The MplusAutomation package leverages the flexibility of
the R language to automate latent variable model estimation
and interpretation using Mplus, a powerful latent variable modeling
program developed by Muthén and Muthén (www.statmodel.com).
Specifically, MplusAutomation provides routines for
creating related groups of models, running batches of models, and
extracting and tabulating model parameters and fit statistics.
You can install the latest release of MplusAutomation
directly from CRAN by
running
install.packages("MplusAutomation")Alternately, if you want to try out the latest development
MplusAutomation code, you can install it straight from
github using Hadley Wickham’s devtools package. If you do
not have devtools installed, first install it and then
install MplusAutomation.
#install.packages("devtools")
library(devtools)
install_github("michaelhallquist/MplusAutomation")For questions, answers, and updates on the status of the
MplusAutomation package, email or subscribe to the Google
group list.
You can find a detailed example of how to use the
MplusAutomation package in the vignette
.
Here is an example of using the package to run a simple path model
using the mtcars dataset built into R.
library(MplusAutomation)
pathmodel <- mplusObject(
TITLE = "MplusAutomation Example - Path Model;",
MODEL = "
mpg ON hp;
wt ON disp;",
OUTPUT = "CINTERVAL;",
rdata = mtcars)
## R variables selected automatically as any variable name that occurs in the MODEL, VARIABLE, or DEFINE section.
## If any issues, suggest explicitly specifying USEVARIABLES.
## A starting point may be:
## USEVARIABLES = mpg disp hp wt;
fit <- mplusModeler(pathmodel, modelout = "model1.inp", run = 1L)That is all it takes to run Mplus! MplusAutomation takes
care of figuring out which variables from your R dataset
are used in the model and which are not (if it get’s confused, you can
also specify usevariables). It creates a dataset suitable
for Mplus, calls Mplus to run the model on the dataset, and reads it
back into R.
There is even pretty printing now. To see the results:
library(texreg)
screenreg(fit, summaries = c("Observations", "CFI", "SRMR"), single.row=TRUE)
==================================
Model 1
----------------------------------
MPG<-HP -0.06 (0.01) ***
WT<-DISP 0.01 (0.00) ***
WT<->MPG -1.02 (0.38) **
MPG<-Intercepts 29.59 (1.53) ***
WT<-Intercepts 1.82 (0.18) ***
MPG<->MPG 14.04 (3.52) ***
WT<->WT 0.21 (0.06) ***
----------------------------------
Observations 32
CFI 0.87
SRMR 0.14
==================================
*** p < 0.001, ** p < 0.01, * p < 0.05
The fit is not great, to add some extra paths we can update the model.
pathmodel2 <- update(pathmodel, MODEL = ~ . + "
mpg ON disp;
wt ON hp;")
fit2 <- mplusModeler(pathmodel2, modelout = "model2.inp", run = 1L)We can make some pretty output of both models:
screenreg(list(
extract(fit, summaries = c("Observations", "CFI", "SRMR")),
extract(fit2, summaries = c("Observations", "CFI", "SRMR"))),
single.row=TRUE)
====================================================
Model 1 Model 2
----------------------------------------------------
MPG<-HP -0.06 (0.01) *** -0.02 (0.01)
WT<-DISP 0.01 (0.00) *** 0.01 (0.00) ***
WT<->MPG -1.02 (0.38) ** -0.73 (0.26) **
MPG<-Intercepts 29.59 (1.53) *** 30.74 (1.27) ***
WT<-Intercepts 1.82 (0.18) *** 1.68 (0.19) ***
MPG<->MPG 14.04 (3.52) *** 8.86 (2.21) ***
WT<->WT 0.21 (0.06) *** 0.19 (0.05) ***
MPG<-DISP -0.03 (0.01) ***
WT<-HP 0.00 (0.00)
----------------------------------------------------
Observations 32 32
CFI 0.87 1.00
SRMR 0.14 0.00
====================================================
*** p < 0.001, ** p < 0.01, * p < 0.05
If you want confidence intervals, those can also be printed, so long as they were requested as part of the output (we did in the initial model, which propogates to later models that were updated()ed based on the original model):
screenreg(list(
extract(fit, cis=TRUE, summaries = c("Observations", "CFI", "SRMR")),
extract(fit2, cis=TRUE, summaries = c("Observations", "CFI", "SRMR"))),
single.row=TRUE)
================================================================
Model 1 Model 2
----------------------------------------------------------------
MPG<-HP -0.06 [-0.08; -0.05] * -0.02 [-0.05; 0.00]
WT<-DISP 0.01 [ 0.00; 0.01] * 0.01 [ 0.01; 0.01] *
WT<->MPG -1.02 [-1.77; -0.27] * -0.73 [-1.25; -0.21] *
MPG<-Intercepts 29.59 [26.59; 32.58] * 30.74 [28.25; 33.22] *
WT<-Intercepts 1.82 [ 1.46; 2.17] * 1.68 [ 1.31; 2.04] *
MPG<->MPG 14.04 [ 7.14; 20.95] * 8.86 [ 4.52; 13.20] *
WT<->WT 0.21 [ 0.10; 0.32] * 0.19 [ 0.10; 0.28] *
MPG<-DISP -0.03 [-0.04; -0.02] *
WT<-HP -0.00 [-0.00; 0.00]
----------------------------------------------------------------
Observations 32 32
CFI 0.87 1.00
SRMR 0.14 0.00
================================================================
* 0 outside the confidence interval
If you have a tutorial or examples using
MplusAutomation, please add them to the github Wiki.
In addition, on the Wiki,
is a list of publications that cite or use MplusAutomation.
If you use MplusAutomation in your own work — papers,
posters, presentations, etc. — please add a citation to the list, and if
possible, include an abstract or link to the full text. This helps us
get to know our users and how MplusAutomation is being
used.
Finally, if you find bugs or have suggestions for new features or
ways to enhance MplusAutomation, please let us know! Just
click the ‘Issues’ button at the top of the github page or go here
and open a New Issue.
Lastly, if you use MplusAutomation and have space, we
greatly appreciating citations. In addition to being easier to track,
the recognition and credit help make it easier for us to continue
putting our time into developing and sharing this software!