Description

This page attempts to summarize some of the common problems with fitting [gn]lmer models and how to troubleshoot them.

• failure to converge in (xxxx) evaluations The optimizer hit its maximum limit of function evaluations. To increase this, use the optControl argument of [g]lmerControl – for Nelder_Mead and bobyqa the relevant parameter is maxfun; for optim and optimx-wrapped optimizers, including nlminbwrap, it's maxit; for nloptwrap, it's maxeval.

• Model failed to converge with max|grad| ... The scaled gradient at the fitted (RE)ML estimates is worryingly large. Try

  • refitting the parameters starting at the current estimates: getting consistent results (with no warning) suggests a false positive

  • switching optimizers: getting consistent results suggests there is not really a problem; getting a similar log-likelihood with different parameter estimates suggests that the parameters are poorly determined (possibly the result of a misspecified or overfitted model)

  • compute values of the deviance in the neighbourhood of the estimated parameters to double-check that lme4 has really found a local optimum.

• Hessian is numerically singular: parameters are not uniquely determined The Hessian (inverse curvature matrix) at the maximum likelihood or REML estimates has a very large eigenvalue, indicating that (within numerical tolerances) the surface is completely flat in some direction. The model may be misspecified, or extremely badly scaled (see "Model is nearly unidentifiable").

• Model is nearly unidentifiable ... Rescale variables? The Hessian (inverse curvature matrix) at the maximum likelihood or REML estimates has a large eigenvalue, indicating that the surface is nearly flat in some direction. Consider centering and/or scaling continuous predictor variables.

• Contrasts can be applied only to factors with 2 or more levels One or more of the categorical predictors in the model has fewer than two levels. This may be due to user error when converting these predictors to factors prior to modeling, or it may result from some factor levels being eliminated due to NAs in other predictors. Double-check the number of data points in each factor level to see which one is the culprit: lapply(na.omit(df[,vars]), table) (where df is the data.frame and vars are the column names of your predictor variables).