program handles the iterators.
The second required init variable is filename. This variable defines the filenames for input files created by the createModels() function. In general, these should end in “.inp” to be consistent with Mplus conventions. Other tags can be (and probably should be) included in the filename definition. The idea is that the combination of filename and outputDirectory should define a unique file/path for each input file created by the createModels() function. Remember that if your filename contains any spaces, please use double quotes.
The third required init variable is outputDirectory. This variable defines the directory (or directories) where input files should be saved. Please note: If directories specified by outputDirectory do not exist, the createModels() function will create them, so be careful that the path is correct. If no output directory is specified, createModels() will place the input files in the R working directory (viewable by getwd() and settable by setwd()), but it is preferable always to specify an output directory. If an output directory is not an absolute path (i.e., one that begins with a drive letter, such as C:\), then the top level of the output directory will be placed within the working directory. Consider this value of outputDirectory:
[[init]]
iterators = outcome model;
outcome = 1:2;
model = 1:3;
outcomeName#outcome = Outcome1 Outcome2;
modelName#model = Model1 Model2 Model3;
outputDirectory = "C:/CFA/[[outcomeName#outcome]]/[[modelName#model]]";
filename="testfile.inp"
[[/init]]The above syntax will create the following directory structure:
CFA/
CFA/Outcome1/Model1
CFA/Outcome1/Model2
CFA/Outcome1/Model3
CFA/Outcome2/Model1
CFA/Outcome2/Model2
CFA/Outcome2/Model3It is generally recommended that users specify an absolute path (i.e., one that begins with a drive letter such as C:/) for outputDirectory to avoid any confusion about where the files will be saved. Note that it is typical to include tags in the definition of outputDirectory to allow for dynamic naming of the directories according to the model file being created. Consider this example, which defines both filename and outputDirectory to create a set of unique files:
[[init]]
iterators = outcome model;
outcome = 1:5;
model = 1:3;
outcomeDirNames#outcome = Conscientiousness Extraversion Agreeableness Openness Neuroticism;
modelNames#model = Poisson "Negative Binomial" "Negative Binomial Hurdle";
filename = "[[modelNames#model]] Growth Model.inp";
outputDirectory = "Template Output/[[outcomeDirNames#outcome]];"
[[/init]]In this case, because the outputDirectory is not an absolute path (i.e., does not begin with a drive letter), a directory called “Template Output” will be created within the R working directory (getwd()). Five subdirectories within “Template Output” will be created: “Conscientiousness”, “Extraversion”, “Agreeableness”, “Openness”, and “Neuroticism”. Within each of those directories, three files will be created: “Poisson Growth Model.inp”, “Negative Binomial Growth Model.inp”, and “Negative Binomial Hurdle Growth Model.inp”. The idea is that as createModels() iterates over outcome and model, the appropriate values of outcomeDirNames and modelNames will be inserted. As described in the list tag section below, the # separating the modelNames and model terms indicates that with each iteration of model, the matching element of the modelNames variable will be inserted.
There are four types of tags supported by the Mplus template language.
Here, we provide a complex complete example of a template file that generates 480 input files in numerous subfolders. This is probably overkill for most applications, but it gives a sense of what the language is capable of. The application here is growth mixture modeling for symptoms of DSM-IV personality disorders over time. There are 10 personality disorders and two groups of participants: high risk and low risk. In addition, one question of interest is whether a class constrained to 0 at the first measurement occasion with 0 growth captures a subgroup of participants. In addition, one might be interested in comparing whether these count data are better modeled by a continuous normal model versus a Poisson model, and whether Poisson GMM provides a better fit than Poission latent class growth analysis (LCGA). All of these considerations are reflected in this single template file. As a sane default, we chose to generate models that vary between 1 and 4 latent trajectory classes.
