Calculate the mean square error (Brier score) for a model.
mse(object, ..., subset = TRUE) brier(object, ..., subset = TRUE) # S3 method for elo.run mse(object, ..., subset = TRUE) # S3 method for elo.glm mse(object, ..., subset = TRUE) # S3 method for elo.running mse(object, running = TRUE, discard.skipped = FALSE, ..., subset = TRUE) # S3 method for elo.markovchain mse(object, ..., subset = TRUE) # S3 method for elo.winpct mse(object, ..., subset = TRUE) # S3 method for elo.colley mse(object, ..., subset = TRUE)
| object | An object |
|---|---|
| ... | Other arguments (not used at this time). |
| subset | (optional) A vector of indices on which to calculate |
| running | logical, denoting whether to use the running predicted values. |
| discard.skipped | Logical, denoting whether to ignore the skipped observations in the calculation |
Even though logistic regressions don't use the MSE on the y=0/1 scale, it can still be informative.
Note that the S3 method is mse.