DODR (version 0.99.2)

dodr: Detection of differences in rhythmic behavior between two time series sets

Description

Detection of differences in rhythmic behavior between two time series sets

Usage

dodr(val1, val2, times1, times2 = times1, norm = TRUE, period = 24, method = "robust", verbose = options("verbose")[[1]])

Arguments

val1

matrix: data for values in first set. One column per sample, one row per time point

val2

matrix: data for values in second set. One column per sample, one row per time point

times1

vector: times of first set.

times2

vector: times of second set.

norm

boolean: whether to normalize the time series (division by mean), prior to the analysis.

period

numeric: period of the oscillations. Same unit as the time points in times1 and times2

method

vector: method(s) to detect differences. Groups of related methods have additional identifiers. Elementary methods: HANOVA, harmScaleTest, harmNoisePred1, harmNoisePred2, robustDODR, robustHarmScaleTest) Groups:

• "all"All methods
• "robust"Combination of robust detection methods (robustDODR, robustHarmScaleTest)
• "lsq"Combination of least square based detetion methods (HANOVA, HarmScaleTest, HarmNoisePred1, HarmNoisePred2)
• "ANOVA"Combination of ANOVA like methods (HANOVA, robustDODR)
• "scaleTest"Combination of scaleTest methods (harmScaleTest, robustHarmScaleTest)
• "harmNoisePred"combination of both scaleTest methods to generate a two sided test

verbose

boolean: verbosity.

Value

A list containing

• 'p.value.table' A table containing the p-values for all the tests specified by method. Each row contains the results for one sample. A column meta.p.val is added containing the lowest p-value, corrected for multiple testing using a beta-distribution based aproach.
• details A list containing the detailed results from the selected methods

Details

This method applies a set of different methods on a pair of two experiments with one measurement matrix each. Samples to compare have to have the same column in both matrices. Different methods could be selected.

Examples

library(DODR)

#defining the parameters for two sets of oscillations
n=50
testTimes1 <- 0:15*3
testTimes2 <- testTimes1
tp <- length(testTimes1)
per1 <- 24
amp1 <- 0.3
ph1 <- 5
sd1 <- 0.1

per2 <- per1
amp2 <- amp1
ph2 <- ph1+4
sd2 <- sd1

#creating artificial oscillation sets
v1 <- 1 + amp1 * cos((testTimes1 - ph1)/per1*2*pi)
noise1 <- rnorm(length(testTimes1)*n, 0, sd1)
val1 <- matrix(v1 + noise1, ncol=n)

v2 <- 1 + amp2 * cos((testTimes2 - ph2)/per2*2*pi)
noise2 <- rnorm(length(testTimes2)*n, 0, sd2)
val2 <- matrix(v2 + noise2, ncol=n)

# run DODR
dodr <- dodr(val1, val2, testTimes1, testTimes2, 24, method = 'all')
dodr$p.value.table[1:3,]

#create another set with alterations in noise scale
ph2 <- ph1
sd2 <- sd1 * 3

v2 <- 1 + amp2 * cos((testTimes2 - ph2)/per2*2*pi)
noise2 <- rnorm(length(testTimes2)*n, 0, sd2)
val2 <- matrix(v2 + noise2, ncol=n)

dodr <- dodr(val1, val2, testTimes1, testTimes2, 24, method = 'all')
dodr$p.value.table[1:3,]