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0answers
16 views

How to obtain significance tests (/standard errors) that are still valid after variable selection?

I want to do variable selection via information criteria like the AIC or BIC. However, I am struggling with how I can obtain valid standard errors after doing this such that I can properly interpret ...
0
votes
0answers
7 views

Can I do model selection comparing model computed using aov and lmer?

I am interested in model selection and wonder if it is appropriate to compare an anova (generated using aov) to a linear mixed model (generated using lmer) using AIC?
1
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1answer
54 views

Overfitting model or issue of categorical predictors?

Is it possible to overfit a model by virtue of having too many categorical variables? I have 3 categorical variables and my dependent measure is continuous (or a ratio I guess, I'm measuring ...
1
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0answers
47 views

Glmer Model selection [duplicate]

Bit stuck on how to choose between models. My goal is to evidence the direction of the regression slope (negative shows improvement in a metric, positive shows decline). Models ...
1
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1answer
253 views

Model selection: Two-Part Mixed Effects Model for Semi-Continuous Data

I have now been studying mixed models for about a month, I am still a pure beginner. I have zero inflated semi continuous dependent variable (yield of trees between two periods). Exploring ...
0
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0answers
68 views

Model selection with AIC/BIC, which variables to remove?

I am running a Generalized Linear Mixed Model analysis in SPSS 25, and have gotten to the point where I would like to justify the selection of my final model based on information criteria. However, ...
0
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1answer
63 views

Why does AIC model rank order change in lme models with standardization of predictor variables?

I can't figure this out. The AIC/AICc rank of my mixed effect models are different whether or not I standardize my predictor values using rescale. Note, I'm not concerned that AICc is changing, as ...
0
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1answer
52 views

Can I compare AIC values for similar models fitted for two samples from the same population?

If I have two samples (n=300) from the same population and I'm fitting a GLMM (Generalised Linear Mixed-effects Model) with a similar response and explanatory variables but with a completely different ...
19
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3answers
15k 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 ...
22
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2answers
9k views

How should mixed effects models be compared and or validated?

How are (linear) mixed effects models normally compared against each other? I know likelihood ratio tests can be used, but this doesn't work if one model is not a 'subset' of the other correct? Is ...
3
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2answers
50 views

Comparing non-nested GLMMs with AIC

Suppose I want to compare four non-nested models: m0 = lm(y ~ 1) m1 = lm(y ~ x) m2 = lmer(y ~ 1 +(1|A)) m3 = lmer(y ~ x + (1|A)) Can we use AIC to compare ...
1
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1answer
46 views

Extremely large difference in AICs between two models

I am currently fitting a mixed model where I analye longitudinal trends in migration between country pairs (68335 observations nested in 6442 groups). One of the first questions I wanted to have ...
1
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0answers
197 views

Mixed linear models with exactly the same AIC?

I have a dataset in which I measure the response variable of a number of individual animals. I used this response variable as the dependent variable in a linear mixed model, for which I have 5 ...
1
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0answers
283 views

Fitting an HLM model in lme4

I'm a fairly inexperienced statistician fighting a huge deadline and just need some peace of mind that I'm not making a massive error here. I'd be most grateful for pointers. I've been playing ...
2
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1answer
521 views

How can I compare robust linear mixed models and get the p-values, F statistic, AIC and BIC?

I need to fit a linear mixed model but my dependent variable is has some outliers that I can't discard. Then I used the rlmer() function (...
1
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1answer
50 views

Can you remove a factor from your model if it has a significant effect, but the removal improves AIC and R square?

I have a complex problem but the title sums it up pretty easily. I have four types of cages that manipulate water flow, but I also have an actual measure of water flow from inside the cages. I'm ...
0
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1answer
570 views

AIC model selection when successive models have ΔAIC <2 compared to next best model

I have a set of linear mixed effects models with which I'm changing fixed effects, and comparing AIC for model selection. Something akin to this hypothetical model set. Sample is ~750 observations so ...
1
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0answers
67 views

Inexplicable AIC/BIC growth from base model in SPSS 23 in a mixed model

I am running a mixed model with a binary dependent variable and a logit link using SPSS 23´s Generalized linear mixed model procedure. The data is multilevel (1 Subject, 5 questions/data points per ...
2
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1answer
739 views

What are the consequences of including unnecessary random effects?

In mixed models (GLMMs), random effects are often used to account for non-independence between observations e.g. of the same patient, or of animals from the same farm. I sometimes see multiple random ...
31
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1answer
78k views

Negative values for AIC in General Mixed Model [duplicate]

I'm trying to select the best model by the AIC in the General Mixed Model test. The best model is the model with the lowest AIC, but all my AIC's are negative! So is the biggest negative AIC the ...
1
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0answers
145 views

model comparison of non-nested mixed-effects regressions fit to datasets of unequal sample size

My aim is to compare the performance of non-nested but closely related mixed-effects regression models fit to two datasets of different sample size. The data was obtained from behavioral experiments ...
6
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1answer
446 views

How can mixed-effect and fixed-effect generalised linear models be compared using BIC?

