Can a GLMM cause problems with model convergence?
This post was last updated on 2020-06-26. Fitting generalized linear mixed models (GLMM) can be tricky. For example, having explanatory variables with very different magnitudes can cause problems with model convergence.
Which is the S3 method for default predictoreffects?
# S3 method for default predictorEffects (mod, predictors = ~ ., focal.levels=50, xlevels=5., sources) A model object. Supported models include all those described on the help page for Effect. quoted name of the focal predictor. If the default, ~ ., a predictor effect plot is drawn for each predictor (not regressor) in a model.
When to draw a predictor effect plot in rdocumentation?
If the default, ~ ., a predictor effect plot is drawn for each predictor (not regressor) in a model. Otherwise, this is a one-sided formula specifying the first-order predictors for which predictor effect plots are to be drawn.
How is the predictor effect of a focal predictor plotted?
The predictor effect of a focal predictor, say x1, is the usual effect for the generalized interaction of x1 with all the other predictors in a model. When a predictor effect object is plotted, the focal predictor is by default plotted on the horizontal axis.
What causes problems with fitting generalized linear mixed models?
Fitting generalized linear mixed models (GLMM) can be tricky. For example, having explanatory variables with very different magnitudes can cause problems with model convergence. Standardizing the explanatory variables by subtracting the mean and dividing by the standard deviation prior to model fitting can often fix this issue.
How to standardize the explanatory variables in a model?
Standardizing the explanatory variables by subtracting the mean and dividing by the standard deviation prior to model fitting can often fix this issue. However, the interpretation of standardized coefficients is about the change in mean response for a 1 standard deviation increase in a variable; this is not particularly intuitive.