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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Acceptable values for variance, aic and bic in multilevel models

I'm building a multilevel model from a sample of 820 observations at level 1 and 11 groups (level 2). I'm using stata xtmixed. Running the empty model (including ...
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15 views

Excluding Outliers and Influential Observations ($R^2$ and AIC/BIC)

I am working on a cross-sectional data set relating mortgage payments to debt-income ratios. I have some extreme outliers and experimented with excluding them from the model (some 30 observations of a ...
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14 views

Gamma vs tweedie distribution for large productivity dataset

I'm running some GAMs using the mgcv R package on a dataset with ~8.5k observations, where productivity is the response and environmental conditions are the covariates. However I am unsure of which ...
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45 views

Multiple linear regression, backward selection : Normality of the residuals?

I need to create a Multiple Linear regression model on those data explaining max03 T9 T12 T15 Ne9 Ne12 Ne15 Vx9 Vx12 Vx15 maxO3v !My data 1 My first intuition was to make a backward selection : ...
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“object 'logl.H0' not found” - Error in fitMeasures when calculating AIC for Lavaan model

EDIT: SOLVED The problem seems to have been an explanatory variable that was a factor. If it is made binary numeric insted, the values of BIC and AIC is calculated alright. However, the analyses give ...
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24 views

Akaike information criterion for Cox proportional hazard models

I am conducting an analysis of survival data using Cox proportional hazard (CPH) models, to figure out what is the best model to use. The models I am comparing are non-nested. My plan is to compute ...
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6 views

transforming of standardised effect size in MuMIN package

I ran the following model ...
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102 views

Should auto.arima in R ever report a model with higher AIC, AICC and BIC than other models considered?

I have used auto.arima to fit a time series model (a linear regression with ARIMA errors, as described on Rob Hyndman's site ) When finished - the output reports that the best model has a (5,1,0) ...
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10 views

AIC or similar selection techniques for Variograms?

I have a very basic question: how does one choose the "best" variogram? It is possible to fit different models to an empirical variogram, e.g. nugget, ...
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68 views

Assuming a probability density for MLE to do model selection

Motivation: I am trying to use Akaike Information Criterion to assess model ranking and over-fitting risk for a set of nonlinear models. I am an electrical engineer with no formal statistical training ...
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30 views

Model averaging with MuMIn. What's the mean of pvalue?

the summary() of a model.avg made with MuMIn in R, give a lot of interesting results, in particular model averaged coefficients (estimate, standard error, adjusted standard error and a z value with a ...
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G and R matrices in mixed model and model selection

I have data in which the plants were subjected to four conditions and measured weekly for a month. I would like to incorporate "plot" as a random factor into my linear mixed model using SPSS. I am ...
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31 views

Can we use the AIC values to compare a hurdle poisson model to a multinomial logit model?

I estimated two different models using an SP survey: Hurdle poisson and multinomial logit with 5 alternatives. My dependent variable is the number of weekly trips (0,1,2,3,4,5 trips) that students ...
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5 views

Relative variable importance for simple model set

I am evaluating models based on AIC. I started by running the simplest models and the dot model (no covariates) is the best model, with little support for any others. When reporting the relative ...
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6 views

multimodel inference when using rms package

I would be glad to have some advise about how to proceed with multimodel inference to obtain weighted estimates based on AICc after running ordinal logistic analyzes with "rms' package. I used the ...
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33 views

Model averaging when linear and quadratic effects are modeled in a global model

I am trying to derived estimates of model-averaged parameter effects on a fairly complicated set of models using an information-theoretic approach. I have several models that investigate continuous ...
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12 views

Is there a BIC or AIC formula for correcting a G-statistic?

I am using the G-test (http://en.wikipedia.org/wiki/G-test) for scoring models with different numbers of parameters in a model comparison problem. Is there a BIC or AIC formula to correct a ...
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33 views

Diagnostics for logistic regression and how to include/interpret interactions between categorical and continous variables?

I am working on a project that aims to identify the factors that affect the probability of detecting targets placed in different habitats in aerial photographs. I have done a lot of reading concerning ...
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23 views

AIC: relative versus absolute predictive error

I've read two interpretations of Akaike's Information Criterion (AIC) that seem to be in conflict, and I was hoping that someone could help me understand how to reconcile them. Interpretation 1: ...
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110 views

Suitable metric for consistency of parametric models

When fitting a parametric model to a data set assuming that our selected model class contains the truth, what performance metric should be used so that parameters converge to the truth as sample size ...
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114 views

comparing non nested models with AIC

say we have to glmms mod1 <- glmer (y ~ x + A + (1|g) data= dat) mod2 <- glmer (y ~ x + B + (1|g) data= dat) These models are not nested in the usual sense ...
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40 views

Calculation AIC without loglikelihood-function

I want to calculate the AIC without calculating the loglikelihood-function (which seems complicated). If the residuals are normally distributed, this can be done, according to wikipedia, as follows: ...
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43 views

AIC criterion: definition

I have two questions regarding the AIC criterion : AIC=$2k-2ln(l)$ Where does the number 2 comes from? As we usually minimize it why don't we consider only : $k-ln(l)$. (Maybe I am missing ...
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25 views

model comparison when alternatives are not all nested within one another

I am running a glmm with three fixed effects: opponent 1 size ("1") opponent 2 size ("2") opponent 1 size - opponent 2 size ("diff") I am unable to run all three variables in the model at once ...
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60 views

how to extract AIC(Akaike's Information Criterion) in LAR(Least Angle Regression) in R Studio?

