"RCLSMIX"Object of class RCLSMIX.
Objects can be created by calls of the form new("RCLSMIX", ...). Accessor methods for the slots are a.o(x = NULL),
a.Dataset(x = NULL), a.s(x = NULL), a.ntrain(x = NULL), a.P(x = NULL), a.ntest(x = NULL), a.Zt(x = NULL),
a.Zp(x = NULL), a.CM(x = NULL), a.Accuracy(x = NULL), a.Error(x = NULL), a.Precision(x = NULL), a.Sensitivity(x = NULL),
a.Specificity(x = NULL) and a.Chunks(x = NULL), where x stands for an object of class RCLSMIX.
x:a list of objects of class REBMIX of length \(o\) obtained by running REBMIX on \(g = 1, \ldots, s\) train datasets \(Y_{\mathrm{train}g}\) all of length \(n_{\mathrm{train}g}\).
For the train datasets the corresponding class membership \(\bm{\Omega}_{g}\) is known. This yields
\(n_{\mathrm{train}} = \sum_{g = 1}^{s} n_{\mathrm{train}g}\), 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 \(o\). \(Y = \{\bm{y}_{j}; \ j = 1, \ldots, n\}\) is an observed \(d\)-dimensional dataset of size \(n\) of vector observations \(\bm{y}_{j} = (y_{1j}, \ldots, y_{dj})^{\top}\) 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 \(\bm{\Omega}_{g}\) is not known.
s:finite set of size \(s\) of classes \(\bm{\Omega} = \{\bm{\Omega}_{g}; \ g = 1, \ldots, s\}\).
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 \(P(\bm{\Omega}_{g}) = \frac{n_{\mathrm{train}g}}{n_{\mathrm{train}}}\).
ntest:number of observations in test dataset \(Y_{\mathrm{test}}\).
Zt:a factor of true class membership \(\bm{\Omega}_{g}\) for the test dataset.
Zp:a factor of predictive class membership \(\bm{\Omega}_{g}\) 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 \(q, g = 1, \ldots, s\).
Accuracy:proportion of all test observations that are classified correctly. \(\mathrm{Accuracy} = \frac{\sum_{g = 1}^{s} x_{gg}}{n_{\mathrm{test}}}\).
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 \(g\). \(\mathrm{Precision}(g) = \frac{x_{gg}}{\sum_{q = 1}^{s} x_{qg}}\).
Sensitivity:a vector containing proportions of test observations in class \(g\) that are classified correctly into class \(g\). \(\mathrm{Sensitivity}(g) = \frac{x_{gg}}{\sum_{q = 1}^{s} x_{gq}}\).
Specificity:a vector containing proportions of test observations that are not in class \(g\) and are classified into the non \(g\) class. \(\mathrm{Specificity}(g) = \frac{n_{\mathrm{test}} - \sum_{q = 1}^{s} x_{qg}}{n_{\mathrm{test}} - \sum_{q = 1}^{s} x_{gq}}\).
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.