I have been using glmulti to obtain model averaged estimates and relative importance values for my variables of interest. In running glmulti I specified a candidate model for which all variables and interactions were included based on a priori knowledge (see code below).
After running the glmutli model I studied the results by using the functions summary() and weightable(). There seem to be a number of strange things going on with the results which I do not understand.
First of all, when I run my candidate model with lme4 glmer() function I obtain an AIC value of 2086. In the glmulti output this candidate model (with exactly the same formula) has a lower AIC value (2107), as a result of which it appears at position 8 out of 26 in the list of all potential models (as obtained through the weigtable() function).
What seems to be causing this problem is that the logArea:Habitat interaction is dropped from the candidate model, despite level=2 being specified. The function summary(output_new#objects[[8]]) provides a different formula (without the logArea:Habitat interaction variable) compared to the formula provided through weightable(). This explains why the candidate model AIC value is not the same as obtained through lme4, but I do not understand why the interaction variables logArea:Habitat is missing from the formula. The same is happening for other possible models. It seems that for all models with 2 or more interactions, one interaction is dropped.
Does anyone have an explanation for what is going on? Any help would be much appreciated!
Best,
Robert
Note: I have created a subset of my data (https://drive.google.com/open?id=1rc0Gkp7TPdnhW6Bw87FskL5SSNp21qxl) and simplified the candidate model by removing variables in order to decrease model run time. (The problem remains the same)
newdat <- Data_ommited2[, c("Presabs","logBodymass", "logIsolation", "Matrix", "logArea", "Protection","Migration", "Habitat", "Guild", "Study","Species", "SpeciesStudy")]
glmer.glmulti <- function (formula, data, random, ...) {
glmer(paste(deparse(formula), random), data = data, family=binomial(link="logit"),contrasts=list(Matrix=contr.sum, Habitat=contr.treatment, Protection=contr.treatment, Guild=contr.sum),glmerControl(optimizer="bobyqa", optCtrl = list(maxfun = 100000)))
}
output_new <- glmulti(y = Presabs ~ Matrix + logArea*Protection + logArea*Habitat,
data = sampledata,
random = '+(1|Study)+(1|Species)+(1|SpeciesStudy)',
family = binomial,
method = 'h',
level=2,
marginality=TRUE,
crit = 'aic',
fitfunc = glmer.glmulti,
confsetsize = 26)
print(output_new)
summary(output_new)
weightable(output_new)
I found a post (https://stats.stackexchange.com/questions/341356/glmulti-package-in-r-reporting-incorrect-aicc-values) of someone who encountered the same problem and it appears that the problem was caused by this line of code:
glmer.glmulti <- function (formula, data, random, ...) {
glmer(paste(deparse(formula), random), data = data, family=binomial(link="logit"))
}
By changing this part of the code into the following the problem was solved:
glmer.glmulti<-function(formula,data,random,...) {
newf <- formula
newf[[3]] <- substitute(f+r,
list(f=newf[[3]],
r=reformulate(random)[[2]]))
glmer(newf,data=data,
family=binomial(link="logit"))
}
Related
When trying to graph the conditional fixed effects of a glmmTMB model with two random intercepts in GGally I get the error:
There was an error calling "tidy_fun()". Most likely, this is because the
function supplied in "tidy_fun=" was misspelled, does not exist, is not
compatible with your object, or was missing necessary arguments (e.g. "conf.level=" or "conf.int="). See error message below.
Error: Error in "stop_vctrs()":
! Can't recycle "..1" (size 3) to match "..2" (size 2).`
I have tinkered with figuring out the issue and it seems to be related to the two random intercepts included in the model. I have also tried extracting the coefficient and standard error information separately through broom.mixed::tidy and then feeding the data frame into GGally:ggcoef() with no avail. Any suggestions?
# Example with built-in randu data set
data(randu)
randu$A <- factor(rep(c(1,2), 200))
randu$B <- factor(rep(c(1,2,3,4), 100))
# Model
test <- glmmTMB(y ~ x + z + (0 +x|A) + (1|B), family="gaussian", data=randu)
# A few of my attempts at graphing--works fine when only one random effects term is in model
ggcoef_model(test)
ggcoef_model(test, tidy_fun = broom.mixed::tidy)
ggcoef_model(test, tidy_fun = broom.mixed::tidy, conf.int = T, intercept=F)
ggcoef_model(test, tidy_fun = broom.mixed::tidy(test, effects="fixed", component = "cond", conf.int = TRUE))
There are some (old!) bugs that have recently been fixed (here, here) that would make confidence interval reporting on RE parameters break for any model with multiple random terms (I think). I believe that if you are able to install updated versions of both glmmTMB and broom.mixed:
remotes::install_github("glmmTMB/glmmTMB/glmmTMB#ci_tweaks")
remotes::install_github("bbolker/broom.mixed")
then ggcoef_model(test) will work.
