Scoring {multiridge} | R Documentation |
Evaluate predictions
Description
Evaluates predictions by a score suitable for the corresponding response
Usage
Scoring(lp, Y, model = NULL, score = ifelse(model == "linear", "mse", "loglik"),
print = TRUE)
Arguments
lp |
Numerical vector. Linear predictor. |
Y |
Response vector: numeric, binary, factor or |
score |
Character. See Details. |
model |
Character. Any of |
print |
Boolean. Should the score be printed on screen. |
Details
Several scores are allowed, depending on the type of output. For model = "linear"
,
score
equals any of c("loglik","mse","abserror","cor","kendall","spearman")
, denoting
CV-ed log-likelihood, mean-squared error, mean absolute error, Pearson (Kendall, Spearman) correlation with response.
For model = "logistic"
, score
equals any of c("loglik","auc", "brier")
, denoting
CV-ed log-likelihood, area-under-the-ROC-curve, and brier score a.k.a. MSE.
For model = "cox"
, score
equals any of c("loglik","cindex")
, denoting
CV-ed log-likelihood, and c-index.
Value
Numerical value.
See Also
CVscore
for obtaining the cross-validated score (for given penalties), and doubleCV
to obtain doubly cross-validated linear predictors to which Scoring
can be applied to estimated predictive performance by double cross-validation. A full demo and data are available from:
https://drive.google.com/open?id=1NUfeOtN8-KZ8A2HZzveG506nBwgW64e4