I am estimating a mixed model with glmer and experience the error
Error in zeta(shiftpar, start = opt[seqpar1][-w]) : profiling detected new, lower deviance
I found a solution by "boosting" the devtol parameter. However, I don't know how and I can't find the solution.
Here is my model:
m3.glmer = glmer(binExap ~ (1|id) + Lag1 + Lag2 + Lag5 + BroadQ,
data = CLnMD,
family = binomial(link="logit"),
nAGQ=1, control = glmerControl(optimizer = 'bobyqa',
optCtrl=list(maxfun=100000)))
This is the code I am using for estimating the CIs:
KIsBoot <- confint.merMod(m3.glmer, method = "profile", nsim = 250)
Now where do I boost/how would I boost "devtol"?
This is admittedly a bit obscure. confint.merMod() takes a ... argument that gets passed to profile.merMod. ?profile.merMod says:
devtol: tolerance for fitted deviances less than baseline (supposedly
minimum) deviance.
So, if you want to ignore this check completely,
confint(m3.glmer, devtol = Inf)
should work. (You don't need .merMod, R figures that out automatically; "profile" is the default setting; and nsim is ignored unless method = "boot" [we should add a warning!])
However, I would also say a little bit pessimistically that if you're getting this error your profile CIs might not be very reliable ... try visualizing the profile as well (pp <- profile(m3.glmer, devtol = Inf); lattice::xyplot(pp)) to make sure it looks reasonable (i.e. at least monotonic!)
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.
I am trying to find a best model fitting on my data using library(nlme) and lme function in R. Here is my model when the slope is fixed:
FixedRopeLength <- lme(EnergyCost~ RopeLength,
data = data,
random=~1|Subject, method = "ML")
summary(FixedRopeLength)
To see whether a random slope provides a better model than a fixed slope, I let the slope to vary across Subject as follows:
RandomRopeLength <- lme(EnergyCost~RopeLength,
data = data,
random=~RopeLength|Subject, method = "ML")
summary(RandomRopeLength)
However, I got this error:
Error in lme.formula(EnergyCost ~ RopeLength, data = data, random =
~RopeLength | : nlminb problem, convergence error code = 1
message = iteration limit reached without convergence (10)
Any solution??
Thank you so much for your help. Your code worked. I only needed to justify your code based on lme function. Here is the code which can be used for aforementioned error:
RandomRopeLength<-lme(EnergyCost~RopeLength, data = data, random=~RopeLength|Subject, method = "ML", control =list(msMaxIter = 1000, msMaxEval = 1000))
summary(RandomRopeLength)
Thanks!
?lme shows that there is a control argument, which redirects you to ?lmerControl, which gives you
msMaxIter: maximum number of iterations for the optimization step
inside the ‘lme’ optimization. Default is ‘50’.
and
msMaxEval: maximum number of evaluations of the objective function
permitted for nlminb. Default is ‘200’.
These correspond to eval.max and iter.max from ?nlminb. Since I'm not sure which of these is the problem, I would re-run the model with
control = lmeControl(msMaxIter = 1000, msMaxEval = 1000)
However, I'll warn you that once you have a problem that experiences numerical problems with the default parameter settings, adjusting the parameter settings may just lead to other problems farther down the line ...
I am trying to calculate the temporal autocorrelation of a poison distributed mixed model, and was wondering how to do so. I get an error that says "$ operator not defined for this S4 class" I can successfully run the the dwtest on a linear model, with a poisson distribution, but not the one I really want.
Successful model and code:
temp.nem.cuc.glm<-glm(AllDat$nem.cuc~ AllDat$year.collected, family=poisson(link="log"))
summary(temp.nem.cuc.lm)
time<-AllDat$year.collected
dwnem.cuc<-dwtest(temp.nem.cuc.lm, order.by = time, alternative = "two.sided", iterations = 50, exact = FALSE, tol = 1e-10)
dwnem.cuc
Unsuccessful model and code
#the model I am really interested in
nem.cuc.pois=glmer(nem.cuc~ I(year.collected-1930)+I(standard.length..mm./100) + (1|sites1), family = "poisson", data=AllDat)
time<-AllDat$year.collected
dwnemresid.cuc<-dwtest(nem.cuc.pois, order.by = time, alternative = "two.sided", iterations = 50, exact = FALSE, tol = 1e-10)
dwnem.cuc
For future reference, I found the "check_autocorrelation" function from the performance package, but it returns only whether it detected autocorrelation or not.
I would also be interested in finding a DW test that returns a statistic, if anyone knows one - or in a way to solve the S4 class error, since I encounter it repeatedly.
I get the following warning from glmer:
m <- glmer(cbind(Y, N) ~ c1 + c2 + c3 + (1|g1:Year) + (1 + c1 + c2|g1) + (1|g1:Site),
family = binomial, data = data,
control = glmerControl(optimizer ='optimx', optCtrl=list(method='nlminb')))
# Warning in optimx.check(par, optcfg$ufn, optcfg$ugr, optcfg$uhess, lower, :
# Parameters or bounds appear to have different scalings.
# This can cause poor performance in optimization.
# It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
This is interesting, since all my covariates are scaled (c1: mean = 5.410769e-16, sd = 1), (c2: mean = -2.411114e-16, sd = 1), (c3: mean = 7.602661e-18, sd = 1).
What does this is warning actually mean? All my covariates are scaled, see above. Scaling them again won't fix it.
Shall I have to be concerned about this warning in the sense my model could return unreliable estimates? I got no other warnings or errors.
Thank you!
PS: note - the warning seems to be kinda non-deterministic, on certain data sets in different runs I've observed it sometimes is present and sometimes isn't.
I am rather late with this but since you haven't got other answers maybe someone can still use it. The source of this message is the optimx package. You use optimx as the non-linear optimiser.
The message is the result of a scale check (see the scalecheck() function in the manual). It raises suspicion when the parameter space could be set too narrow. However, this function can also throw misleading warnings. John Nash himself wrote in the manual: "It is, however, an imperfect and heuristic tool, and could be improved." If you get good results, you are probably okay.
Hope this help,
Jan