X Mplus is not available.
Closed this issue · 11 comments
I am about to try tidySEM, but tidySEM seems not to find Mplus:
> library(tidySEM)
> library(lavaan)
> library(MplusAutomation)
> df <- HolzingerSwineford1939
> names(df)[grepl("^x", names(df))] <- c("vis_1", "vis_2", "vis_3", "tex_1",
"tex_2", "tex_3", "spe_1", "spe_2", "spe_3")
> model <- tidy_sem(df)
> model <- model |> measurement()
> model |> estimate_lavaan()
lavaan 0.6-9 ended normally after 35 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 21
Number of observations 301
Model Test User Model:
Test statistic 85.306
Degrees of freedom 24
P-value (Chi-square) 0.000
> model |> estimate_mplus()
X Mplus is not available.
NULL
>
I'm on a Mac. I have no problem running Mplus from R with MplusAutomation. Mplus is installed under the following path:
/Applications/Mplus/Mplus Editor.app
I have tried restarting R after installing tidySEM.
Dear @cbratt, this is an interesting case, because tidySEM relies completely on MplusAutomation here. What happens when you run mplusAvailable(TRUE) == 0 ?
Thank you so much for your speedy reply!
> mplusAvailable(TRUE) == 0
[1] FALSE
> mplus_model <- mplusObject(
+ VARIABLE = "usevariables = vis_1 vis_2;",
+ MODEL = "vis_1 WITH vis_2;",
+ rdata = df
+ )
> mplus_test <- mplusModeler(mplus_model, modelout = "model.inp", run = 1L)
> summary(mplus_test)
Estimated using ML
Number of obs: 301, number of (free) parameters: 5
Model: Chi2(df = 0) = 0, p = 0
Baseline model: Chi2(df = 1) = 27.863, p = 0
Fit Indices:
CFI = 1, TLI = 1, SRMR = 0
RMSEA = 0, 90% CI [0, 0], p < .05 = 0
AIC = 1880.066, BIC = 1898.602
>
Interesting :) Looks like the bug is in MplusAutomation::mplusAvailable(). It might be worth opening an issue on their github.
One final question: What value does this return? print(MplusAutomation::mplusAvailable())
Thank you for looking into this!
> print(MplusAutomation::mplusAvailable())
[1] 1
>
I have done as you suggested: https://github.com/michaelhallquist/MplusAutomation/issues/148
Just to let you know: I downgraded MplusAutomation to 0.7-3. The error persists, which makes me wonder... If this bug is not new, it should have been detected by others long ago. Could it be that my setup causes the error?
Yes; I'm quite sure that it is "caused" by your setup, but it's still a bug ;) That function should do what it needs to do on any system. Is there anything special about your setup that you think might cause it? Linux? Server session? No admin rights? Etc
All is local, on my own machine (macOS).
> version
_
platform x86_64-apple-darwin17.0
arch x86_64
os darwin17.0
system x86_64, darwin17.0
status
major 4
minor 1.0
year 2021
month 05
day 18
svn rev 80317 ##
language R
version.string R version 4.1.0 (2021-05-18)
nickname Camp Pontanezen
RStudio Version 1.4.1717, but that shouldn't be relevant, I guess.
Very thankful for the work you do for the community, and of course now, in trying to solve this specific case.
================================================<-
Added a few hours later:
I just tried on a different computer (MacBook), with a similar setup. Same problem there.
Thanks to @JWiley, tidySEM can now find Mplus. But @cjvanlissa, I don't think tidySEM does more than find Mplus?
> library(tidySEM)
> library(lavaan)
> library(MplusAutomation)
> df <- HolzingerSwineford1939
> names(df)[grepl("^x", names(df))] <- c("vis_1", "vis_2", "vis_3", "tex_1",
+ "tex_2", "tex_3", "spe_1", "spe_2", "spe_3")
> model <- tidy_sem(df)
> model <- model |> measurement()
> estimate_lavaan(model)
lavaan 0.6-9 ended normally after 35 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 21
Number of observations 301
Model Test User Model:
Test statistic 85.306
Degrees of freedom 24
P-value (Chi-square) 0.000
> estimate_mplus(model)
v Mplus is available.
>
(I checked the working directory. No output there, it seems. And evidently, no output in the R console apart from celebrating that Mplus can be detected.)
@cjvanlissa, I fully understand your attitude towards continued support for Mplus (expressed in response to another post of mine), Mplus is neither open source nor free. I would definitely prefer lavaan, Mplus is expensive in the long run and thus exclusive, and also cumbersome to use (although MplusAutomation helps a lot). But Mplus is still what many of us need to be able to develop advanced latent variable modelling, and Mplus is robust and remarkably fast. If you with tidySEM can make OpenMX more accessible, then that would of course be an interesting alternative. I still think you might reach more people with support for Mplus. That could be a dilemma.
Unfortunately, I'm still stuck with tidySEM even after the new development version of Mplus.
Have you tried assigning the result, e.g., estimate_mplus(model) -> res; summary(res)?
I wouldn't say that people "need" Mplus to develop advanced lv modeling, but people definitely use it. I would like to contribute to changing that.
Regarding "robust", I'm not sure how you define this. Regarding fast, it definitely isn't. Have you tried Hamiltonian Monte Carlo in STAN? I'm uncomfortable with the fact that Mplus includes many proprietary solutions that are not sufficiently documented or tested. What if there is a mistake? E.g., I already noticed that Mplus often does not arrive at the global optimum for mixture modeling. That's not great, considering mixture modeling is one of the reasons I hear most often for using Mplus.
I see that tidySEM still has summary and print methods for MplusAutomation objects; this is a remnant from when these were not yet incorporated in MplusAutomation. Removing these will show summary(res) by default when the object is printed.
@cjvanlissa You are so right!
Following up the code above (factor model for HolzingerSwineford1939):
> res <- estimate_mplus(model)
v Mplus is available.
> summary(res)
Estimated using ML
Number of obs: 301, number of (free) parameters: 30
Model: Chi2(df = 24) = 85.306, p = 0
Baseline model: Chi2(df = 36) = 918.852, p = 0
Fit Indices:
CFI = 0.931, TLI = 0.896, SRMR = 0.06
RMSEA = 0.092, 90% CI [0.071, 0.114], p < .05 = 0.001
AIC = 7535.49, BIC = 7646.703
Thanks a lot for your assistance.