GLM mixed model with quasibinomial family for percentual response variable - r

Question

I don't have any 'treatment' except the passage of time (date), and 10 times points. I have a total of 43190 measurements, they are continuous binomial data (0.0 to 1.0) of the percentual response variable (canopycov). In glm logic, this is a quasibinomial case, but I find just only glmmPQL in MASS package for use, but the model is not OK and I have NA for p-values in all the dates. In my case, I try:
#Packages
library(MASS)
# Dataset
ds<-read.csv("https://raw.githubusercontent.com/Leprechault/trash/main/pred_attack_F.csv")
str(ds)
# 'data.frame': 43190 obs. of 3 variables:
# $ date : chr "2021-12-06" "2021-12-06" "2021-12-06" "2021-12-06" ...
# $ canopycov: int 22 24 24 24 25 25 25 25 26 26 ...
# $ rep : chr "r1" "r1" "r1" "r1" ...
# Binomial Generalized Linear Mixed Models
m.1 <- glmmPQL(canopycov/100~date,random=~1|date,
family="quasibinomial",data=ds)
summary(m.1)
#Linear mixed-effects model fit by maximum likelihood
# Data: ds
# AIC BIC logLik
# NA NA NA
# Random effects:
# Formula: ~1 | date
# (Intercept) Residual
# StdDev: 1.251838e-06 0.1443305
# Variance function:
# Structure: fixed weights
# Formula: ~invwt
# Fixed effects: canopycov/100 ~ date
# Value Std.Error DF t-value p-value
# (Intercept) -0.5955403 0.004589042 43180 -129.77442 0
# date2021-06-14 -0.1249648 0.006555217 0 -19.06341 NaN
# date2021-07-09 0.7661870 0.006363749 0 120.39868 NaN
# date2021-07-24 1.0582366 0.006434893 0 164.45286 NaN
# date2021-08-03 1.0509474 0.006432295 0 163.38607 NaN
# date2021-08-08 1.0794612 0.006442704 0 167.54784 NaN
# date2021-09-02 0.9312346 0.006395722 0 145.60274 NaN
# date2021-09-07 0.9236196 0.006393780 0 144.45595 NaN
# date2021-09-22 0.7268144 0.006359224 0 114.29293 NaN
# date2021-12-06 1.3109809 0.006552314 0 200.07907 NaN
# Correlation:
# (Intr) d2021-06 d2021-07-0 d2021-07-2 d2021-08-03 d2021-08-08
# date2021-06-14 -0.700
# date2021-07-09 -0.721 0.505
# date2021-07-24 -0.713 0.499 0.514
# date2021-08-03 -0.713 0.499 0.514 0.509
# date2021-08-08 -0.712 0.499 0.514 0.508 0.508
# date2021-09-02 -0.718 0.502 0.517 0.512 0.512 0.511
# date2021-09-07 -0.718 0.502 0.518 0.512 0.512 0.511
# date2021-09-22 -0.722 0.505 0.520 0.515 0.515 0.514
# date2021-12-06 -0.700 0.490 0.505 0.499 0.500 0.499
# d2021-09-02 d2021-09-07 d2021-09-2
# date2021-06-14
# date2021-07-09
# date2021-07-24
# date2021-08-03
# date2021-08-08
# date2021-09-02
# date2021-09-07 0.515
# date2021-09-22 0.518 0.518
# date2021-12-06 0.503 0.503 0.505
# Standardized Within-Group Residuals:
# Min Q1 Med Q3 Max
# -6.66259139 -0.47887669 0.09634211 0.54135914 4.32231889
# Number of Observations: 43190
# Number of Groups: 10
I'd like to correctly specify that my data is temporally pseudo replicated in a mixed-effects, but I don't find another approach for this. Please, I need any help to solve it.

Answer 1

I don't understand the motivation for a quasi-binomial model here, there's some nice discussion of the binomial and quasi binomial densities here and here that might be worth reading (including applications).
The problem with the code is that you have date as a character, so R doesn't know its a date. You will have to decide the units of measurement for time as well as the reference point, but the model works fine once you fix this.
ds<-read.csv("https://raw.githubusercontent.com/Leprechault/trash/main/pred_attack_F.csv")
str(ds)
head(ds)
class(ds$date)
ds$datex <- as.Date(ds$date)
summary(as.numeric(difftime(ds$datex, as.Date(Sys.Date(), "%d%b%Y"), units = "days")))
ds$date_time <- as.numeric(difftime(ds$datex, as.Date(Sys.Date(), "%d%b%Y"), units = "days"))
# scale for laplace
ds$sdate_time <- scale(ds$date_time)
m1 <- glmer(cbind(canopycov,100 - canopycov)~ date_time + (1|date_time),
family="binomial",data=ds)
summary(m1)
# Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
# Family: binomial ( logit )
# Formula: canopycov/100 ~ date_time + (1 | date_time)
# Data: ds
#
# AIC BIC logLik deviance df.resid
# 35109.7 35135.7 -17551.9 35103.7 43187
#
# Scaled residuals:
# Min 1Q Median 3Q Max
# -19.0451 -1.5605 -0.5155 -0.1594 0.8081
#
# Random effects:
# Groups Name Variance Std.Dev.
# date_time (Intercept) 0 0
# Number of obs: 43190, groups: date_time, 10
#
# Fixed effects:
# Estimate Std. Error z value Pr(>|z|)
# (Intercept) 8.5062394 0.0866696 98.15 <2e-16 ***
# date_time 0.0399010 0.0004348 91.77 <2e-16 ***
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# Correlation of Fixed Effects:
# (Intr)
# date_time 0.988
# optimizer (Nelder_Mead) convergence code: 0 (OK)
# boundary (singular) fit: see ?isSingular
m2 <- MASS::glmmPQL(canopycov/100~ date_time,random=~1|date_time,
family="quasibinomial",data=ds)
summary(m2)
# Linear mixed-effects model fit by maximum likelihood
# Data: ds
# AIC BIC logLik
# NA NA NA
#
# Random effects:
# Formula: ~1 | date_time
# (Intercept) Residual
# StdDev: 0.3082808 0.1443456
#
# Variance function:
# Structure: fixed weights
# Formula: ~invwt
# Fixed effects: canopycov/100 ~ date_time
# Value Std.Error DF t-value p-value
# (Intercept) 0.1767127 0.0974997 43180 1.812443 0.0699
# date_time 0.3232878 0.0975013 8 3.315728 0.0106
# Correlation:
# (Intr)
# date_time 0
#
# Standardized Within-Group Residuals:
# Min Q1 Med Q3 Max
# -6.66205315 -0.47852364 0.09635514 0.54154467 4.32129236
#
# Number of Observations: 43190
# Number of Groups: 10