I am working on a dataset that has random effects (so I need a mixed-effects model). The response variable is a count (non-negative, integer) which is also zero-inflated (51% zeros). The model that I have arrived at is a zero-inflated generalized linear mixed-effects model (ZIGLMM). Several packages that I have attempted to use to fit such a model include glmmTMB and glmmADMB in R.
My question is: is it possible to account for spatial autocorrelation using such a model and if so, how can it be done? I am unsure if this has been done before since both packages are relatively new..
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I am currently working on univariate GARCH models with different specifications and got stuck on including the exponential term in the variance equation:
mean model (setting ω4 = 0)
variance model
I am using the rugarch package in R and (unsuccessfully) tried the 'eGARCH' model type and external regressor option for the recession dummy INBER to get the estimates. Is this generally the correct way for including the exponential part or am I completely off?
I have a dataset with data left censored and I wanted to apply a multilevel mixed-effects tobit regression, but I only find information about how to do it in Stata. Is it possible to do it in R?
I found the packages 'VGAM' and 'CensREG', but I don't get how to add fixed and random effects.
Also my data is log-normal distributed, is there a way to add this to the model?
Thanks!
According to Section 3.5 of a vignette, the censReg package can handle a mixed model if the data are prepared properly via the plm package.
This Cross Validated page shows an example.
I don't have experience with this; it might only work with formal panel data rather than more general random-effects structures.
If your data are truly log-normal, you could take logs first and set the lower censoring limit on the log scale. Note that an apparent log-normal distribution of outcomes might just represent a corresponding distribution of predictor values with an underlying normal error distribution around the predictions. Don't jump blindly into a log-normal assumption.
I`m considering several models such as GLM, GLMM, zero-inflated, and zero-inflated mixed in the count data.
All my work was done in R.
Prior studies confirmed that there is a problem of zero excess and over-dispersion as a consideration in counter data analysis.
So I tried the following tests.
1. zero excess
Voung test was performed using the zero-inflated model and the GLM.
Vuong of the pscl package was used.
ZIP vs. GLM Poisson
ZINB vs. GLM NB
Significant results were obtained from the above two tests (p<0.05).
2. over-dispersion
dispersion test was performed to find out why over-dispersion should be
considered in real data using the Poisson model.
dispersiontest of the AER package was used (Cameron, A.C. and Trivedi 1990).
The above test results in rejection of the null hypothesis (p<0.05)
In addition, it was confirmed that dispersion parameter(1/theta) had a value of about 0.39.
However, I have not yet found a verification method for the reason why random effects should be considered.
My data is traffic accident data according to the year of each road. i.e. it is longitudinal count data.
I was told by a professor of statistics that a mixed model should be used considering road heterogeneity.
Therefore, I constructed GLMM poisson/NB and zero-inflated mixed poisson/NB using random effects by road and confirmed the results.
GLMM used glmer of lme4, and glmmTMB of glmmTMB was used as the zero-inflated mixed model.
I did the Houseman test at first. However, this test compares the fixed-effects model with the random-effects model and was considered inappropriate for the count data (not linear model).
Crucially, when testing the random effect of the mixed model from the count data, no previous study was seen that conducted the Hausmann test.
Therefore, my question is as follows:
1. I would like to know if there is a previous study that identifies the reason for considering ramdom effect in modeling in longitudinal study data.
2. Is there a validation method to verify the significant effects of random effects in the mixed model?
The AIC and BIC comparison has already been carried out.
3. If there is a way, what package does R use? Additionally, how to use it
I believe that a Bayesian classifier is based on statistical model. But after training a Bayesian model, I can save it and do not need the training dataset to predict the test data. For example, if I build a bayesian model by
y - labels,X-samples
Can I take the model as a equation like this?
If so, how can I extract the weights and bias? and what is the new formula looks like?If not, what is the new equation like?
Yes, from the docs, a trained classifier has two attributes, intercept_ and coef_ which are useful if you want to interpret the NBC as a linear model.
I'm trying to learn about MARS/Earth models for classification and am using "classif.earth" in the MLR package in R. My issue is that the MLR documentation says that "classif.earth" performs flexible discriminant analysis using the earth algorithm.
However, when I look at the code:
(https://github.com/mlr-org/mlr/blob/master/R/RLearner_classif_earth.R)
I don't see a call to fda in the mda package, rather it directs earth to fit a glm with a default logit link.
So tell me if I'm wrong, but it seems to me that "classif.earth" is not doing flexible discriminant analysis but rather fitting a logistic regression on the earth model.
The implementation uses MARS to perform the FDA, where the MARS model determines the different groups. You can find more information in this paper; I quote from the abstract:
Linear discriminant analysis is equivalent to multiresponse linear regression [...] to represent the groups.