I am utilizing the predictInterval() function from the merTools package. My model is fit utilizing a Poisson family specification like the below:
glmer(y ~ (1|key) + x, data = dat, family = poisson())
When I use predictInterval() to calculate the prediction interval associated with my model, I get the following warning message:
Warning message:
Prediction for NLMMs or GLMMs that are not mixed binomial regressions is not tested. Sigma set at 1.
I am taking this to mean that predictInterval() doesn't have an implementation for models fit with a Poisson distribution. I therefore do not trust the resulting interval.
Is my interpretation correct? I have searched around for similar issues but haven't found anything.
Any help would be greatly appreciated.
Related
I am building a model using the mgcv package in r. The data has serial measures (data collected during scans 15 minutes apart in time, but discontinuously, e.g. there might be 5 consecutive scans on one day, and then none until the next day, etc.). The model has a binomial response, a random effect of day, a fixed effect, and three smooth effects. My understanding is that REML is the best fitting method for binomial models, but that this method cannot be specified using the gamm function for a binomial model. Thus, I am using the gam function, to allow for the use of REML fitting. When I fit the model, I am left with residual autocorrelation at a lag of 2 (i.e. at 30 minutes), assessed using ACF and PACF plots.
So, we wanted to include an autocorrelation structure in the model, but my understanding is that only the gamm function and not the gam function allows for the inclusion of such structures. I am wondering if there is anything I am missing and/or if there is a way to deal with autocorrelation with a binomial response variable in a GAMM built in mgcv.
My current model structure looks like:
gam(Response ~
s(Day, bs = "re") +
s(SmoothVar1, bs = "cs") +
s(SmoothVar2, bs = "cs") +
s(SmoothVar3, bs = "cs") +
as.factor(FixedVar),
family=binomial(link="logit"), method = "REML",
data = dat)
I tried thinning my data (using only every 3rd data point from consecutive scans), but found this overly restrictive to allow effects to be detected due to my relatively small sample size (only 42 data points left after thinning).
I also tried using the prior value of the binomial response variable as a factor in the model to account for the autocorrelation. This did appear to resolve the residual autocorrelation (based on the updated ACF/PACF plots), but it doesn't feel like the most elegant way to do so and I worry this added variable might be adjusting for more than just the autocorrelation (though it was not collinear with the other explanatory variables; VIF < 2).
I would use bam() for this. You don't need to have big data to fit a with bam(), you just loose some of the guarantees about convergence that you get with gam(). bam() will fit a GEE-like model with an AR(1) working correlation matrix, but you need to specify the AR parameter via rho. This only works for non-Gaussian families if you also set discrete = TRUE when fitting the model.
You could use gamm() with family = binomial() but this uses PQL to estimate the GLMM version of the GAMM and if your binomial counts are low this method isn't very good.
I would like to estimate a random effects (RE) multinomial logit model.
I have been applying mblogit from the mclogit package. However, once I introduce RE into my model, it fails to converge.
Is there a workaround this?
For instance, I tried to adjust the fitting process of mblogit and increase the maximal number of iterations (maxit), but did not succeed to correctly write the syntax for the control function. Would this be the right approach? And if so, could you advise me how to implement it into my model which so far looks as follows:
meta.mblogit <- mblogit(Migration ~ ClimateHazard4 , weights = logNsquare,
data = meta.df, subset= Panel==1, random = ~1|StudyID,
)
Here, both variables (Migration and ClimateHazard4) are factor variables.
Or is there an alternative approach you could recommend me for an estimation of RE multinomial logit?
Thank you very much!
I am trying to run a Generalized linear mixed model (GLMM) on r, I have two fixed factors and two random factors
however there are a lot of holes in my data set and the I am struggling to find a code to run the glmm all I found is the glm
Can someone please walk me through this, I know very little about R and coding
You can use the lme4 package as well. The command for a generalized linear mixed model is glmer().
Example:
install.packages("lme4") #If you still haven't done it.
library(lme4)
myfirstmodel <- glmer(variable_to_study ~ fixed_variable + (1|random_effect_varible), data = mydataset, family = poisson)
Family = poisson was just an example. Choose the 'family' according to the nature of the variable_to_study (eg. poisson for discrete data).
