pm: Calculate Probability Matrix

In mcca: Multi-Category Classification Accuracy

Description

compute the probability matrix of two or three or four categories classifiers with an option to define the specific model or user-defined model.

Usage

pm(y, d, method="multinom", ...)

Arguments

y	The multinomial response vector with two, three or four categories. It can be factor or integer-valued.
d	The set of candidate markers, including one or more columns. Can be a data frame or a matrix.
method	Specifies what method is used to construct the classifier based on the marker set in d. Available option includes the following methods:"multinom": Multinomial Logistic Regression which is the default method, requiring R package nnet;"tree": Classification Tree method, requiring R package rpart;"svm": Support Vector Machine (C-classification and radial basis as default), requiring R package e1071;"lda": Linear Discriminant Analysis, requiring R package lda.
...	Additional arguments in the chosen method's function.

Details

The function returns the probability matrix for predictive markers based on a user-chosen machine learning method. Currently available methods include logistic regression (default), tree, lda, svm and user-computed risk values.

Value

The probability matrix of the classification using a particular learning method on a set of marker(s).

Author(s)

Ming Gao: [e-mail]

Jialiang Li: [e-mail]

References

Li, J. and Fine, J. P. (2008): ROC analysis with multiple tests and multiple classes: methodology and applications in microarray studies. Biostatistics. 9 (3): 566-576.

Li, J., Chow, Y., Wong, W.K., and Wong, T.Y. (2014). Sorting Multiple Classes in Multi-dimensional ROC Analysis: Parametric and Nonparametric Approaches. Biomarkers. 19(1): 1-8.

See Also

pdi

Examples

str(iris)
data <- iris[, 1:4]
label <- iris[, 5]
pm(y = label, d = data,method = "multinom")

mcca documentation built on Dec. 20, 2019, 9:07 a.m.