Rich Jones with all credit to Joshua Wiley
Tuesday, December 02, 2014
mplusModeler is part of the MplusAutomation package.
It can be used to call Mplus interactively.
I will show you how.
This code was prepared by Josh Wiley, and shared without permission but I think he would d be cool with it.
Note: must needs R 3.1.2+ and MplusAutomation 0.6-3
setwd("c:/work/syntax")There are those in the R community who think this is a bad idea, setting the working directory.
I think they are wrong. Just do all your setwd’ing at the top of the file and make all folder referencing that follows using relative paths. It’ll be OK.
library(MplusAutomation)
library(texreg)
library(datasets)In case you’ve run this model before and want to clean up any leftover output and input and dat files. Note that mplusModeler will overwrite by default. But belts and suspenders.
foo <- file.remove("mtcars.inp")
foo <- file.remove("mtcars.out")
foo <- file.remove("mtcars.dat")res <- mplusModeler(
mplusObject(
MODEL = "mpg ON wt hp; wt WITH hp;", rdata = mtcars),
"mtcars.dat", run = 1L)## No R variables to use specified.
## Selected automatically as any variable name that occurs in the MODEL or DEFINE section.
## Wrote model to: mtcars.inp
## Wrote data to: mtcars.dat##
## Running model: mtcars.inp
## System command: C:\Windows\system32\cmd.exe /c cd "C:\WORK\Syntax" && "Mplus" "mtcars.inp"
## Reading model: mtcars.outpwd <- getwd()
list.files(pwd, "mtcars.*")## [1] "mtcars.dat" "mtcars.inp" "mtcars.out"summary(res)## Estimated using ML
## Number of obs: 32, number of (free) parameters: 9
##
## Model: Chi2(df = 0) = 0, p = 0
## Baseline model: Chi2(df = 2) = 56.103, p = 0
##
## Fit Indices:
##
## CFI = 1, TLI = 1, SRMR = 0
## RMSEA = 0, 90% CI [0, 0], p < .05 = 0
## AIC = 597.217, BIC = 610.408screenreg(res, single.row=TRUE)##
## =======================================
## Model 1
## ---------------------------------------
## MPG<-WT -3.88 (0.60) ***
## MPG<-HP -0.03 (0.01) ***
## WT<->HP 42.81 (13.76) **
## HP<-Means 146.69 (11.93) ***
## WT<-Means 3.22 (0.17) ***
## MPG<-Intercepts 37.23 (1.52) ***
## HP<->HP 4553.97 (1138.49) ***
## WT<->WT 0.93 (0.23) ***
## MPG<->MPG 6.09 (1.52) ***
## =======================================
## *** p < 0.001, ** p < 0.01, * p < 0.05cat( readLines( "mtcars.out" ) , sep = "\n" )## Mplus VERSION 7.11
## MUTHEN & MUTHEN
## 12/02/2014 2:56 PM
##
## INPUT INSTRUCTIONS
##
## DATA:
## FILE = "mtcars.dat";
##
## VARIABLE:
## NAMES = mpg hp wt;
## MISSING=.;
##
## MODEL:
## mpg ON wt hp; wt WITH hp;
##
##
##
## INPUT READING TERMINATED NORMALLY
##
##
##
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 32
##
## Number of dependent variables 1
## Number of independent variables 2
## Number of continuous latent variables 0
##
## Observed dependent variables
##
## Continuous
## MPG
##
## Observed independent variables
## HP WT
##
##
## Estimator ML
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## mtcars.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 1
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.100
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## MPG HP WT
## ________ ________ ________
## MPG 1.000
## HP 1.000 1.000
## WT 1.000 1.000 1.000
##
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 9
##
## Loglikelihood
##
## H0 Value -289.608
## H1 Value -289.608
##
## Information Criteria
##
## Akaike (AIC) 597.217
## Bayesian (BIC) 610.408
## Sample-Size Adjusted BIC 582.351
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 0.000
## Degrees of Freedom 0
## P-Value 0.0000
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.000
## 90 Percent C.I. 0.000 0.000
## Probability RMSEA <= .05 0.000
##
## CFI/TLI
##
## CFI 1.000
## TLI 1.000
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 56.103
## Degrees of Freedom 2
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.000
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## MPG ON
## WT -3.878 0.602 -6.438 0.000
## HP -0.032 0.009 -3.696 0.000
##
## WT WITH
## HP 42.812 13.757 3.112 0.002
##
## Means
## HP 146.688 11.929 12.296 0.000
## WT 3.217 0.170 18.898 0.000
##
## Intercepts
## MPG 37.227 1.522 24.459 0.000
##
## Variances
## HP 4553.965 1138.491 4.000 0.000
## WT 0.927 0.232 4.000 0.000
##
## Residual Variances
## MPG 6.095 1.524 4.000 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.116E-04
## (ratio of smallest to largest eigenvalue)
##
##
## Beginning Time: 14:56:46
## Ending Time: 14:56:46
## Elapsed Time: 00:00:00
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2013 Muthen & MuthensessionInfo()## R version 3.1.2 (2014-10-31)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
##
## 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] texreg_1.34 MplusAutomation_0.6-3
##
## loaded via a namespace (and not attached):
## [1] boot_1.3-13 coda_0.16-1 digest_0.6.4 evaluate_0.5.5
## [5] formatR_1.0 grid_3.1.2 gsubfn_0.6-6 htmltools_0.2.6
## [9] knitr_1.7 lattice_0.20-29 plyr_1.8.1 proto_0.3-10
## [13] Rcpp_0.11.3 rmarkdown_0.3.10 stringr_0.6.2 tcltk_3.1.2
## [17] tools_3.1.2 xtable_1.7-4 yaml_2.1.13