I have longitudinal data from two surveys and I want to do a pre-post analysis. Normally, I would use survey::svyglm() or svyVGAM::svy_vglm (for multinomial family) to include sampling weights, but these functions don't account for the random effects. On the other hand, lme4::lmer accounts for the repeated measures, but not the sampling weights.
For continuous outcomes, I understand that I can do
w_data_wide <- svydesign(ids = ~1, data = data_wide, weights = data_wide$weight)
svyglm((post-pre) ~ group, w_data_wide)
and get the same estimates that I would get if I could use lmer(outcome ~ group*time + (1|id), data_long) with weights [please correct me if I'm wrong].
However, for categorical variables, I don't know how to do the analyses. WeMix::mix() has a parameter weights, but I'm not sure if it treats them as sampling weights. Still, this function can't support multinomial family.
So, to resume: can you enlighten me on how to do a pre-post test analysis of categorical outcomes with 2 or more levels? Any tips about packages/functions in R and how to use/write them would be appreciated.
I give below some data sets with binomial and multinomial outcomes:
library(data.table)
set.seed(1)
data_long <- data.table(
id=rep(1:5,2),
time=c(rep("Pre",5),rep("Post",5)),
outcome1=sample(c("Yes","No"),10,replace=T),
outcome2=sample(c("Low","Medium","High"),10,replace=T),
outcome3=rnorm(10),
group=rep(sample(c("Man","Woman"),5,replace=T),2),
weight=rep(c(1,0.5,1.5,0.75,1.25),2)
)
data_wide <- dcast(data_long, id~time, value.var = c('outcome1','outcome2','outcome3','group','weight'))[, `:=` (weight_Post = NULL, group_Post = NULL)]
EDIT
As I said below in the comments, I've been using lmer and glmer with variables used to calculate the weights as predictors. It happens that glmer returns a lot of problems (convergence, high eigenvalues...), so I give another look at #ThomasLumley answer in this post and others (https://stat.ethz.ch/pipermail/r-help/2012-June/315529.html | https://stats.stackexchange.com/questions/89204/fitting-multilevel-models-to-complex-survey-data-in-r).
So, my question is now if a can use participants id as clusters in svydesign
library(survey)
w_data_long_cluster <- svydesign(ids = ~id, data = data_long, weights = data_long$weight)
summary(svyglm(factor(outcome1) ~ group*time, w_data_long_cluster, family="quasibinomial"))
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.875e+01 1.000e+00 18.746 0.0339 *
groupWoman -1.903e+01 1.536e+00 -12.394 0.0513 .
timePre 5.443e-09 5.443e-09 1.000 0.5000
groupWoman:timePre 2.877e-01 1.143e+00 0.252 0.8431
and still interpret groupWoman:timePre as differences in the average rate of change/improvement in the outcome over time between sex groups, as if I was using mixed models with participants as random effects.
Thank you once again!
A linear model with svyglm does not give the same parameter estimates as lme4::lmer. It does estimate the same parameters as lme4::lmer if the model is correctly specified, though.
Generalised linear models with svyglm or svy_vglm don't estimate the same parameters as lme4::glmer, as you note. However, they do estimate perfectly good regression parameters and if you aren't specifically interested in the variance components or in estimating the realised random effects (BLUPs) I would recommend just using svy_glm.
Another option if you have non-survey software for random effects versions of the models is to use that. If you scale the weights to sum to the sample size and if all the clustering in the design is modelled by random effects in the model, you will get at least a reasonable approximation to valid inference. That's what I've seen recommended for Bayesian survey modelling, for example.
I fit a given data using Cox model via glmnet R package and my
little R example is:
library(fastcox);data(FHT);attach(FHT) #
library(glmnet)
library(survival)
fit = glmnet(x,Surv(y,status),family="cox",alpha=1)
From the help document, we know glmnet fits penalized models like
-loglik/nobs + λ*penalty
i.e., objective function = loss function + penalty function.
I want to fetch -loglik/nobs (loss function value,
the negative partial log-likelihood of the fitted model
or two term
Taylor series expansions of the log likelihoods) from the fit object.
Any idea? Tks
BTW, we also tried
fit0 = glmnet(x,Surv(y,status),family="cox",alpha=1,lambda=0)
according to -loglik/nobs + λ*penalty, but it shows errors.