prediction0: [!] Function to create prediction objects

In GegznaV/spHelper: Extension of Package `hyperSpec` and Various Convenience Functions

[!] Function to create prediction objects

Description

This function is similar to prediction (package ROCR, which is described: Every classifier evaluation using ROCR starts with creating a prediction object. This function is used to transform the input data (which can be in vector, matrix, data frame, or list form) into a standardized format.

Usage

prediction0(predictions, labels, label.ordering = NULL, neg.smaller = NULL)

Arguments

predictions	A vector, matrix, list, or data frame containing the predictions.
labels	A vector, matrix, list, or data frame containing the true class labels. Must have the same dimensions as predictions.
label.ordering	The default ordering (cf.details) of the classes can be changed by supplying a vector containing the negative and the positive class label.
neg.smaller	Either logical or NULL (default): TRUE value of of negative group is always smaller (default in function prediction); FALSE value of of negative group is always larger; NULL it is automatically decided either value of negative group is smaller or larger by calculating and comparing 10 groups.

Details

The main difference in prediction0 is that it calculates performance in a more flexible way. I.e. value of negative group do not have to be smaller than in positive group (see description of parameter neg.smaller)

predictions' and 'labels' can simply be vectors of the same length. However, in the case of cross-validation data, different cross-validation runs can be provided as the *columns* of a matrix or data frame, or as the entries of a list. In the case of a matrix or data frame, all cross-validation runs must have the same length, whereas in the case of a list, the lengths can vary across the cross-validation runs. Internally, as described in section 'Value', all of these input formats are converted to list representation.

Since scoring classifiers give relative tendencies towards a negative (low scores) or positive (high scores) class, it has to be declared which class label denotes the negative, and which the positive class. Ideally, abels should be supplied as ordered factor(s), the lower level corresponding to the negative class, the upper level to the positive lass. If the labels are factors (unordered), numeric, logical or characters, ordering of the labels is inferred from R's built-in < relation (e.g. 0 < 1, -1 < 1, 'a' < 'b', FALSE < TRUE). Use label.ordering to override this default ordering. Please note that the ordering can be locale-dependent e.g. for character labels '-1' and '1'.

Currently, ROCR supports only binary classification (extensions toward multiclass classification are scheduled for the next release, however). If there are more than two distinct label symbols, execution stops with an error message. If all predictions use the same two symbols that are used for the labels, categorical predictions are assumed. If there are more than two predicted values, but all numeric, continuous predictions are assumed (i.e. a scoring classifier). Otherwise, if more than two symbols occur in the predictions, and not all of them are numeric, execution stops with an error message.

Value

An S4 object of class prediction.

See Also

Detailed descriptions and references: prediction.

Examples

# create a simple prediction object
library(spHelper)
data(ROCR.simple,package = "ROCR")
x  <- ROCR.simple$predictions
gr <- ROCR.simple$labels
pred <- prediction(x, gr)

pred0 <- prediction0(x, gr)

GegznaV/spHelper documentation built on April 16, 2023, 1:42 p.m.