I am using the package lqmm, to run a linear quantile mixed model on an imputed object of class mira from the package mice. I tried to make a reproducible example:
library(lqmm)
library(mice)
summary(airquality)
imputed<-mice(airquality,m=5)
summary(imputed)
fit1<-lqmm(Ozone~Solar.R+Wind+Temp+Day,random=~1,
tau=0.5, group= Month, data=airquality,na.action=na.omit)
fit1
summary(fit1)
fit2<-with(imputed, lqmm(Ozone~Solar.R+Wind+Temp+Day,random=~1,
tau=0.5, group= Month, na.action=na.omit))
"Error in lqmm(Ozone ~ Solar.R + Wind + Temp + Day, random = ~1, tau = 0.5, :
`data' must be a data frame"
Yes, it is possible to get lqmm() to work in mice. Viewing the code for lqmm(), it turns out that it's a picky function. It requires that the data argument is supplied, and although it appears to check if the data exists in another environment, it doesn't seem to work in this context. Fortunately, all we have to do to get this to work is capture the data supplied from mice and give it to lqmm().
fit2 <- with(imputed,
lqmm(Ozone ~ Solar.R + Wind + Temp + Day,
data = data.frame(mget(ls())),
random = ~1, tau = 0.5, group = Month, na.action = na.omit))
The explanation is that ls() gets the names of the variables available, mget() gets those variables as a list, and data.frame() converts them into a data frame.
The next problem you're going to find is that mice::pool() requires there to be tidy() and glance() methods to properly pool the multiple imputations. It looks like neither broom nor broom.mixed have those defined for lqmm. I threw together a very quick and dirty implementation, which you could use if you can't find anything else.
To get pool(fit2) to run you'll need to create the function tidy.lqmm() as below. Then pool() will assume the sample size is infinite and perform the calculations accordingly. You can also create the glance.lqmm() function before running pool(fit2), which will tell pool() the residual degrees of freedom. Afterwards you can use summary(pooled) to find the p-values.
tidy.lqmm <- function(x, conf.int = FALSE, conf.level = 0.95, ...) {
broom:::as_tidy_tibble(data.frame(
estimate = coef(x),
std.error = sqrt(
diag(summary(x, covariance = TRUE,
R = 50)$Cov[names(coef(x)),
names(coef(x))]))))
}
glance.lqmm <- function(x, ...) {
broom:::as_glance_tibble(
logLik = as.numeric(stats::logLik(x)),
df.residual = summary(x, R = 2)$rdf,
nobs = stats::nobs(x),
na_types = "rii")
}
Note: lqmm uses bootstrapping to estimate the standard error. By default it uses R = 50 bootstrapping replicates, which I've copied in the tidy.lqmm() function. You can change that line to increase the number of replicates if you like.
WARNING: Use these functions and the results with caution. I know just enough to be dangerous. To me it looks like these functions work to give sensible results, but there are probably intricacies that I'm not aware of. If you can find a more authoritative source for similar functions that work, or someone who is familiar with lqmm or pooling mixed models, I'd trust them more than me.
The question is more or less as the title indicates. I would like to use the caret::train function with beta-binomial models made with glmmTMB package (although I am not opposed to other functions capable of fitting beta-binomial models) to calculate median absolute error (MdAE) estimates through jack-knife (leave-one-out) cross-validation. The glmmTMBControl function is already capable of estimating the optimal dispersion parameter but I was hoping to retain this information somehow as well... or having caret do the calculation possibly?
The dataset I am working with looks like this:
df <- data.frame(Effect = rep(seq(from = 0.05, to = 1, by = 0.05), each = 5), Time = rep(seq(1:20), each = 5))
Ideally I would be able to pass the glmmTMB function to trainControl like so:
BB.glmm1 <- train(Time ~ Effect,
data = df, method = "glmmTMB",
method = "", metric = "MAD")
The output would be as per the examples contained in train, although possibly with estimates for the dispersion parameter.
Although I am in no way opposed to work arounds - Thank you in advance!
I am unsure how to perform the required operation with caret without creating a custom method but I trust it is fairly easy to implement it with a for (lapply) loop.
In the example I will use the sleepstudy data set since your example data throws a bunch of warnings.
library(glmmTMB)
to perform LOOCV - for every row, create a model without that row and predict on that row:
data(sleepstudy,package="lme4")
LOOCV <- lapply(1:nrow(sleepstudy), function(x){
m1 <- glmmTMB(Reaction ~ Days + (Days|Subject),
data = sleepstudy[-x,])
return(predict(m1, sleepstudy[x,], type = "response"))
})
get the median of the residuals (I think this is MdAE? if not post a comment on how its calculated):
median(abs(unlist(LOOCV) - sleepstudy$Reaction))
AIM: The aim here was to find a suitable fit, using step functions, which uses age to describe wage, in the Wage dataset in the library ISLR.
