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List:       r-sig-mixed-models
Subject:    Re: [R-sig-ME] Mixed effects model with many zeros
From:       "Philippi, Tom via R-sig-mixed-models" <r-sig-mixed-models () r-project ! org>
Date:       2020-06-04 22:35:49
Message-ID: BYAPR09MB3189D507568F22FB2C42C297F3890 () BYAPR09MB3189 ! namprd09 ! prod ! outlook ! com
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No, to me  that does not seem like a reasonable way to analyze the data you describe, \
although I'm coming from a very different field so I could be off base.  The zero \
inflation and skew are artifacts of treating ordinal categories as numeric, and \
category "0" as numeric 0.

Is there a reason you don't want to model your ordered categorical response as \
ordered categorical?  There could be something about patterns across the 10 questions \
I'm missing, but for what you describe, perhaps ordinal::clmm() or something in the \
mixor package would do what you need.  

Note that glmmTMB is unlikely to add ordinal responses:
https://github.com/glmmTMB/glmmTMB/issues/514

Tom


-----Original Message-----
From: R-sig-mixed-models <r-sig-mixed-models-bounces@r-project.org> On Behalf Of \
                Austen Anderson via R-sig-mixed-models
Sent: Thursday, June 4, 2020 2:49 PM
To: r-sig-mixed-models@r-project.org
Subject: [EXTERNAL] [R-sig-ME] Mixed effects model with many zeros

Hi, I've got a set of longitudinal data with negative affect as the dependent \
variable. Negative affect was measured by 10 items asking about how much of the day \
the participant felt 10 different negative emotions (ordinal scale from 0-4). The \
modal response to that survey was 0 for all ten items, resulting in a large number of \
zero's for that variable along with a strong right skew. I've been exploring \
CrossValidated and other sources to get a sense of what my options are for modeling \
this data. I've read about Tweedie models, Tobit (censored) models, hurdle models, \
beta distribution models, and zero-inflated gamma models. As far as I could \
understand, the Tweedie model seemed reasonable and I modeled it this way: \
neg_nat_mod_tweed <- glmmTMB(negaff ~ enjoynat_c + enjoynat_mean + daynum + (1|MRID), \
data = daily,                         family = tweedie)summary(neg_nat_mod_tweed)

Family: tweedie  ( log )
Formula:          negaff ~ enjoynat_c + enjoynat_mean + daynum + (1 | MRID)Data: \
                daily
     AIC      BIC   logLik deviance df.resid   4637.0   4683.6  -2311.5   4623.0     \
5753 Random effects: Conditional model: Groups Name        Variance Std.Dev. MRID   \
(Intercept) 1.19     1.091   Number of obs: 5760, groups:  MRID, 782 Overdispersion \
parameter for tweedie family (): 0.248 Conditional model:               Estimate Std. \
Error z value Pr(>|z|)    (Intercept)   -1.653574   0.075059 -22.030  < 2e-16 \
***enjoynat_c    -0.132666   0.033037  -4.016 5.93e-05 ***enjoynat_mean  0.009201   \
0.134737   0.068    0.946    daynum        -0.087213   0.005578 -15.636  < 2e-16 \
***---Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

I have a few questions. First, does it seem like this is a reasonable way to analyze \
this data? If not, do you have other recommendations? Second, while the manual for \
GLMMtmb provides the Tweedie model as an option, here \
(https://cran.r-project.org/web/packages/glmmTMB/vignettes/glmmTMB.pdf) it says it is \
not yet implemented. Does anyone know if this model is trustworthy? Lastly, it \
mentions that the link function is log. I am still learning about how link functions \
work and I am not sure how to make sense of the coefficients because in their current \
form the negative intercept makes no sense. Can you offer some guidance on \
interpretation? Thank you for your time,Austen
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