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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What is the global model?
I'm trying to calculate c-hat, the overdispersion parameter for a QAIC model set. According to Burnham and Anderson, you're supposed to calculate c-hat on the global model. Is the global model the ...
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Is it reasonable to calculate AIC of a subset of the data set which was used to fit the model?
There is a factor variable called "Treatment" in my data set. This factor consists of two levels, "C" and "H". I want to test whether there is there any significant difference between two levels. I ...
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AIC with Mantel's tests
Mantel's tests are commonly used to compare genetic distances (say, between a number of individuals) with true or hypothesized landscape distances between those same individuals. For example, “does ...
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Are very large log likelihood and delta AIC values problematic for model selection?
I am using AICc for small sample sizes to compare 8 a priori models (including null model). I fitted my models using a GLMM due to the nested nature of my data and defined the family as 'poisson' ...
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Compare fits of model to transformed and untransformed response
I want to compare data that proportions among three different groups e.g.:
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AIC/BIC: how many parameters does a permutation count for?
Let's say I have a model selection problem and I am trying to use AIC or BIC to evaluate the models. This is straightforward for models that have some number $k$ of real-valued parameters.
However, ...
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LASSO vs AIC for feature selection with the Cox model
I have some questions about the Lasso.
After using the AIC or BIC to select a model, the model is fit with the variables selected in order to get the standard errors of the estimates with CIs, ...
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Relation between reduced chi square and Akaike criterion
In choosing the best fit between different curves, supposing the errors are distributed normally, I can do the following things:
Calculate the reduced $\chi_r^2=\frac{\chi^2}{N_b-N_p}$, where $N_b$ ...
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Random coefficient models with fractional polynomial transformation selection method, which criterion to use?
I am trying to conduct a meta analysis for dose response studies where I am using fractional polynomial transformation from predefined family of powers. Now data fitting using all possible ...
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Is there a way to correct standard errors and/or prediction intervals for multiple comparison after doing backwards selection?
It is well known that most model selection algorithms can easily fall into a multiple comparison trap. To quote Friedman:
Consider developing a regression model in a context where substantive
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How do I know if the differences in ICs among candidate models are significant?
I'm doing some exploratory modelling on a data set with 29 covariates and an additional 11 variables that are of interest to my research question.
My strategy is to develop a model with a subset of ...
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Is the AIC/BIC different for different Granger causality directions?
If I have two possible Granger causality setups:
$A_t = intercept + \sum_{i=1}^p a_i L^i (A_t) + \sum_{i=1}^p b_i L^i (B_t) + e_t$
$B_t = intercept+\sum_{i=1}^p a_i L^i (A_t) + \sum_{i=1}^p b_i L^i ...
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Using AIC to distinguish between models using multiple datasets
I want to use AIC to compare three candidate models (labeled by m), each having K_m parameters. However, I have M datasets over which I can make the comparison. My ultimate goal is to report the ...
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AIC vs BIC vs MDL
I am trying to learn the difference between the three approaches and their applications.
a) As I understand,
AIC = -LL+K
BIC = -LL+(K*logN)/2
Unless I am ...
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lm to lmer function tweaking
I have stolen and modified a snippet of code found off the internet from (http://www.r-bloggers.com/aic-bic-vs-crossvalidation/) which graphically depicts AIC and BIC values for different polynomial ...
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How to calculate the weighted sum of absolute deviations to determine AIC for quantile regression
I would like to know if there is a way to calculate the sum of the weighted absolute deviations for quantile regressions with package quantreg?
I'm following the ...
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incorporating averaging models from AIC and still using k-fold cross validation?
Ive a county/district that Ive divided into ~300 grids that are 15km^2 in size attributed with various habitat and economic variables that have been summarized and standardized. I then have 2 types ...
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How do I explain that software implemented model selection procedures should not be used unsupervised?
I know that people generally say that procedures which select a model based on information criterion lead to inconsistent model selections.
I read a paper by Leeb and Potscher (2005), MODEL SELECTION ...
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Can dredge() in R package MuMIn deal with global model objects generated by gls() in nlme?
I am trying to use the function dredge() in the package MuMIn to compare AIC model-selection statistics for models of all ...
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AIC with multiple linear slopes
I have a set of data points. I would like to know which model of linear slopes best fits the data: two slopes, three slopes, or four slopes. How can I calculate the AIC for each of these models (i.e, ...
