Class "RCLSMIX"

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Description

Object of class RCLSMIX.

Objects from the Class

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

Slots

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.

Author(s)

Marko Nagode

References

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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