I'm already done in conducting the whole LAR Algorithm using lars() function in R Studio. But my problem is how to extract or use AIC in R Studio for choosing enough the number of variable that will ...
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41 views

Comparing two models

I am interested in comparing two logistic regression models. The two models are nested: model 1 contains all predictors, and model 2 contains all predictors except 1. My goal is to test if removing ...
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the role of AIC versus p-values in model selection

Let's say you are trying to choose between two models. One has two significant fixed effects. The other includes only one of the two fixed effects from the aforementioned model but has a lower AIC ...
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36 views

Model selection using mean AIC for very huge data sets

I want to select a model which best performs for a very huge data set. However, the data set is too large to calculate a model within reasonable time. If this is the case, is the following a ...
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41 views

Choosing between two parameters in a model

I have a few parameters that are related (let's call them X1 and X2), and I want to use whichever one will provide the strongest model. The model has many other parameters. Would I simply be able to ...
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43 views

AICc and K for categorical factors and interactions

I am new to multimodel inference. I am trying to create a model that has multiple categorical factors and possible interactions. For example say that my model is... Y ~ X1 + factor(X2) + ...
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42 views

Can AIC be used to compare an ARMA model to an ARMA-GARCH model?

Suppose I have one time series and two competing models that describe it. Model 1 is ARMA$(p_1,q_1)$, model 2 is ARMA$(p_2,q_2)$-GARCH$(r,s)$. I obtain AIC values of model 1 and model 2. I would ...
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42 views

Normalized likelihoods

AIC (BIC) model selection methods are widely used. These methods can select non-nested models unlike likelihood ratio type selection that requires model to be nested. The AIC has definition ...
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57 views

Prediction vs. Explanation and its Effect on Statistical Methods [duplicate]

In layman's terms, what is the difference between predicting and explaining in statistics? I was looking for the differences between AIC and BIC and found this post with an answer stating: My ...
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34 views

Number of parameters for AIC for a particular model

I know there have been a few well answered questions on this topic, but i have found myself in a bit of a special case this time. I am using AIC for model selection, and i am having trouble counting ...
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Correlation between predictor variables in an AIC model

I'm using multinomial logistic regression analysis to analyse deer behavioural responses to camera traps based on 7 predictor variables. I have 2 models which are very close together in AIC value ...
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What is the upside of treating a factor as random in a mixed model?

I have a problem embracing the benefits of labeling a model factor as random for a few reasons. To me it appears like in almost all cases the optimal solution is to treat all of the factors as fixed. ...
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16 views

Choosing models with similar AIC values [duplicate]

I'm using a multinomial logistic regression analysis to examine deer behavioural responses to camera traps in terms of 7 predictors (both singly and their interactions). I have found that the model ...
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56 views

Understanding output stepAIC

I am using the stepAIC function in R to do a bi-directional (forward and backward) stepwise regression. I do not understand what each return value from the function ...
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51 views

How can I use Akaike's Information Criterion to compare two models of multi-peaked emission spectra?

I have several photoluminescence emission spectra that I am trying to fit curves to. The spectra each have a slight baseline and four peaks. The independent variable $x_i$ is wavelength (converted to ...
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2answers
60 views

Is cross validation for validating a model or for selecting best model in different kinds of models?

I am confused about the concept of cross validation and its usage. As I read about cross validation before, it is a way of validating a model. I did cross validation in my project (developing ...
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29 views

Akaike test and random effect model

I am analyzing a panel data (25 years x 30 countries) through OLS, FE, RE and mixed model (xtmixed). The results I get from the LM test and the Hausman show that I have to focus on the RE model. For ...
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IT-AIC approach for mixed effects model

I was reading around the Information Theoretic-AIC approach of model selection where AIC values are used to select the candidate set of models. I am quite clear on this. My question is this: for ...
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130 views

Model diagnostics for a glmmPQL in R mixed-effects model

Several texts (both online and published books) have been reviewed prior to asking this. What diagnostics are accepted as best practise for a generalised linear mixed-effects model fitted in R using ...
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295 views

AIC, BIC and GCV: what is best for making decision in penalized regression methods?

My general understanding is AIC deals with the trade-off between the goodness of fit of the model and the complexity of the model. $AIC =2k -2ln(L)$ $k$ = number of parameters in the model $L$ = ...
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Model without endogeneity correction has lower AIC than one with correction

I have two models, one with endogeneity correction (includes correction terms obtained using Heckman) and one without. The correction terms are significant in the second stage model, yet the AIC/BIC ...
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245 views

Unimodal or bimodal data (MATLAB)?

I am trying to figure out what I did wrong or what I could do to get accurate results. I have n vectors of data, and I am trying to decide whether each dataset is unimodal or bimodal. I assumed that ...
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161 views

The 'best' model selected with AICc have lower $R^2$ -square than the full/global model

I have used the R lme function (nlme package) to construct linear mixed models, with a single random effect (as a random intercept) and a varIdent variance structure on a fixed effect (that is a ...
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46 views

Cross-Validation vs. AICc for LASSO

I was working on a research project in which I try to estimate the the individual contribution of a group of regional political leaders to local economic growth. The major challenge is that there is ...
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134 views

If summarizing stats from multiple models is it meaningful to report a mean AIC?

I am currently summarizing results from several groups of models. Is it meaningful to report a mean AIC for each group of models? If not then how best to give a summary measure for each model ...
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after using AIC, how to determine the contribution or effect size of a individual covariate?

I am confused and looking for advise. I have found myself in this same situation repeatedly in the last few months. I want to know if covariate X is influential or important. However, I also ...