mcsens is used to calculate the multiclass sensitivity of a predicted
outcome variable relative to an observed outcome variable. Multiclass
sensitivity is calculated as the total number of true postive predicted outcomes
of each class divided by total observed outcomes. For more details see
Sensitivity.

mcprec is used to calculate the multiclass precision of a predicted
outcome variable relative to an observed outcome variable. Multiclass precision
is calculated as the total number of true postive predicted outcomes of each
class divided by total predicted outcomes. For more details see
Precision.

mcrecall is used to calculate the multiclass recall of a predicted outcome
variable relative to an observed outcome variable. Multiclass recall is a
synonym for multiclass sensitivity. For more details see
Recall.

mcspec is used to calculate the multiclass specificity of a predicted
outcome variable relative to an observed outcome variable. Multiclass
specificity is calculated as the total number of true negative predicted
outcomes of each class divided by the total of true negative and false positive
outcomes of each class. For more details see
Specificity.

mcppv is used to calculate multiclass positive predicted values (PPV). As
in the binary case, multiclass PPV can be understood as the share of predicted
positives that are actually positive. Multiclass PPV is defined identically to
multiclass precision. For more details see
PPV.

mcnpv is used to calculate multiclass negative predicted values (NPV). As
in the binary case, multiclass NPV can be understood as the share of predicted
negatives that are actually negative. For more details see
NPV.

mcacc is used to calculate the multiclass accuracy of a predicted outcome
variable relative to an observed outcome variable. In both binary and
multiclass cases, accuracy can be understood as the share of the data predicted
correctly and is the ratio of true positives plus true negatives relative to all
predicted outcomes. This option is identical to the binary accuracy option,
acc. For more details see
Accuracy.

mcbacc is used to calcuate the multiclass balanced accuracy of a predicted
outcome variable relative to an observed outcome variable. Multiclass balanced
accuracy can be understood as the unweighted mean of multiclass sensitivity and
multiclass specificity. For more details see
Balanced Accuracy.

mcmcc is used to calculate Matthew's correlation coefficient (MCC). The
MCC is a correlation coefficient between the observed and predicted binary
variables that range from [−1, 1]. This option is identical to the binary
Matthews correlation coefficient option, mcc. For more details see
Matthew's Correlation Coefficient.

mcf1 is used to calculate the multiclass F1 score. The F1 score is an
alternate measure of a model's accuracy. F1 score is calculated as the harmonic
mean of the multiclass precision and recall metrics, with an optional parameter
beta that defaults to 1. You can specify a value of beta to use by passing a
1 x 1 real matrix as an optional argument to this function. For example, to
specify a value of 1.5 for beta when using mcf1 as a metric, the option
would look like this: metric(mcf1((1.5))). For more details
see F1.

mcjindex is used to calculate multiclass Youden's J statistic (J-index).
The J statistic is defined as the sum of the multiclass sensitivity and
specificity minus one and ranges from [0, 1]. For more details see
Youden's J.

mcordr2 is used to calculate the polychoric correlation of two ordinal
variables. This option requires the installation of polychoric
developed by Stas Kolenikov. If you already have polychoric installed
see polychoric for additional details.

mcdetect is used to calculate the multiclass detection prevalence.
Multiclass detection prevalence is defined as the sum of true posttive and false
positive predicted outcomes for each class divided by the total number of
predicted outcomes. For more details see
Detection Prevalence.

mckappa is used to calculate the multiclass Kappa. Kappa is similar to
multiclass accuracy but is normalized by the accuracy expected by random chance.
Additionally, users may alter the weighting matrix used in the calculation by
passing an argument to this function, where the value specified in the option is
the power to which the alternate weighting matrix is raised. For example, if
you wanted to use mckappa as a metric with a quadratic weighting matrix
you would code: metric(mckappa((2))). For more details see
Kappa.