softclassval (version 1.0-20160527)

confusion: Performance calculation for soft classification

Description

These performance measures can be used with prediction and reference being continuous class memberships in [0, 1].

Calculate the soft confusion matrix

Usage

confusion(r = stop("missing reference"), p = stop("missing prediction"), groups = NULL, operator = "prd", drop = FALSE, .checked = FALSE)
confmat(r = stop("missing reference"), p = stop("missing prediction"), ...)
sens(r = stop("missing reference"), p = stop("missing prediction"), groups = NULL, operator = "prd", op.dev = dev(match.fun(operator)), op.postproc = postproc(match.fun(operator)), eps = 1e-08, drop = FALSE, .checked = FALSE)
spec(r = stop("missing reference"), p = stop("missing prediction"), ...)
ppv(r = stop("missing reference"), p = stop("missing prediction"), ..., .checked = FALSE)
npv(r = stop("missing reference"), p = stop("missing prediction"), ..., .checked = FALSE)

Arguments

r

vector, matrix, or array with reference.

p

vector, matrix, or array with predictions

groups

grouping variable for the averaging by rowsum. If NULL, all samples (rows) are averaged.

operator

the operators to be used

drop

should the results possibly be returned as vector instead of 1d array? (Note that levels of groups are never dropped, you need to do that e.g. by factor.)

.checked

for internal use: the inputs are guaranteed to be of same size and shape. If TRUE, confusion omits input checking

...

handed to sens

op.dev

does the operator measure deviation?

op.postproc

if a post-processing function is needed after averaging, it can be given here. See the example.

eps

limit below which denominator is considered 0

Value

numeric of size (ngroups x dim (p) [-1]) with the respective performance measure

Details

The rows of r and p are considered the samples, columns will usually hold the classes, and further dimensions are preserved but ignored.

r must have the same number of rows and columns as p, all other dimensions may be filled by recycling.

spec, ppv, and npv use the symmetry between the performance measures as described in the article and call sens.

References

see the literature in citation ("softclassval")

See Also

Operators: prd

For the complete confusion matrix, confmat

Examples

ref <- softclassval:::ref
ref

pred <- softclassval:::pred
pred

## Single elements or diagonal of confusion matrix
confusion (r = ref, p = pred)

## complete confusion matrix
cm <- confmat (r = softclassval:::ref, p = pred) [1,,]
cm

## Sensitivity-Specificity matrix:
cm / rowSums (cm)

## Matrix with predictive values:
cm / rep (colSums (cm), each = nrow (cm))

## sensitivities
sens (r = ref, p = pred)

## specificities
spec (r = ref, p = pred)

## predictive values
ppv (r = ref, p = pred)
npv (r = ref, p = pred)