query: How to choose predictor variables for GLM / GLMM from rather large data set?
positive_passages.docid: 25539
positive_passages.text: Algorithms for automatic model selection
negative_passages.docid: 142875
negative_passages.text: Moderated regression: Why do we calculate a *product* term between the predictors?
negative_passages.docid: 52765
negative_passages.text: When to use a GAM vs GLM
negative_passages.docid: 710
negative_passages.text: Importance of predictors in multiple regression: Partial $R^2$ vs. standardized coefficients
negative_passages.docid: 27838
negative_passages.text: Caret and coefficients (glmnet)
negative_passages.docid: 78581
negative_passages.text: Variable importance from GLMNET
negative_passages.docid: 83905
negative_passages.text: How to deal with quasi-complete separation in a logistic GLMM?
negative_passages.docid: 44881
negative_passages.text: Using lasso for feature selection, followed by a non-regularized regression
negative_passages.docid: 152881
negative_passages.text: Using R's lm on a dataframe with a list of predictors
negative_passages.docid: 34324
negative_passages.text: How would you deal with a bimodally distributed predictor variable in bivariate logistic regression?
negative_passages.docid: 5026
negative_passages.text: Can I use multiple regression when I have mixed categorical and continuous predictors?
negative_passages.docid: 59695
negative_passages.text: How to force R to use a specified factor level as reference in a regression?
negative_passages.docid: 47428
negative_passages.text: Can a random forest be used for feature selection in multiple linear regression?
negative_passages.docid: 110802
negative_passages.text: Model selection with low N?
negative_passages.docid: 86632
negative_passages.text: Only binary predictors -- ANOVA, regression, or other?
negative_passages.docid: 99864
negative_passages.text: Using a regression model to make prediction: When to stop?
negative_passages.docid: 54879
negative_passages.text: Using x to predict y when they are highly correlated