Hi Thierry,
Thank you for your advice and speedy response.
Most of the data is closer to the lower bound (0). e.g. the mean time for
group A in zone A = 15.1 seconds and group A in zone B = 3.8 seconds.
However there are a very small number of outliers near the upper bound, the
largest being 294 out of the 300 seconds (see the attached file if you want
to look at the data).
I have taken a stab at running a Zero-inflated Beta GLMM using glmmTMB in
R like so:
betta_mod <- glmmTMB(prop_time ~ group*zone + (1|id),
family = beta_family(),
ziformula=~1,
data = glmm_zone_data)
summary(beta_mod)
*Family: beta ( logit )*
*Formula: prop_time ~ group * zone + (1 | id)Zero inflation:
~1Data: glmm_zone_data AIC BIC logLik deviance df.resid
-763.6 -736.3 388.8 -777.6 359Random effects:Conditional
model: Groups Name Variance Std.Dev. id (Intercept) 2.386e-09
4.885e-05Number of obs: 366, groups: id, 14Overdispersion parameter for
beta family (): 13.1Conditional model: Estimate Std. Error
z value Pr(>|z|) (Intercept) -2.7685 0.1031 -26.844 < 2e-16
***groupB -0.4455 0.1498 -2.975 0.002932 **zonezone_B
-0.4179 0.1524 -2.741 0.006124 **groupB:zonezone_B 0.8443
0.2190 3.855 0.000116 ***---Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*?
0.05 ?.? 0.1 ? ? 1Zero-inflation model: Estimate Std. Error z
value Pr(>|z|) (Intercept) -1.1804 0.1233 -9.575 <2e-16
***---Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1*
Does this look like the correct way of specifying the model? I am a little
confused about specifying and interpreting the zero-inflation component - I
have only just begun reading about this.
I noticed that the dispersion parameter is quite high at 13.1. I'm not
sure if this matters for beta models?. I tried running DHARMa
simulateResiduals on the model output and got significant deviations in the
dispersion (<2.2e-16) and KS tests. e.g.
DHARMa::testDispersion(beta_mod)
*DHARMa nonparametric dispersion test via sd of residuals fitted vs.
simulated*
*data: simulationOutput*
*ratioObsSim = 1.3612, p-value < 2.2e-16*
*alternative hypothesis: two.sided*
Many thanks,
Mike
On Mon, 17 May 2021 at 13:22, Thierry Onkelinx <thierry.onkelinx at inbo.be>
wrote:
Dear Michael,
Your data has bounds (lower bound at 0 and upper bound at 300) and you
have a lot of data close to a boundary. In such a case, a continuous
distribution which ignores those bound is not a good idea. If the time
spent outside of both zones is limited, then a long time in zone A excludes
a long time in zone B by definition. Then I'd look towards a multinomial
distribution. If the time spent outside both zones is dominant, then you
can use a zero-inflated beta as you suggested. A zero-inflated gamma might
be OK if the data is not too close to the upper boundary. If you are
considering zero-inflated beta vs zero-inflated gamma, then you should
choose zero-inflated beta IMHO.
Best regards,
ir. Thierry Onkelinx
Statisticus / Statistician
Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
AND FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be
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Op ma 17 mei 2021 om 13:52 schreef Michael Lawson via R-sig-mixed-models <
r-sig-mixed-models at r-project.org>:
Hello,
I am new to GLMMs and have a dataset where I have two distinct groups (A
and B) of 7 individuals each. The data consists of repeated measurements
of
each individual where the amount of time spent at either zone_A or zone_B
is recorded (out of a total time of 300s/observation period). For most of
the time period the individuals are in neither zone.
I want to test if group A and group B spend more time in zone A compared
to
zone B (and vice versa).
Speaking to someone else, they said I should use a Binomial GLMM using
cbind. i.e.
cbind(time_at_zone_A, time_at_zone_B) ~ group + (1| id).
However, the response variable is continuous (albeit with an upper bound
of
300 seconds per observation period), so I'm not sure if this is
appropriate?
Should I convert the response into a proportion and use something like a
Beta GLMM or else use a continuous (Gamma) GLMM? e.g. something like:
prop_time ~ zone*group + (1|id)
The data is quite heavily right-skewed and contains a lot of 0's, so
reading around it also looks like I may need to convert these into a
zero-inflated/hurdle model?
Thank you for any suggestions,
Mike
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