RCLSMIX-class: Class '"RCLSMIX"'

Description Objects from the Class Slots Author(s) References

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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