# load sample models:
data(rotif.mods)

# choose a particular model to play with:
mod <- rotif.mods$models[[1]]

# plot this model:
plotGLM(model = mod)

# calculate the area under the ROC curve for the model:
AUC(model = mod)

# calculate some threshold-based measures for this model:
threshMeasures(model = mod, thresh = 0.5)
threshMeasures(model = mod, thresh = "preval")

# calculate optimal thresholds based on several criteria:
optiThresh(model = mod, measures = c("CCR", "Sensitivity", "kappa", "TSS"), 
ylim = c(0, 1))

# calculate the optimal threshold balancing two evaluation measures:
optiPair(model = mod, measures = c("Sensitivity", "Specificity"))

# calculate the explained deviance, Hosmer-Lemeshow goodness-of-fit,
# Miller's calibration stats, and (pseudo) R-squared values for the model:
Dsquared(model = mod)
HLfit(model = mod, bin.method = "quantiles")
MillerCalib(model = mod)
RsqGLM(model = mod)

# calculate a bunch of evaluation measures for a set of models:
multModEv(models = rotif.mods$models[1:4], thresh = "preval", 
bin.method = "quantiles")