Description Usage Arguments Value Functions
Functions to assess model performance
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | myaccuracy(predicted, y, criteria = mse, ...)
mse(predicted, y)
loglik_binary(predicted, y)
Brier(predicted, y, confint = FALSE)
dis_slope(predicted, y)
c_index(predicted, y, confint = FALSE, ci_collapse = "-",
ci_parentheses = FALSE)
concordance(x, y)
calib(predicted, y, confint = FALSE)
binperform(predicted, y, cutoff = 0.5)
sen(predicted, y, cutoff)
spe(predicted, y, cutoff)
ppv(predicted, y, cutoff)
npv(predicted, y, cutoff)
acc(predicted, y, cutoff)
auc(predicted, y, alpha = 0.05, digits = 2)
best.youden(predicted, y, by = 0.01, digits = 2, name = "")
best.acc(predicted, y, by = 0.01, digits = 2, name = "")
new.accuracy(predicted, y, cutoff = 0.5)
|
predicted |
Predicted values from fitted models |
y |
Response |
numeric value of model's performance
mse
: Mean squared errors
loglik_binary
: log-likelihood (binary case)
Brier
: Brier score
dis_slope
: Discrimination slope
c_index
: AUC (with confidence interval)
concordance
: Concordance
calib
: Calibration slope and intercept (with confidence intervals)
binperform
: Other measures of binary test
sen
: Sensitivity
spe
: Specificity
ppv
: Positive predictive value
npv
: Negative predictive value
acc
: Accuracy rate
auc
: AUC
best.youden
: Best Youden's index
best.acc
: Best accuracy rate
new.accuracy
: Accuracy of binary test
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