ActCR (version 0.3.0)

ActCosinor_long: Cosinor Model for Circadian Rhythmicity for the Whole Dataset

Description

A parametric approach to study circadian rhythmicity assuming cosinor shape.This function is a whole dataset wrapper for ActCosinor.

Usage

ActCosinor_long(count.data, window = 1, export_ts = FALSE)

Arguments

count.data

data.frame of dimension n * (p+2) containing the p dimensional activity data for all n subject days. The first two columns have to be ID and Day. ID can be either character or numeric. Day has to be numeric indicating the sequence of days within each subject.

window

The calculation needs the window size of the data. E.g window = 1 means each epoch is in one-minute window.

export_ts

A Boolean to indicate whether time series should be exported (notice: it takes time and storage space to export time series data for all subject-days. Use this with caution. Suggest to only export time series for selected subjects)

Value

A data.frame with the following 5 columns

ID

ID

ndays

number of days

mes

MESRO, which is short for midline statistics of rhythm, which is a rhythm adjusted mean. This represents mean activity level.

amp

amplitude, a measure of half the extend of predictable variation within a cycle. This represents the highest activity one can achieve.

acro

acrophase, a meaure of the time of the overall high values recurring in each cycle. Here it has a unit of radian. This represents time to reach the peak.

acrotime

acrophase in the unit of the time (hours)

ndays

Number of days modeled

and

cosinor_ts

Exported data frame with time, time over days, original time series, fitted time series using cosinor model

Examples

# NOT RUN {
counts_1 = example_activity_data$count[c(1:12),]
cos_all_1 = ActCosinor_long(count.data = counts_1, window = 1,export_ts = TRUE)
counts_10 = cbind(counts_1[,1:2],
as.data.frame(t(apply(counts_1[,-c(1:2)], 1,
FUN = bin_data, window = 10, method = "average"))))
cos_all_10 = ActCosinor_long(count.data = counts_10, window = 10)
# }