avAcc


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.


datasets

The recent advancement of high-throughput technologies has led to frequent utilization of gene expression and other "omics" data for toxicological, diagnostic or prognostic studies in and clinical applications. Unlike in classical predictions where the number of samples is greater than the number of variables (n>p), the challenge faced with prediction using "omics" data is that the number of parameters greatly exceeds the number of samples (p>>n). To solve this curse of dimensionality problem, several predictive functions have been proposed for direct and probabilistic classification and survival predictions. Nevertheless, these predictive functions have been shown to perform differently across datasets. Comparing predictive functions and choosing the best is computationally intensive and leads to selection bias. Thus, the question which function should one choose for a given dataset is to be ascertained. This package implements the approach proposed by Jong et al., (2016) to address this question.

Victor L Jong

SPreFuGED

Selecting a Predictive Function for a Given Gene Expression Data

avAcc function

A data frame with 11520 observations on the following 8 variables.
  <dl> </dl> 

Format

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

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avAcc: Average accuracy data

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Description

Usage

Arguments

Format

Source

Details

Examples

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RADAR: AI Edition