Package {LPWC}

Version:	1.0.1
Title:	Lag Penalized Weighted Correlation for Time Series Clustering
Description:	Computes a time series distance measure for clustering based on weighted correlation and introduction of lags. The lags capture delayed responses in a time series dataset. The timepoints must be specified. T. Chandereng, A. Gitter (2020) <doi:10.1186/s12859-019-3324-1>.
Depends:	R (≥ 3.0.2)
Suggests:	testthat, rmarkdown, pkgdown, ggplot2, knitr, devtools
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	6.1.1
VignetteBuilder:	knitr
URL:	https://github.com/gitter-lab/LPWC
BugReports:	https://github.com/gitter-lab/LPWC/issues
Imports:	nleqslv
NeedsCompilation:	yes
Packaged:	2026-06-30 05:38:27 UTC; thevaasiinenchandereng
Author:	Thevaa Chandereng [aut, cre, cph], Anthony Gitter [aut, cph]
Maintainer:	Thevaa Chandereng <thevaasiinen@gmail.com>
Repository:	CRAN
Date/Publication:	2026-07-05 10:20:03 UTC

Best Lag

Description

best.lag computes the best lags for a dataset using weighted correlation. The lags obtained are in reference to the original timepoints.

Usage

best.lag(data, timepoints, max.lag = NULL, C)

Arguments

Value

a vector of best lags used in the dataset, one per gene

Author(s)

Thevaa Chandereng, Anthony Gitter

Examples

best.lag(data = array(rnorm(20), c(4, 5)), timepoints = c(0, 5, 10, 20, 40), C = 300)
best.lag(data = array(runif(100), c(5, 20)), timepoints = seq(2, 40, 2), C = 10)
best.lag(data = array(runif(100), c(5, 20)), timepoints = seq(2, 40, 2), max.lag = 2, C = 10)

Computing corr

Description

This function computes the weighted correlation with a penalty for lags. It should only be used after the fixed lags have already been applied to the dataset and timepoints using the functions prep.data() and best.lag().

Usage

comp.corr(data, time, C)

Arguments

Value

a simmilarity matrix with values between -1 and 1 (1 highly correlated, 0 no correlation)

Author(s)

Thevaa Chandereng, Anthony Gitter

Examples

## This function computes the correlation after the lags (or shifts) have
## been computed.  In this example, the lags argument is randomly sampled
## for the sake of illustrating how prep.data() applies the lags and
## prepares a transformed dataset for comp.corr().
lagged <- prep.data(array(rnorm(30), c(3, 10)), timepoints = seq(0, 45, 5),
          lags = sample(c(0, 1, -1, 2, -2), size = 3))
comp.corr(data = lagged$data, time = lagged$time, C = 10)

## This example shows how comp.corr is used in practice with real data.
## The best.lag() function is called first to pre-compute the lags, which
## are passed to prep.data().
randdata <- array(rnorm(120), c(10, 12))
bl <- best.lag(data = randdata, timepoints = 1:12, C = 5)
lag.data <- prep.data(randdata, timepoints = 1:12, lags = bl)
comp.corr(lag.data$data, time = lag.data$time, C = 5)

Computes best lag correlation

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

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)

Finding best C

Description

This function computes the values of C to test using the timepoints and max lag in the dataset

Usage

findC(timepoints, max.lag = NULL, pi = 0.95, iter = 10)

Arguments

Value

a vector of length iter of the different values of C to test

Author(s)

Thevaa Chandereng, Anthony Gitter

Examples

findC(c(0, 5, 10, 15, 20, 25), max.lag = 1, iter = 15)
findC(c(2, 4, 8, 16, 32, 64, 128, 256), iter = 5)
findC(c(2, 6, 10, 15, 22, 30, 40, 55, 80), pi = 0.8, iter = 20)
findC(c(1, 2, 3.2, 4, 5.3, 7), pi = 0.99)

Preparing Data

Description

This function prepares the data to compute correlation by introducing NA's when lags are needed

Usage

prep.data(data, lags, timepoints)

Arguments

Value

a list of two matrices, one matrix with NA's for the lags for the dataset and another matrix with the timepoints used for each row in the dataset

Author(s)

Thevaa Chandereng, Anthony Gitter

Examples

prep.data(array(rnorm(20), c(5, 4)), c(0, 0, 0, -1, 1),
          timepoints = c(0, 5, 15, 30))
prep.data(array(runif(100, 0, 10), c(10, 10)), sample((-2:2), size = 10, replace = TRUE),
          timepoints = c(0, 5, 15, 30, 45, 60, 75, 80, 100, 120))

Score of Lags

Description

This function computes the score of best lags by summing the correlation of corresponding lags

Usage

score(corr, lags)

Arguments

Value

a numerical value of best lag picked based on corr and lags

Author(s)

Thevaa Chandereng, Anthony Gitter

Examples

score(runif(10, 0, 1), c(2, 0, 0, 0, 3, 2, -1, 2, 0, 1))
score(runif(20, 0.5, 0.8), sample(-3:3, size = 20, replace = TRUE))

Example datasets for LPC

Description

Data is simulated from ImpulseDE function with 8 timepoints c(0, 2, 4, 6, 8, 18, 24, 32, 48, 72) See https://doi.org/10.1093/bioinformatics/btw665

Usage

simdata

Format

data matrix

Examples

data(simdata)

Weight in correlation

Description

This function computes the weight used for correlation using timepoints used and lags used

Usage

weight(t, lag, C)

Arguments

Value

a list containing w0 and vector w used for computing weighted correlation

Author(s)

Thevaa Chandereng, Anthony Gitter

Examples

weight(t = c(0, 5, 10, 15, 20), lag = 1, C = 20)
weight(t = c(0, 2, 5, 10, 14, 19, 22), lag = 1,C = 100)

Weight Lag

Description

This function matches vectors of two different lengths

Usage

weight.lag(x1, x2)

Arguments

Value

a matrix with two rows with the shortest length of the vector as the number of columns

Author(s)

Thevaa Chandereng, Anthony Gitter

Examples

weight.lag(1:5, 2:9)
weight.lag (seq(0, 10, 2), seq(4, 10, 2))

Weighted correlation

Description

This function computes weighted Pearson correlation between two vectors with weights given. The output is between -1 and 1 with 1 being highly positively correlated, -1 being highly negatively correlated, and 0 being no correlation

Usage

wt.corr(x, y, w)

Arguments

Value

a numerical value of weighted Pearson correlation

Source

https://en.wikipedia.org/wiki/Pearson_correlation_coefficient#Weighted_correlation_coefficient

Examples

wt.corr(c(1, 2, -9, 4, 5), c(2:6), c(0.5, 1, 2, 0.5, 2))
wt.corr(rnorm(5), rnorm(5), runif(5, 0, 1))