IPCA: Principal Components Analysis for distributions of variations...

In gaiac/TFARM: Transcription Factors Association Rules Miner

Description

The function is used for validation of the defined Importance Index. It is defined as the linear combination of variations of support and confidence measures. The Principal Component Analysis lets the user evaluate if in the 1D reference system defined by such linear combination, it is possible to describe the variability of the data.

Usage

IPCA(delta_list, IMP)

Arguments

delta_list	list of variations of distributions of support and confidence measures, obtained using the IComp.
IMP	the importance matrix with the mean Importance Index of every candidate co-regulator transcription factor and the number of rules in which each of them appears.

Value

Variance explained by every principal component (summary), scores (i.e., the coordinates) of data in delta_list in the reference system defined by the principal components (scores) and loadings (i.e., the coefficinets) of the linear combination that defines each principal component (loadings). The plots of the variability, the cumulate percentage of variance explained by each principal component and loadings of every principal component are also returned.

Examples

# Load IMP, DELTA and TF_Imp from the data_man collection of datasets:
data('data_man')

colnames(IMP)
TF_Imp <- data.frame(IMP$TF, IMP$imp, IMP$nrules)
i.pc <- IPCA(DELTA, TF_Imp)
names(i.pc)

gaiac/TFARM documentation built on June 24, 2019, 5:43 p.m.