I have tried to produce diagnostics plots for glmmTMB models using package DHARMa without success. Example 1.1 in this vignette gives:
owls_nb1 <- glmmTMB(SiblingNegotiation ~ FoodTreatment*SexParent +
(1|Nest)+offset(log(BroodSize)),
contrasts=list(FoodTreatment="contr.sum",
SexParent="contr.sum"),
family = nbinom1,
zi = ~1,
data=Owls)
plot(owls_nb1_simres <- simulateResiduals(owls_nb1) )
# Error in on.exit(add = TRUE, { : invalid 'add' argument
The same happens with:
if (!require(RCurl)) install.packages('RCurl'); library(RCurl)
unicorns <- read.csv(text= RCurl::getURL("https://raw.githubusercontent.com/marcoplebani85/datasets/master/unicorns.csv"))
# simulated data, obviously
unicorns_glmmTMB <- glmmTMB(Herd_size_n ~ food.quantity
+ (1 + food.quantity | Locality)
+ (1 + food.quantity | Year_Month),
family="poisson",
data=unicorns)
plot(simulateResiduals(unicorns_glmmTMB))
# Error in on.exit(add = TRUE, { : invalid 'add' argument
If I run the same model in lme4::glmer:
unicorns_glmer <- glmer(Herd_size_n ~ food.quantity
+ (1 + food.quantity | Locality)
+ (1 + food.quantity | Year_Month),
family="poisson",
data=unicorns)
...and "feed" it to:
plot(simulateResiduals(unicorns_glmer))
I obtain diagnostics plots without issues (by the way I am aware that model unicorns_glmer is suboptimal and can be improved).
I'm using:
glmmTMB version 1.0.2.9000 freshly installed from github;
DHARMa version 0.4.1;
R version 3.6.3;
MacOS Sierra version 10.12.6.
Has anyone encountered the same problem? Does anyone know how to solve it?
EDIT: my question was originally on how packages performance and DHARMa handle glmmTMB objects. For the sake of focus and clarity I removed the references to package performance, thus making this question specific to glmmTMB and DHARMa.
It looks like this is a bug that was present in R <= 4.0.1. From the R NEWS file for version 4.0.2:
on.exit() now correctly matches named arguments, thanks to PR#17815 (including patch) by Brodie Gaslam.
I have attempted to fix the glmmTMB code so it works around the bug.
You could try
remotes::install_github("glmmTMB/glmmTMB/glmmTMB#on_exit_order")
and see if that helps (provided nothing goes wrong, this branch should be merged into master shortly ...)
Related
I have the following packages loaded and I have tried reinstalling them in case it had not downloaded properly. That was not the issue. I'm working on the latest version of R and R studio.
library(Matrix)
library(lme4)
library(lmerTest)
library(emmeans)
library(stats)
library(fitdistrplus)
library(tidyverse)
library(buildmer)
library(performance)
library(see)
library(sjPlot)
After importing and transforming my data a little bit e.g. recoding 1's and 2's to more meaningful labels. I have run the following code which has always worked for me in the past.
modelRT4 <- lmer(RT4 ~ condition_number +
(1 + condition_number | participant) +
(1 + condition_number | item_number),
data = alldata_Pred_RT, REML = TRUE)
However, now I am getting the error code...
Error in diag(Lambdat) : object 'R_sparse_diag_get' not found
For context RT4 is a reaction time measure in seconds. Whilst the others are self-explanatory.
I'm having no problems getting descriptives or visualising the data using violin plots and box plots.
Any ideas why this might be and what can be done to rectify this? I can show more code if needed.
The full code can be found on my GitHub page: https://github.com/E-LeLuan/ASC_small/blob/master/Tidy_RT_data/Prediction/Prediction_tidy_script.R
It seems a bit silly to post this as a question, but I haven't been able to find an answer for this.
I want to use the glmmTMB package for modelling data with zero-inflation, but when I try to fit the model I want, the function glmmTMB() returns the following error:
Error in glmmTMB(y ~ x + (1 | z), data = df, ziformula = x) : object 'x' not found
This only happens after I introduce the term for ziformula. The model without that term works just fine. For some reason, it seems that the ziformula overrides the data command in the function.
My glmmTMB package version is 1.1.3 and my R version is 4.1.1.
Here's a little sample data to reproduce the problem:
df <- data.frame(y=c(2,2,5,7,3,8,3,2,4,6),
x = c(23,21.7,22.5,18,20,19.9,21,20.3,20,20.5),
z = c(rep("a",5),rep("b",5)))
glmmTMB(y ~ x + (1|z), data = df, ziformula = x)
If anyone has had any similar issue, I would be grateful for any tips on how to solve it!
I have discovered some heteroscedasticity in my model that I would like to compensate for with more robust standard errors. I have tried to use the Huber-White robust standard errors from the merDeriv package in R but I beleive these only work for a GLMM with a binomial distribution. Is there a way I could achieve the same thing for a Negative Binomial distribition?