Assuming I have two generalised linear models, one with only fixed effects and another with fixed and random effects, how can I compare which model is most parsimonious using AIC/BIC? I can only fit ...
1
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0answers
55 views

How can I compare two mixed models with the same variables but not the same relations between them

I have created two different models that include the same variables but not the same relations between them. ...
1
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1answer
44 views

Calculating DeltaAIC

I've been advised by a colleague that when performing backwards elimination for model selection using AIC as my criterion, I should remove terms individually - starting from the most complex ...
1
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0answers
313 views

How to compare AIC for mixed effects vs fixed effects

I have time series data for 80 individuals and I'm trying to fit a nonlinear ODE. I am comparing the result of doing the fit separately for each individual vs. doing a mixed effects model. My approach ...
1
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1answer
250 views

Comparing Multiple Model Types (Poisson, Negative Binomial, ZINB)

I'm working with OTU count data (essentially counts of clustered sequence reads), and trying to determine differential abundance of counts between two groups. As of now, we're using a method defined ...
6
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1answer
2k views

Linear mixed effects model and - multiplicity issue and adjusting for p-values

In our randomized controlled trial, we used linear mixed effects models to test differences between groups in changes from baseline to six months while adjusting for important covariates. We ran ...
4
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2answers
717 views

Mixed effects model output - no difference in AIC values

In our study we are looking at the change in the numbers of acoustically tagged fish detections with respect to tidal state (ebbing, flowing), light period (dawn, day, dusk, night) and month (February,...
0
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1answer
47 views

AIC discordance in mixed effects modeling: which to prioritize?

I'd like to ask about mixed effects models. I'm modeling based on the significance of the likelihood ratio, within R. As a primary criterion I use the p-significance of the model comparison (...
2
votes
2answers
4k views

Relative variable importance values vs. magnitude of effect

I have ran a series of models to see which best fit the response variable and I got the following (for the model average of all models with a $\Delta AIC < 2$). I am currently learning models so ...
39
votes
5answers
75k views

Negative values for AICc (corrected Akaike Information Criterion)

I have calculated AIC and AICc to compare two general linear mixed models; The AICs are positive with model 1 having a lower AIC than model 2. However, the values for AICc are both negative (model 1 ...
8
votes
2answers
10k views

Relative variable importance with AIC

I am confused and just need some confirmation about calculating the relative variable importance value for the co-variates I used in AIC model selection procedures. I know that there is this one ...
2
votes
1answer
583 views

Model selection for random effects: can unselected random effects be used as fixed effects?

I am working on a mixed effects model. What I would consider random effects are year, sampling transect, and sampling location. There are multiple collections taken along each transect, and multiple ...
0
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0answers
96 views

Model selection for nested count data

What is the best tool-box for model selection when working with nested count data? Is AICc appropriate for comparing Poisson and negative binomial mixed models? Is there anything special to the ...
3
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2answers
6k 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 ...
5
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1answer
3k views

Number of parameters in mixed model

How can I tell how many parameters will be estimated for random effects in mixed models? Here is the example from the lme function in nlme: ...
2
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2answers
1k views

Using AIC to select between models that use nested and non-nested variables

I'm using SPSS to try and find a mixed model that adequate explains the data that I have. Two of the explanatory variables are closely related ('Sample group' and 'individual'), as an individual is ...
1
vote
1answer
2k views

AIC and anova p in multilevel model, how to interpret?

I have a model with a random-nested factor, I am comparing it with a model without the random factor (to test significance of random factor) as follows: ...
1
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1answer
51 views

Duplicate AICc values for multiple models with interactions

I am going through a model selection process with a mixed-model with 3 variables: A, B, and C. B and C are orthogonal factors. B or C may interact with A, so my full model would be: fixed: ...
0
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1answer
954 views

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 ...
10
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3answers
10k views

Generalized linear mixed models: model selection

This question/topic came up in a discussion with a colleague and I was looking for some opinions on this: I am modeling some data using a random effects logistic regression, more precisely a random ...
24
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5answers
3k views

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. ...
1
vote
1answer
833 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) + factor(X3) ...
1
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1answer
79 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 ...
1
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1answer
2k views

AICc results in R

I used the model: ...
1
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0answers
691 views

Selection of lme models using AIC & appropriate random effects & variance structure

I am using three categorical predictor variables $X_1$, $X_2$, $X_3$ and one continuous dependent variable $Y$, and I want to treat $X_3$ as a random effect. The simplest model I could come with: <...
2
votes
0answers
633 views

How to obtain predicted values from a gamm() using averaged coefficients (MuMIn)?

I want to extract the predicted values from a gamm() whose coefficients have been averaged using the package MuMIn, but I'm getting an error. ...
3
votes
2answers
721 views

How do I setup a model with hierarchical structure using lmer in R?

I am trying to isolate the important predictors for my response variable "Y". I know that "TL" (which is an individual level predictor) affects "Y", and now I want to determine if adding the site ...
1
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1answer
880 views

Negative and positive AICc/BIC for two models with transformed data - how to compare?

I am using AICc for model selection for transformed data (continous variable). One model I used $\log_{10}(PWV)$ as response and the other $\log(PWV)$ as response, but I am not sure which one to use ...