[[init]]
iterators = outcome group model classes zeroclass;
outcome = 1:10;
group = 2 5;
model = 1:3;
classes = 1:4;
zeroclass = 1:2;
outcomenames#outcome = Paranoid Schizoid Schizotypal Antisocial
Borderline Histrionic Narcissistic Avoidant Dependent OCPD;
groupnames#group = "Low Risk" "High Risk";
modelnames#model = "Normal LGCM" "Poisson GMM" "Poisson LCGA";
zeroclassnames#zeroclass = "" " with zero class";
#wave names are with respect to the outcome iterator
w1name#outcome = Paran1 Szoid1 Sztyp1 Anti1 Border1 Hist1
Narc1 Avoid1 Depend1 OCPD1;
w2name#outcome = Paran2 Szoid2 Sztyp2 Anti2 Border2 Hist2
Narc2 Avoid2 Depend2 OCPD2;
w3name#outcome = Paran3 Szoid3 Sztyp3 Anti3 Border3 Hist3
Narc3 Avoid3 Depend3 OCPD3;
filename = "[[classes]]-class [[groupnames#group]] [[outcomenames#outcome]] [[modelnames#model]][[zeroclassnames#zeroclass]].inp";
outputDirectory = "PD GMM/[[outcomenames#outcome]]/[[groupnames#group]]/Unconditional_Models/[[modelnames#model]]";
[[/init]]
TITLE: [[classes]]-class [[outcomenames#outcome]] [[groupnames#group]] [[modelnames#model]] Unconditional Model[[zeroclassnames#zeroclass]]
DATA: FILE = "personality_mplus.dat";
VARIABLE: NAMES ARE
id group sex age Paran1 Szoid1 Sztyp1 Anti1 Border1 Hist1 Narc1 Avoid1 Depend1
OCPD1 PaAg1 Sadist1 SelfDef1 Paran2 Szoid2 Sztyp2 Anti2 Border2 Hist2 Narc2
Avoid2 Depend2 OCPD2 Paran3 Szoid3 Sztyp3 Anti3 Border3 Hist3 Narc3 Avoid3
Depend3 OCPD3;
MISSING ARE .;
USEVARIABLES ARE [[w1name#outcome]] [[w2name#outcome]] [[w3name#outcome]];
USEOBSERVATIONS ARE group EQ [[group]]; ![[groupnames#group]] Only
[[model > 1]]
COUNT ARE [[w1name#outcome]] [[w2name#outcome]] [[w3name#outcome]];
[[/model > 1]]
CLASSES = c ([[classes]]);
ANALYSIS:
TYPE = MIXTURE;
STARTS = 1000 10;
K-1STARTS = 750 6;
PROCESSORS = 4;
[[model = 2]]
ALGORITHM = INTEGRATION;
[[/model = 2]]
MODEL:
%OVERALL%
Int Slope | [[w1name#outcome]]@0 [[w2name#outcome]]@0.97 [[w3name#outcome]]@2.77;
[[classes > 1]]
[[zeroclass = 2]]
!creates a class with all zeros at all time points
[[model=1]]
%c#2%
[Int@0 Slope@0];
Int@0 Slope@0;
[[/model=1]]
[[model>1]]
%c#2%
[Int@-15 Slope@-15];
Int@0 Slope@0;
[[/model>1]]
[[/zeroclass = 2]]
[[/classes > 1]]
PLOT:
Type = PLOT3;
Series = [[w1name#outcome]] (0) [[w2name#outcome]] (0.97) [[w3name#outcome]] (2.77);
OUTPUT: TECH1 TECH4 [[model != 1]]TECH10 [[/model != 1]][[classes > 1]]TECH11 TECH14 [[/classes > 1]]STANDARDIZED RESIDUAL;Rather than detailing this file line-by-line, a few points will be highlighted. First note that the group iterator has values of 2 and 5, which are non-contiguous and do not start at zero. This is fine for iterators, but note that the groupnames#group list variable has two elements (not 5). The essential idea is that iterators can take on any values (assuming they are unique) and the elements of the corresponding list tag should match one-to-one with the order of values (left to right) of the iterator.
Second, notice that conditional tags are used to specify that the numerical integration algorithm should only be used for model 2, which refers to Poisson GMM, whereas Poisson LCGA and Normal GMM do not require these features. Third, notice how two conditional tags are used on the last line to indicate when TECH10, TECH11, TECH14 should be included in the model estimation (TECH10 is only relevant for count outcomes and TECH11 and TECH14 are only relevant for multi-class models). Lastly, notice how nested conditional tags are used to specify a large block of code that is included only when creating a model with classes > 1 and zeroclass = 2. Here, zeroclass = 2 refers to models where an all-zero class is desired, whereas zeroclass = 1 indicates no zero class.
Caveats and limitations Circular tag definitionAlthough it is frequently useful to define init variables that include tags, be careful not to define two variables whose definitions depend on each other. This situation will result in a repetitive loop that cannot adequately resolve the tags (the program will inform you of this error). Here is an example of circular init variable definition:
iterators = model;
model = 1:3;
A = a_test1 a_test2 "a_test3[[B#model]]";
B = "b_test1 [[A#model]]" "b_test2 [[A#model]]" "b_test3 [[A#model]]";In this case, both list variables are defined with respect to the model iterator. When model is 3, the tag cannot be resolved because the tags are circularly defined.
At this point, conditional tags only support checking the status of a single proposition (e.g., model != 1). In some cases, it is useful to only include a certain piece of Mplus syntax when two or more conditions are met. The workaround in the current version of MplusAutomation is to define two conditional tags, such as this example:
[[model > 1]]
[[classes != 1]]
var3@0; !Mplus code here
[[/classes]]
[[/model > 1]]Checking for multiple conditions is on the short list of “to do” items and statements such as [[model > 1 && classes != 1]] should be available in a future version of the package.
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