GENEAread (version 1.1.1)

epoch.apply: Compute epochal summary statistics.

Description

Computes epochal summary statistics for an "AccData" object, matrix, or vector, and collates into a matrix or vector.

Usage

epoch.apply(obj, epoch.size=10, incl.date = FALSE, FUN)
epoch.mean(obj, epoch.size=10, incl.date = FALSE, sqrt )
epoch.sd(obj, epoch.size=10, incl.date = FALSE, sqrt )
epoch.median(obj, epoch.size=10, incl.date = FALSE, sqrt )
epoch.mad(obj, epoch.size=10, incl.date = FALSE, sqrt )
epoch.autocor(obj, epoch.size=10, lag = 1, type =  c("correlation", "covariance", "partial"), incl.date = FALSE, sqrt)
epoch.quantile(obj, epoch.size = 10,  quantiles= c(0.1, 0.25, 0.5, 0.75, 0.9), incl.date = FALSE, sqrt )
svm(obj, sqrt )

Arguments

obj

The object to compute statistics for. Can be an "AccData" object, a matrix, or a vector.

epoch.size

Numeric giving intervals to consider and aggregate. For "AccData" obj taken as seconds. Otherwise, considered as rows, or as individual readings.

incl.date

logical. If TRUE, include a column of times or original indices with the results.

FUN

A function to be applied to each epoch.

sqrt

logical. If TRUE, the square rooted svm will be used in computations instead.

lag

Autocorrelation lag to compute.

type

Type of autocorrelation, as used in acf.

quantiles

Sample quantiles of SVM to compute.

Value

A vector or array giving the computed epochal summaries. With incl.date = TRUE, the result is given as a data.frame suitable for plotting.

Details

These functions compute epochal summary statistics for "AccData" objects, matrices and vectors.

epoch.apply is the general function - according to the size of epoch.size, it splits up the obj into collections of consecutive rows, each with the same size. These are then successively supplied to FUN as its first argument. If the result of FUN is a single value, then the results are concatenated into a vector output. Otherwise, an array is formed with each row corresponding to a single epochal group. For AccData, the sampling frequency of the dataset is used to interpret the epoch size in seconds. Otherwise, the raw record indices are used. If incl.date is set, the original timestamp vector of the data, or the original indices, are downsampled and included as the first column of the output.

The remaining functions are wrappers that compute various commonly useful statistics -- in particular, applied to "AccData" objects and arrays, they by default compute the epochal SVM mean, standard deviation, median, median absolute deviation, and autocorrelation, and sample quantiles respectively. (Arrays are treated as each column representing the x, y, and z components respectively.) Applied to vector input, processing will occur without the SVM calculation. This behaviour may be overridden by the sqrt setting, which will force the function to use the squared (default for arrays and "AccData") or original unit (default for vectors) values in the statistical analysis.

svm acts identically to 'epoch.mean', with the epoch set to the sampling period. In other words, it computes the instantaneous sum of vector magnitudes of the acceleration at each record point. The function takes "AccData", array and vector input. Note that if provided with an array with 4 or more columns, columns 2 to 4 are used -- the first column is regard as a timestamp and hence ignored.

See Also

plot.AccData, summary.AccData, aggregate, acf

Examples

dat <- read.bin(system.file("binfile/TESTfile.bin", package = "GENEAread")[1]
    , calibrate = TRUE)

#look for the epochs that exceed a certain threshold 50% of the time
plot(epoch.apply( dat, epoch.size = 3 , 
    FUN = function(t) mean(abs(svm(t) -1)>0.2)> 0.5 ), type = "l")

plot(dat[,1], svm(dat), log = "y", pch = ".")
lines(epoch.mean(dat, incl.date = TRUE), lwd = 2)
lines(epoch.mean(dat, epoch.size = 30, incl.date = TRUE), col = 2, lwd = 2)
#this should give all the same results, but by a different way
lines(epoch.apply(dat, epoch.size = 30, 
    FUN = function(A) mean(svm(A, FALSE)), incl.date = TRUE), col = 3)
epsize = 30; lines(epoch.apply(dat, epoch.size = epsize, 
    FUN = function(t) median(t[,1])), epoch.apply(dat, epoch.size = epsize, 
    FUN = function(A) mean(svm(A, FALSE))), col = 4)
#note this is different
lines(epoch.apply(dat, epoch.size = epsize, 
    FUN = function(t) median(t[,1])),epoch.apply(dat, epoch.size = epsize, 
    FUN = function(A) mean(svm(A, sqrt = TRUE)))^2, col = 5)

#plot some statistics
par(mfrow = c(5,1), mar = c(1,4.5,1,1))
plot(epoch.sd(dat), type="l")
plot(epoch.median(dat), type= "l")
plot(epoch.mad(dat), type= "l")
plot(epoch.autocor(dat), type= "l")
tmp = epoch.quantile(dat, quantiles= c(0.1, 0.25, 0.5, 0.75, 0.9)); matplot(tmp, type = "l")