`"RCLSMIX"`

Object of class `RCLSMIX`

.

Objects can be created by calls of the form `new("RCLSMIX", ...)`

.

`x`

:-
a list of objects of class

`REBMIX`

of length*o*obtained by running`REBMIX`

on*g = 1, …, s*train datasets*Y_{\mathrm{train}g}*all of length*n_{\mathrm{train}g}*. For the train datasets the corresponding class membership*\bm{Ω}_{g}*is known. This yields*n_{\mathrm{train}} = ∑_{g = 1}^{s} n_{\mathrm{train}g}*, while*Y_{\mathrm{train}q} \cap Y_{\mathrm{train}g} = \emptyset*for all*q \neq g*. Each object in the list corresponds to one chunk, e.g.,*(y_{1j}, y_{3j})^{\top}*. `o`

:-
number of chunks

*o*.*Y = \{\bm{y}_{j}; \ j = 1, …, n\}*is an observed*d*-dimensional dataset of size*n*of vector observations*\bm{y}_{j} = (y_{1j}, …, y_{dj})^{\top}*and is partitioned into train and test datasets. Vector observations*\bm{y}_{j}*may further be split into*o*chunks when running`REBMIX`

, e.g., for*d = 6*and*o = 3*the set of chunks substituting*\bm{y}_{j}*may be as follows*(y_{1j}, y_{3j})^{\top}*,*(y_{2j}, y_{4j}, y_{6j})^{\top}*and*y_{5j}*. `Dataset`

:-
a data frame containing test dataset

*Y_{\mathrm{test}}*of length*n_{\mathrm{test}}*. For the test dataset the corresponding class membership*\bm{Ω}_{g}*is not known. `s`

:-
finite set of size

*s*of classes*\bm{Ω} = \{\bm{Ω}_{g}; \ g = 1, …, s\}*. `ntrain`

:-
a vector of length

*s*containing numbers of observations in train datasets*Y_{\mathrm{train}g}*. `P`

:-
a vector of length

*s*containing prior probabilities*P(\bm{Ω}_{g}) = \frac{n_{\mathrm{train}g}}{n_{\mathrm{train}}}*. `ntest`

:-
number of observations in test dataset

*Y_{\mathrm{test}}*. `Zt`

:-
a factor of true class membership

*\bm{Ω}_{g}*for the test dataset. `Zp`

:-
a factor of predictive class membership

*\bm{Ω}_{g}*for the test dataset. `CM`

:-
a table containing confusion matrix for multiclass classifier. It contains number

*x_{qg}*of test observations with the true class*q*that are classified into the class*g*, where*q, g = 1, …, s*. `Accuracy`

:-
proportion of all test observations that are classified correctly.

*\mathrm{Accuracy} = \frac{∑_{g = 1}^{s} x_{gg}}{n_{\mathrm{test}}}*. `Error`

:-
proportion of all test observations that are classified wrongly.

*\mathrm{Error} = 1 - \mathrm{Accuracy}*. `Precision`

:-
a vector containing proportions of predictive observations in class

*g*that are classified correctly into class*g*.*\mathrm{Precision}(g) = \frac{x_{gg}}{∑_{q = 1}^{s} x_{qg}}*. `Sensitivity`

:-
a vector containing proportions of test observations in class

*g*that are classified correctly into class*g*.*\mathrm{Sensitivity}(g) = \frac{x_{gg}}{∑_{q = 1}^{s} x_{gq}}*. `Specificity`

:-
a vector containing proportions of test observations that are not in class

*g*and are classified into the non*g*class.*\mathrm{Specificity}(g) = \frac{n_{\mathrm{test}} - ∑_{q = 1}^{s} x_{qg}}{n_{\mathrm{test}} - ∑_{q = 1}^{s} x_{gq}}*. `Chunks`

:-
a vector containing selected chunks.

Marko Nagode

D. M. Dziuda. Data Mining for Genomics and Proteomics: Analysis of Gene and Protein Expression Data. John Wiley & Sons, New York, 2010.

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