These performance measures can be used with prediction and reference being continuous class memberships in [0, 1].
Calculate the soft confusion matrix
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  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 = 1e08, 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)

r 
vector, matrix, or array with reference. 
p 
vector, matrix, or array with predictions 
groups 
grouping variable for the averaging by 
operator 
the 
drop 
should the results possibly be returned as vector instead of 1d array? (Note that
levels of 
.checked 
for internal use: the inputs are guaranteed to be of same size and shape. If

... 
handed to 
op.dev 
does the operator measure deviation? 
op.postproc 
if a postprocessing function is needed after averaging, it can be given here. See the example. 
eps 
limit below which denominator is considered 0 
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
.
numeric of size (ngroups x dim (p) [1]
) with the respective performance measure
Claudia Beleites
see the literature in citation ("softclassval")
Operators: prd
For the complete confusion matrix, confmat
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  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
## SensitivitySpecificity 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)

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