corr.bestlag: Computes best lag correlation

In gitter-lab/LPC: Lag Penalized Weighted Correlation for Time Series Clustering

Description

This function computes correlation based on best picked lags. The lags indicate delayed changes.

Usage

corr.bestlag(data, timepoints, max.lag = NULL, C = NULL,
  penalty = "high", iter = 10)

Arguments

data	a matrix or data frame with rows representing genes and columns representing different timepoints. If data is a data frame, the gene names can be specified using the row.names().
timepoints	a vector of time points used in the dataset
max.lag	a integer value of the maximum lags allowed in the dataset, if null, defaults to the floor of the number of timepoints divided by 4
C	a numeric value of C used in computing weighted correlation, if null, a default is computed based on the penalty argument
penalty	a factor with two levels high and low penalty on the weighted correlation
iter	an integer indicating the number of C values to test for low penalty

Value

a list containing weighted correlation and best lags used in each row

Author(s)

Thevaa Chandereng, Anthony Gitter

Examples

corr.bestlag(array(rnorm(30), c(5, 6)), max.lag = 1,
          timepoints = c(0, 5, 10, 15, 20, 25), C = 10, penalty = "high")
corr.bestlag(array(runif(40, 0, 20), c(4, 10)),
          timepoints = c(0, 0.5, 1.5, 3, 6, 12, 18, 26, 39, 50), penalty = "high")
corr.bestlag(matrix(data = rexp(n = 40, 2), nrow = 8),
          timepoints = c(0, 5, 15, 20, 40), penalty = "low", iter = 5)

gitter-lab/LPC documentation built on May 2, 2020, 4:33 a.m.