avAcc: Average accuracy data

In SPreFuGED: Selecting a Predictive Function for a Given Gene Expression Data

Description

A data frame of 11520 data points (rows) corresponding to the 1152 different simulation scenarios by 10 classification functions. The studied data variables and the average misclassification error rates (acc) of the classification functions on the columns.

Usage

data("avAcc")

Format

A data frame with 11520 observations on the following 8 variables.

class

a character vector of the predictive functions

variance

a numeric vector of the different values of the variance parameter

deCorr

a numeric vector of different pairwise correlation values within informative genes

propDE

a numeric vector different values of the proportion of informative genes

log2FC

a numeric vector of different values of absolute log2 fold changes

sampSize

a numeric vector of different values of absolute sample sizes

otherCorr

a numeric vector of the different values of the parameter for the pairwise correlations within non-informative and/or between non-informative and informative genes

acc

a numeric vector of misclassification error rates

Details

This data contains the different values of the six studied factors and the error rates for the different fucntions in different simulation scenarios. And it is required for binary direct classification, to fit the LME model necessary to predict optimal function(s) for a given gene expression dataset.

Source

Jong VL, Novianti PW, Roes KCB & Eijkemans MJC. Selecting a classification function for class prediction with gene expression data. Bioinformatics (2016) 32(12): 1814-1822

Examples

data(avAcc)
## maybe str(avAcc) ; plot(avAcc) ...

SPreFuGED documentation built on May 2, 2019, 9:40 a.m.