metrics {PlayerRatings} | R Documentation |
Prediction Evaluation
Description
Returns measures that assess prediction performance.
Usage
metrics(act, pred, cap = c(0.01,0.99), which = 1:3, na.rm = TRUE,
sort = TRUE, digits = 3, scale = TRUE)
Arguments
act |
A numeric vector of actual values. Typically equal to one for a player one win, zero for a player two win, and one half for a draw. |
pred |
A numeric vector of predictions, typically values between zero and one. A matrix can also be given, in which case the jth column contains the predictions for model j. |
cap |
A numeric vector of length two giving values at which to cap the binomial deviance. |
which |
Select metrics using any subset of |
na.rm |
Remove missing values in predictions. The default is to remove missing values because the default predict method will predict missing values for games with new players. |
sort |
By default output is ordered from best to worst using the first metric specified. |
digits |
Round to this number of digits. |
scale |
If |
Details
The preferred metric for assessing predictions in chess is the capped binomial deviance. Mean squared error and mean absolute error metrics are also produced. By default all metrics are scaled so that the value 100 represents the zero information case. If not scaled, then all metrics are multiplied by 100.
Value
A numeric vector.
See Also
Examples
afl <- aflodds[,c(2,3,4,7)]
train <- afl[afl$Week <= 80,]
test <- afl[afl$Week > 80,]
robj <- elo(train)
metrics(test$Score, predict(robj, test))
metrics(test$Score, predict(robj, test), scale = FALSE)