Calculate confusion matrix, gain and RGain measure.
tdmModConfmat(d, colreal, colpred, opts, predProb = NULL)data frame
name of column in d which contains the real class
name of column in d which contains the predicted class
a list from which we use the elements:
gainmat: the gain matrix for each possible outcome, same size as cm$mat (see below).
gainmat[R1,P2] is the gain associated with a record of real class R1 which we
predict as class P2. (gain matrix = - cost matrix)
rgain.type: one out of {"rgain" | "meanCA" | "minCA" | "bYouden"
| "arROC" | "arLIFT" | "arPRE" },
affects output cm$mat and cm$rgain, see below.
if not NULL, a data frame with as many rows as data frame d, containing
columns (index, true label, predicted label, prediction score). Is only needed
for opts$rgain.type=="ar*".
cm, a list containing:
matrix with real class levels as rows, predicted class levels columns.
mat[R1,P2] is the number of records with real class R1
predicted as class P2, if opts$rgain.type=="rgain".
If opts$rgain.type=="meanCA" or "minCA", then show this number as percentage
of "records with real class R1" (percentage of each row).
CAUTION: If there are NA's in column colpred, those cases are missing
in mat (!) (but the class errors are correct as long as there are
no NA's in column colreal)
class error rates, vector of size nlevels(colreal)+1.
cerr[X] is the misclassification rate for real class X.
cerr["Total"] is the total classification error rate.
the total gain (sum of pointwise product opts$gainmat*cm$mat)
gain.vector[X] is the gain attributed to real class label X. gain.vector["Total"] is again the total gain.
the maximum achievable gain, assuming perfect prediction
Depending on the value of opts$rgain.type:
"rgain": ratio gain/gainmax in percent,
"meanCA": mean class accuracy percentage (i.e. mean(diag(cm$mat)),
"minCA": min class accuracy percentage (i.e. min(diag(cm$mat)),
"bYouden": balanced Youden index: min(sensitivity,specificity),
"arROC": area under ROC curve (a number in [0,1]),
"arLIFT": area between lift curve and horizontal line 1.0,
"arPRE": area under precision-recall curve (a number in [0,1])