multModEv {modEvA} | R Documentation |
If you have a list of GLM model objects (created, e.g., with the multGLM
function of the 'fuzzySim' R-Forge package), or a data frame with presence-absence data and the corresponding predicted values for a set of species, you can use the multModEv
function to get a set of evaluation measures for all models simultaneously, as long as they all have the same sample size.
multModEv(models = NULL, obs.data = NULL, pred.data = NULL, measures = modEvAmethods("multModEv"), standardize = FALSE, thresh = NULL, bin.method = NULL, verbosity = 0, ...)
models |
a |
obs.data |
a data frame with observed (training or test) binary data. This argument is ignored if |
pred.data |
a data frame with the corresponding predicted (training or test) values, with both rows and columns in the same order as in |
measures |
character vector of the evaluation measures to calculate. The default is all implemented measures, which you can check by typing |
standardize |
logical, whether to standardize measures that vary between -1 and 1 to the 0-1 scale (see |
thresh |
argument to pass to |
bin.method |
the method with which to divide the data into groups or bins, for calibration or reliability measures such as |
verbosity |
integer specifying the amount of messages or warnings to display. Defaults to 0, but can also be 1 or 2 for more messages from the functions within. |
... |
optional arguments to pass to |
A data frame with the value of each evaluation measure for each model.
A. Marcia Barbosa
data(rotif.mods) eval1 <- multModEv(models = rotif.mods$models[1:6], thresh = 0.5, bin.method = "n.bins", fixed.bin.size = TRUE) head(eval1) eval2 <- multModEv(models = rotif.mods$models[1:6], thresh = "preval", measures = c("AUC", "CCR", "Sensitivity", "TSS")) head(eval2) # you can also calculate evaluation measures for a set of # observed vs predicted data, rather than from model objects: obses <- sapply(rotif.mods$models, `[[`, "y") preds <- sapply(rotif.mods$models, `[[`, "fitted.values") eval3 <- multModEv(obs.data = obses[ , 1:4], pred.data = preds[ , 1:4], thresh = "preval", bin.method = "prob.bins") head(eval3)