PLAN:
To find a suitable fit, I'll try multiple fits, which will have different cut points. I'll use the glm() function (of the boot library) for the fitting purpose. In order to check which fit is the best, I'll use the cv.glm() function to perform cross-validation over the fitted model.
PROBLEM:
In order to do so, I did the following:
all.cvs = rep(NA, 10)
for (i in 2:10) {
lm.fit = glm(wage~cut(Wage$age,i), data=Wage)
all.cvs[i] = cv.glm(Wage, lm.fit, K=10)$delta[2]
}
But this gives an error:
Error in model.frame.default(formula = wage ~ cut(Wage$age, i), data =
list( : variable lengths differ (found for 'cut(Wage$age, i)')
Whereas, when I run the code given below, it runs.(It can be found here)
all.cvs = rep(NA, 10)
for (i in 2:10) {
Wage$age.cut = cut(Wage$age, i)
lm.fit = glm(wage~age.cut, data=Wage)
all.cvs[i] = cv.glm(Wage, lm.fit, K=10)$delta[2]
}
Hypotheses and Results:
Well, it might be possible that cut() and glm() might not work together. But this works:
glm(wage~cut(age,4),data=Wage)
Question:
So, basically we're using the cut() function, saving it's results in a variable, then using that variable in the glm() function. But we can't put the cut function inside the glm() function. And that too, only if the code is in a loop.
So, why is the first version of the code not working?
This is confusing. Any help appreciated.
I am trying to run ZIP and ZINB models to try look at some of the factors which might help explain disease (orf) distribution within 8 geographical regions. The models works fine for some regions and not others. However upon factoring and running the model in R I get error message.
How can I solve this problem or would there be a model that might work better with the subset as the analysis will only make sense when it’s all uniform across all regions.
zinb3 = zeroinfl(Cases2012 ~ Precip+ Altitude +factor(Breed)+ factor(Farming.Practise)+factor(Lambing.Management)+ factor(Thistles) ,data=orf3, dist="negbin",link="logit")
Error in solve.default(as.matrix(fit$hessian)) :
system is computationally singular: reciprocal condition number = 2.99934e-24
Results after fitting zerotrunc & glm as suggested by #Achim Zeileis. How do i interprete zerotruc output given that no p values. Also how can I correct the error with glm?
zerotrunc(Cases2012 ~ Flock2012+Stocking.Density2012+ Precip+ Altitude +factor(Breed)+ factor(Farming.Practise)+factor(Lambing.Management)+ factor(Thistles),data=orf1, subset = Cases2012> 0)
Call:
zerotrunc(formula = Cases2012 ~ Flock2012 + Stocking.Density2012 + Precip + Altitude +
factor(Breed) + factor(Farming.Practise) + factor(Lambing.Management) + factor(Thistles),
data = orf1, subset = Cases2012 > 0)
Coefficients (truncated poisson with log link):
(Intercept) Flock2012 Stocking.Density2012
14.1427130 -0.0001318 -0.0871504
Precip Altitude factor(Breed)2
-0.1467075 -0.0115919 -3.2138767
factor(Farming.Practise)2 factor(Lambing.Management)2 factor(Thistles)3
1.3699477 -2.9790725 2.0403543
factor(Thistles)4
0.8685876
glm(factor(Cases2012 ~ 0) ~ Precip+ Altitude +factor(Breed)+ factor(Farming.Practise)+factor(Lambing.Management)+ factor(Thistles) +Flock2012+Stocking.Density2012 ,data=orf1, family = binomial)
Error in unique.default(x, nmax = nmax) :
unique() applies only to vectors
It's hard to say exactly what is going on based on the information provided. However, I would suspect that the data in some regions does not allow to fit the model specified. For example, there might be some regions where certain factor levels (of Breed or Farming.Practise or Lambin.Management or Thristles) only have zero values (or only non-zero but that is less frequent in practice). Then the coefficient estimates often degenerate so that the associated zero-inflation probability goes to 1 and the count coefficient cannot be estimated.
It's typically easier to separate these effects by using the hurdle rather the zero-inflation model. Then the two parts of the model can also be fitted separately by glm(factor(y > 0) ~ ..., ..., family = binomial) and zerotrunc(y ~ ..., ..., subset = y > 0). The latter function is essentially the same code as pscl uses but has been factored into a standalone function in the package countreg on R-Forge (not yet on CRAN).