sda_ranking: A MVDA Function
In angy89/MVDA_package: Multi-View Biological Data Analysis

Description Usage Arguments Value

This function determines a ranking of predictors by computing CAT scores (correlation-adjusted t-scores) between the group centroids and the pooled mean by using fucntion of the package sda.

1 2	sda_ranking(prototype = NULL, info = NULL, fdr = FALSE, ranking.score = "avg", lambda = 0.5)

`prototype`	is the matrix of prototype
`info`	is the factor of class label
`fdr`	compute FDR values and HC scores for each feature. default = FALSE
`ranking.score`	how to compute the summary score for each variable from the CAT scores of all classes. default = "avg"
`lambda`	Shrinkage intensity for the correlation matrix. If not specified it is estimated from the data. lambda=0 implies no shrinkage and lambda=1 complete shrinkage. default = 0.5

return a matrix with the following columns: idx original feature number score sum of the squared CAT scores across groups - this determines the overall ranking of a feature cat for each group and feature the cat score of the centroid versus the pooled mean

angy89/MVDA_package documentation built on May 7, 2019, 8:58 p.m.