Model:
library(lme4)
model <- glmer.nb(Jobs ~ 1 + Month + Year + (1|Region), data = df)
Huber-White robust standard errors:
library(merDeriv)
bread.glmerMod(model)
Error:
Error in vcov.lmerMod(object, full = full) : estfun.lmerMod() only works for lmer() models.
Thank you for any help!
This looks like a bug in the package, as far as I can tell (the bread.glmerMod function was calling estfun.lmerMod rather than estfun.glmerMod; there's a broader question here about the design of the generic functions, but never mind ...)
You should be able to install a fixed version from my fork via remotes::install_github("bbolker/merDeriv"), then reload the package and try again.
Alternately, download the tarball, change vcov.lmerMod to vcov.glmerMod in the last line of R/bread.glmerMod.R, and re-install the package ...
Try something like this:
library(lme4)
model <- glmer.nb(Jobs ~ 1 + Month + Year + (1|Region), data = df)
cov <- vcovHC(model, type = "HC1", sandwich = T)
se <- sqrt(diag(cov_m1))
(Can't confirm if it works since this there isn't a reproducible example)
I'm hitting a confusing error while trying to run the lme4::allFit() using some built-in parallelization. I fit an initial model m0, which uses a larger dataframe ckDF (n = 265,623 rows) to model a binary response to a number of categorical and continuous predictors in a logistic framework with a random intercept for year.
I'm interested in determining whether different optimizers yield different results, following some recommendations I've found online (e.g. by #BenBolker here). My data is fairly large and takes ~20 minutes to run usually, so I'm hoping to use the parallel and ncpus parameters of allFit() to speed it up a bit. Here's my relevant code:
require(lme4)
require(parallel)
m0 <- glmer(returned ~ 1 + barge + site + barge:site +
(run + rearType + basin)^2 +
(tdg + temp + holdingTime)^2 +
(1|year),
data = ckDF, family = 'binomial',
control = glmerControl(optimizer='bobyqa',
optCtrl = list(maxfun = 1e5)))
af1 <- allFit(m0, parallel = 'multicore', ncpus = detectCores())
Upon doing this, I encounter the following error:
Error in checkForRemoteErrors(val) :
7 nodes produced errors; first error: no applicable method for 'isGLMM' applied to an object of class "list"
Any ideas? It seems to me that when it constructs a bunch of nodes, somehow some of them don't import the lme4 package and thus do not recognize isGLMM(); but I don't know why allFit() would do this, since it's from lme4(). I tried looking under the hood and altering the function for my own allFit() package, but ran into other errors.
Any help would be appreciated. R Version: 3.6.1; lme4 Version: 1.1-21; platform: Windows 10 64-bit
Thanks to #user20650 & #Ben Bolker for the tips in comments above -- it worked and I was able to get allFit() to run as expected, by ensuring I use parallel = "snow" in my function call since I'm running in Windows. Just posting the edited code here for anyone else who finds this useful:
require(lme4); require(snow)
# Define initial model (switched to defaults here)
m0 <- glmer(returned ~ 1 + barge + site + barge:site +
(run + rearType + basin)^2 +
(tdg + temp + holdingTime)^2 +
(1|year),
data = ckDF, family = 'binomial')
# Set up cluster for running allFit()
optCls <- makeCluster(detectCores()-1, type = "SOCK")
clusterEvalQ(optCls,library("lme4"))
clusterExport(optCls, "ckDF")
# Use allFit() to look at differences in optimizers
system.time(af1 <- allFit(m0, parallel = 'snow',
ncpus = detectCores()-1, cl=optCls))
stopCluster(optCls)
Ended up taking ~40 minutes using 11 cores on my machine.
I'm constructing a piecewise structural equation model using the piecewiseSEM package in R (Lefcheck - https://cran.r-project.org/web/packages/piecewiseSEM/vignettes/piecewiseSEM.html)
I already created the model set and I could evaluate the model fit, so the model itself works. Also, the data fits the model (p = 0.528).
But I do not succeed in extracting the path coefficients.
This is the error i get: Error in cbind(Xlarge, Xsmall) : number of rows of matrices must match (see arg 2)
I already tried (but this did not work):
standardising my data because of the warning: Some predictor variables are on very different scales: consider rescaling
adapted my data (threw some NA values away)
This is my modellist:
predatielijst = list(
lmer(plantgrootte ~ gapfraction + olsen_P + (1|plot_ID), data = d),
glmer(piek1 ~ gapfraction + olsen_P + plantgrootte + (1|plot_ID),
family = poisson, data = d),
glmer(predatie ~ piek1 + (1|plot_ID), family = binomial, data = d)
)
with "predatie" being a binary variable (yes or no) and all the rest continuous variables (gapfraction, plantgrootte, olsen_P & piek1)
Thanks in advance!
Try installing the development version:
library(devtools)
install_github("jslefche/piecewiseSEM#2.0")
Replace list with psem and run the coefs or summary function. It will likely get rid of your error. If not, open a bug on Github!
WARNING: this will overwrite your current version from CRAN. You will need to reinstall from CRAN to get version 1.4 back.
try to use lme (out of the nlme library) ilstead of glmer. As far as I understand, the fact that lmer does not provide p-values (while lme does) seems to be the problem here.
Hope this works.