| pm {mcca} | R Documentation |
Calculate Probability Matrix
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: gaoming@umich.edu
Jialiang Li: stalj@nus.edu.sg
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
Examples
str(iris)
data <- iris[, 1:4]
label <- iris[, 5]
pm(y = label, d = data,method = "multinom")