designSampleSizeClassificationPlots {MSstats}R Documentation

Visualization for sample size calculation in classification problem

Description

To illustrate the mean classification accuracy under different protein number and sample size. The input is the result from function designSampleSizeClassification.

Usage

designSampleSizeClassificationPlots(data)

Arguments

data

output from function designSampleSizeClassification

Details

Data in the example is based on the results of sample size calculation in classification problem from function designSampleSizeClassification

Value

Plot for sample size estimation. x-axis : sample size, y-axis: mean predictive accuracy. Color: different protein number.

Author(s)

Ting Huang, Meena Choi, Olga Vitek.

Maintainer: Meena Choi (mnchoi67@gmail.com)

References

T. Huang et al. TBD 2018

Examples

# Consider the training set from a colorectal cancer study
# Subjects are from control group or colorectal cancer group
# 72 proteins were targeted with SRM
require(MSstatsBioData)
set.seed(1235)
data(SRM_crc_training)
QuantCRCSRM <- dataProcess(SRM_crc_training, normalization = FALSE)
# estimate the mean predictive accuray under different sizes of training data
# n_sample is the number of different sample size to simulate
# Datasets with 10 different sample size and 3 different protein numbers are simulated 
result.crc.srm <- designSampleSizeClassification(data=QuantCRCSRM, 
n_sample = 10, 
sample_incr = 10, 
protein_desc = 0.33, 
iter = 50)
designSampleSizeClassificationPlots(data=result.crc.srm)

[Package MSstats version 3.12.3 Index]