![]() The code runs fine (I guess), but in the results, I have this: Global model call: lmer(formula = formula, data = data) ![]() ![]() To assess multicollinearity between predictors when running the dredge function (MuMIn package), include the following max.r function as the "extra" argument: max.I am trying to MuMIn::dredge linear mixed-effect models lme4::lmer with categorical/continuous variables, the code is as follows: # Selection of variables of interest Answers without enough detail may be edited or deleted. Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. This was interesting, given that I had previously found high Pearson correlation between some predictors. If your models (obtained from the dredge function) are ranked according to their AIC value, using delta < 2 will select the best model (based on AIC), the second best and so on until the AIC difference between the last selected model and the next one is larger than 2. In my subsequent analysis, I've found that multicollinearity was not an issue for my models (all VIF values < 3). 1 the delta is used to subset a set of models based on a criteria. The blogger provides some useful code to calculate VIF for models from the lme4 package. Is using VIF more robust than simple Pearson correlation? My models take the form: model 5, then multicollinearity is high between predictors. ![]() I am currently running some mixed effect linear models. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |