Error message in anova() when using MLR and FIML

[JB]

Dear All,
I am using FIML and MLR in multi-group analysis. I get an error when I try to compare models with anova(). I do not get an error when I choose either MLR or FIML. I reproduced the error I get with the HS.model, see syntax below. The error is:

Error in if (scaling.factor < 0) scaling.factor <- as.numeric(NA) :
  missing value where TRUE / FALSE  is needed

Is it that I am not supposed to compare models when using both methods?

Thanks!

JB


HS.model <- 'visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9'

#create missing pattern
HSMiss <- HolzingerSwineford1939[,paste("x", 1:9, sep="")]
randomMiss <- rbinom(prod(dim(HSMiss)), 1, 0.1)
randomMiss <- matrix(as.logical(randomMiss), nrow=nrow(HSMiss))
HSMiss[randomMiss] <- NA
attach(HolzingerSwineford1939)
HSMiss=cbind(HSMiss,school)
detach(HolzingerSwineford1939)

 fit <- cfa(HS.model, data = HSMiss,group="school",missing="FIML",estimator="MLR")
fit1 <- cfa(HS.model, data = HSMiss,group="school",group.equal="loadings",missing="FIML",estimator="MLR")
anova(fit,fit1)
 

 

[yrosseel]

On 03/25/2013 07:52 PM, JB wrote:
> Dear All,
> I am using FIML and MLR in multi-group analysis. I get an error when I
> try to compare models with anova(). I do not get an error when I choose
> either MLR or FIML. I reproduced the error I get with the HS.model, see
> syntax below. The error is:
>
> Error in if (scaling.factor < 0) scaling.factor <- as.numeric(NA) :
> missing value where TRUE / FALSE is needed

That certainly looks like a bug. Will be fixed ASAP.

Yves.

[Tobias Ludwig]

I do have the same issue here!

[JB]

Hello,
I have come back to analyses with missing and non normal data, thus using Fiml and MLR in combination and I still get the same error with anova (see post above). I installed the latest release of lavaan but I get the same issue. Given the data I work on, I often need this configuration so it would be great if this was fixed...
Thanks a lot for lavaan and Happy Christmas 2013!
JB

[yrosseel]

On 12/22/2013 04:01 PM, JB wrote:
> Hello,
> I have come back to analyses with missing and non normal data, thus
> using Fiml and MLR in combination and I still get the same error with
> anova (see post above). I installed the latest release of lavaan but I
> get the same issue. Given the data I work on, I often need this
> configuration so it would be great if this was fixed...

I will fix this after the holidays (and certainly before 0.5-16) gets
released.

In the meantime, you can use the (hidden) SB.classic=TRUE option as in

anova(fit1, fit2, SB.classic = TRUE)

(In 0.5-16-dev, there is function lavTestLRT() which is a wrapper around
the anova() function; this one contains proper documentation).


Yves.

[JB]

Dear Yves,

Thanks for your answer! SB.classic works fine in the previous example (first post of this thread). Version .5-16-dev lavTestLRT(fit,fit1,SB.classic=T) also worked fine. Same thing with my data with one exception. I compared a series of nested models and the first one is a completely saturated model. When I run anova(saturated.model, constrained.model) I get a chi square diff equal to the model chi square I get in summary(constrained.model), as it should. However, when I use lavTestLRT(saturated.model, constrained.model,SB.classic=T) I get :

Error in if (any(cd1 < 0)) { : missing value where TRUE/FALSE needed

It is not very important as I can get the values anyway but it is still practical to extract results from a series of comparisons…

Many Thanks for your work!

JB

[Yves Rosseel]

It turns out this is much more difficult to fix (the original problem with FIML and MLR) than expected. Therefore, in 0.5-16, SB.classic=TRUE will be the default again (both in lavTestLRT() and anova()).

The good news is: it should work with saturated models.

Yves.

[yleni...@gmail.com]

Hi Yves,

With version 0.5-20, whenever I use MLR and FIML, lavTestLRT() only uses the chi-square from ML, rather than MLR.

The output starts by stating the following: Scaled Chi Square Difference Test (method = "satorra.bentler.2001"). However, the chi-square is from ML estimation.

If I use anova or compareFit (from semTools) I get the same problem.

I can only do a scaled chi-square difference test with MLR and FIML, if I run measurementInvariance(). As useful as that function is, it would be great if lavTestLRT worked with MLR as well, as it would allow more control over the invariance testing process.

Thanks for all your work,

Ylenio

[yrosseel]

On 12/23/2015 07:17 PM, yleni...@gmail.com wrote:
> Hi Yves,
>
> With version 0.5-20, whenever I use MLR and FIML, lavTestLRT() only uses
> the chi-square from ML, rather than MLR.
>
> The output starts by stating the following: Scaled Chi Square Difference
> Test (method = "satorra.bentler.2001"). However, the chi-square is from
> ML estimation.

That is OK. The 'scaled' difference test scales the difference between
the non-scaled test statistics. See the references in ?lavTestLRT

Yves.

[yleni...@gmail.com]

Thanks Yves. That’s great. I just wanted to make sure. 

[Serena]

Hi there,

I also met a similar problem but with sem() when using MLR and FIML.

I have a standardized score of a variable that includes missing values. When I run a single-level model, it works well. 

However, when I run a multilevel model, it shows the error 'Error in if (scaling.factor < 0) scaling.factor <- as.numeric(NA) : missing value where TRUE/FALSE needed'.

If I remove either estimator = "MLR" or missing = "FIML", it works!

Does this mean I can not use both to run a multilevel model that includes missing values (might be just for standardized variables)?

Thanks in advance!

Serena

[Terrence Jorgensen]

Please provide a reprex.  But first verify that this still occurs with the latest development version of lavaan.  You can install it using the remotes package:

remotes::install_github("yrosseel/lavaan")

Terrence D. Jorgensen    (he, him, his)
Assistant Professor, Methods and Statistics
Research Institute for Child Development and Education, the University of Amsterdam
http://www.uva.nl/profile/t.d.jorgensen

[Mugo Muiruri]

good

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