designSampleSizeClassificationPlots {MSstats} | R Documentation |
To illustrate the mean classification accuracy under different protein number and sample size. The input is the result from function designSampleSizeClassification
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designSampleSizeClassificationPlots(data)
data |
output from function |
Data in the example is based on the results of sample size calculation in classification problem from function designSampleSizeClassification
Plot for sample size estimation. x-axis : sample size, y-axis: mean predictive accuracy. Color: different protein number.
Ting Huang, Meena Choi, Olga Vitek.
Maintainer: Meena Choi (mnchoi67@gmail.com)
T. Huang et al. TBD 2018
# 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)