LCA {LCA}

Localised Co-dependency Analysis

Description

Performs Localised Co-dependency Analysis

Usage

LCA(x,PTLmodel,clique,seed.row,combine.method="Fisher",
adjust.method="BH",comparison.alpha=0.05)

Arguments

x

Numeric data input array, standardised to range (0,1)

PTLmodel

List with named elements alpha, beta and gamma specifying PTL parameters

clique

Numeric vector specifying which columns of data table represent entities defining the clique across which to evaluate co-dependency

seed.row

Numeric value specifying which row of data table to use as 'seed' feature with which to evaluate co-dependency

combine.method

Character specifying which method to use for combining individual LCD estimates. One of "Fisher" or "Inverse Product".

adjust.method

Character specifying which method to use for multiple testing adjustment of significance estimates. See p.adjust for further details.

comparison.alpha

Significance level threshold for including objects in the set to be used for evaluating LCD significance estimates for a given pair of features in a given clique.

Details

Function to evaluate LCD, within the members of clique, for all features in a dataset against the feature represented by seed.row.

Value

List with elements:

LCD

Data frame giving across-clique LCD significance estimates for each feature in the dataset, as both unadjusted p-value and adjusted for multiple testing.

combinations

An array detailing the individual pair-wise LCD tests performed amongst members of the clique, which were combined to give the overall significance estimates

Author(s)

Ed Curry [e-mail]

Examples

suppressWarnings(RNGversion("3.5.0"))
## create a data matrix

x <- array(runif(1200),dim=c(40,12))

## implant similarity across a 'clique'

clique.cols <- sample(ncol(x),4)
x[,clique.cols] <- x[,clique.cols] + rnorm(nrow(x))

## scale x to (0,1)

x[x<0] <- 0
x[x>1] <- 1

## choose a 'seed' feature and some partner

seed.row <- sample(nrow(x),1)
partner.row <- sample(setdiff(c(1:nrow(x)),seed.row),1)

x[c(seed.row,partner.row),clique.cols] <- x[c(seed.row,partner.row),clique.cols] + 
rep(rnorm(length(clique.cols)),each=2)

## calibrate PTL models to dataset

PTL.fit <- fitPTLmodel(x,nPairs=15)

## evaluate LCD between 'seed' feature and all other features
 
LCA.result <- LCA(x,PTLmodel=PTL.fit,clique=clique.cols,seed.row=seed.row)
## Not run: head(LCA.result$LCD)