Dear Mike,
The zero-inflation is specified on the logit scale. plogis(-1.18) = 0.235
23.5% zero seems reasonable when reading your story. (Didn't look at the
data).
You need to look at the definition for the "over"dispersion parameter.
For a beta distribution is \phi with Var(y) = \mu * (1 - \mu) / (\phi + 1)
(see ?glmmTMB::beta_family) Hence a large value of \phi implies a low
variance.
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
///////////////////////////////////////////////////////////////////////////////////////////
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey
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Op ma 17 mei 2021 om 15:45 schreef Michael Lawson <mrml500 at york.ac.uk>:
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
///////////////////////////////////////////////////////////////////////////////////////////
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does
not ensure that a reasonable answer can be extracted from a given body of
data. ~ John Tukey
///////////////////////////////////////////////////////////////////////////////////////////
<https://www.inbo.be>
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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