Questions tagged [aic]
AIC stands for the Akaike Information Criterion, which is one technique used to select the best model from a class of models using a penalized likelihood. A smaller AIC implies a better model.
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Validity of AIC When Comparing Models with Varying Dispersion Parameters
I'm currently making a binomial model with a logit link, which is parameterised as a quasibinomial since I'm allowing it to calculate the dispersion parameter. I was wondering, since changes to the ...
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Comparing Models with Unequal Sample Sizes
I have performed an association analysis where I have associatiated several different perdictor variables to a dependent variable. For each predictor, I run two models and compare them via the ...
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comparing non-nested models with different specifications based on AIc/BIC criteria
I am trying to determine if I can use the AIC/BIC criteria for model selection in the case of a multivariate probit model. I have two models with different specifications:
e.g. Model-1: mvprobit ( Y1 =...
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calculating QAIC for GLM
I am trying to calculate QAIC of DLNM models with code offered in here.
but I am confused about the difference between this and the formula of QAIC from other R package as in the answer to this ...
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Comparing Firth's logistic and traditional logistic by AIC
My data has rare events so I decided to develop a Firth's penalized logistic regression using logistf package. I also want to apply a traditional ML logistic ...
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Calculate weight for GLM-quasi poisson model
I am running several models with the quasi-Poisson family. I am looking at data from vulture restaurants. Vulture count was modelled at each site as a function of either a linear or quadratic effect ...
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Any Insights on the adoption and use of the Healthy Akaike Information Criterion (hAIC)?
Recently, I came across the Healthy Akaike Information Criterion (hAIC), introduced by Demidenko in his 2004 book "Mixed Models: Theory and Applications with R." Despite its (potential) ...
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Normality of residuals versus AIC and "best" fit
Hoping to get some insight into normality of residuals vs the "best" fit of the model.
After running a simple linear regression and checking normality of the residuals, I logged my outcome ...
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GAMLSS - How to adjust AIC for log-transformation of variables [duplicate]
I have one GAMLSS model in which the independent and dependent variable are on the natural scale, and a second model in which both variables have been log-transformed. How do I adjust the AIC of the ...
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Is the behavior of log-likelihood and number of parameters correct in probabilistic PCA?
I am studying the behavior of Probabilistic PCA as described by Tipping and Bishop (1999). I am using the R package "Rdimtools" to help. I am puzzled about the number of parameters in the ...
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Different P-Value and AIC before/after standardization [Python - Statsmodels]
I am investigating the correlation between environmental variables (15 continuous variables grouped as 'DHIs' in the code below) and fox occurrence (binary), using logistic regression / Python ...
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How does lmList calculate AIC?
I'm using the lmList function in the lme4 R package to fit the following model:
mod.list <- lmList(record ~ log(LL)*site | species, data = dat, family=binomial)
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Is comparing the AIC of a Bayesian and a frequentist model right?
I’m trying to fit a general linear model where the dependant variable is a probability. It is zero-inflated and continuous, then following the advice here blog of Ben Bolker, I separated my data pool ...
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Additional covariate reduces AIC in mixed models (LMM, GLMM, GAM)
In repeated measure for timepoints in different Group, Age and Gender act as a covariate in ...
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Model Fit Measures in a Binomial Logistic Regression
I am very new to regression statistics and have produced four models in the statistical package Jamovi using binomial logistic regression. Looking at model fit measures I am confused as the results ...
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