Message-ID: <AM6PR0102MB31748BFF6EAF4FEA67D042809E9D9@AM6PR0102MB3174.eurprd01.prod.exchangelabs.com>
Date: 2021-02-26T10:20:18Z
From: Andre Syvertsen
Subject: Help: Interpreting unusual model fit results from generalized linear mixed model (glmmTMB/sjPlot)
Hi,
I am working with a large dataset that contains longitudinal data on gambling behavior of 184,113 participants. The data is based on complete tracking of electronic gambling behavior within a gambling operator. Gambling behavior data is aggregated on a monthly level, a total of 70 months. I have an ID variable separating participants, a time variable (months), as well as numerous gambling behavior variables such as active days played for given month, bets placed for given month, total losses for given month, etc. I am investigating the role of age and gender in predicting active days gambling per month.
I have fitted a model with glmmTMB (see below for model code) and outputed the resulting statistics with sjPlot's tab_model function which I am having trouble interpreting. The full results can be found below. Notably, I appear to have gotten perfect intra-class correlation. While, I am sure variance in outcome responses (active days gambling) are likely to be heavily associated with subject and time, this seems excessive. Furthermore, the pseudo R2 suggests that 8.7% of the variance should be attributable to fixed effects which I would think would lower the variance attributable to individual/time? Are the results affected by the high number of observations, individuals and/or time points? Or maybe I have specified my model in an odd manner?
glmmTMB code for the model:
DaysPlayedConditionalAgeGenderTruncated <- glmmTMB(daysPlayed ~ 1 + time + ageCategory * gender + (time | id), dfLong, family = truncated_nbinom2)
Model summary:
Active Gambling Days Monthly
Predictors
Incidence Rate Ratios
CI
p
(Intercept)
1.18
1.16 ?C 1.20
<0.001
Time
0.99
0.99 ?C 0.99
<0.001
Age Category 30-39
1.29
1.26 ?C 1.32
<0.001
Age Category 40-49
1.81
1.78 ?C 1.85
<0.001
Age Category 50-59
2.47
2.41 ?C 2.53
<0.001
Age Category 60-69
3.08
2.99 ?C 3.17
<0.001
Age Category 70+
3.42
3.29 ?C 3.56
<0.001
Gender: Women
1.69
1.65 ?C 1.74
<0.001
Age Category 30-39:Women
0.90
0.86 ?C 0.94
<0.001
Age Category 40-49:Women
0.67
0.65 ?C 0.70
<0.001
Age Category 50-59:Women
0.53
0.50 ?C 0.55
<0.001
Age Category 60-69: Women
0.46
0.44 ?C 0.48
<0.001
Age Category 70+:Women
0.45
0.43 ?C 0.48
<0.001
Random Effects
??2
0.00
??00 id
1.63
??11 id.time
0.00
??01 id
-0.38
ICC
1.00
N id
184113
Observations
3231544
Marginal R2 / Conditional R2
0.087 / 1.000
Note. Intercept = Men, age 18-29 years, first time point (month 0 of 69).
Kind regards,
Andr??
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