acc (version 1.1.7)

plotAcc: Plots accelerometer data

Description

Plots accelerometer data. This function receives summary object from function accsummary.

Usage

plotAcc(object,markbouts)

Arguments

object

An object returned from either the function accsummary.

markbouts

Whether to mark bouts. If markbout='TRUE' a bar along the time axis will indicate whether the epoch was counted as in bout or not. Default is false.

Value

• A plot is returned.

Examples

##
## Example: Simulate a dataset for two days, for an individual with low MVPA level.
##
mvpaLowData <- simAcc(minutes=(60*24*2),mvpaLevel='low')
summary <- accSummary(data=mvpaLowData)
summary$validDates
plotAcc(summary,markbouts='FALSE')

##
## Example: Simulate a dataset for two days, for an individual with moderate MVPA level.
##
mvpaModData <- simAcc(minutes=(60*24*2),mvpaLevel='moderate')
summary <- accSummary(data=mvpaModData, tri='FALSE', axis=NULL,
             spuriousDef=20, nonwearDef=60, minWear=600, 
             patype='MVPA',pacut=c(1952,Inf), boutsize=10, 
             tolerance='TRUE', returnbout='TRUE')
summary$validDates
plotAcc(summary,markbouts='FALSE')

##
## Example: Simulate a dataset for two days, for an individual with high MVPA level.
##
mvpaHighData <- simAcc(minutes=(60*24*2),mvpaLevel='high')
summary <- accSummary(data=mvpaHighData, tri='FALSE', axis=NULL,
             spuriousDef=20, nonwearDef=60, minWear=600, 
             patype='MVPA',pacut=c(1952,Inf), boutsize=10, 
             tolerance='TRUE', returnbout='TRUE')
summary$validDates

plotAcc(summary,markbouts='FALSE')


##
## Example: Simulate a tri-axial dataset for five days.
##
library(acc)
  library(mhsmm)
  seedset=1234
  minutes=(60*24*5)
  randomTime <- seq(ISOdate(2015,1,1),ISOdate(2020,1,1),"min")
  J <- 3; initial <- rep(1/J, J)
  P <- matrix(rep(NA,9),byrow='TRUE',nrow=J)

  P1 <- matrix(c(0.95, 0.04, 0.01, 
                  0.09, 0.9, 0.01, 
                  0.1, 0.2, 0.7), byrow='TRUE',nrow = J)

  b <- list(mu = c(0, 30, 2500), sigma = c(0, 30, 1000))
  model1 <- hmmspec(init = initial, trans = P1, parms.emis = b,dens.emis = dnorm.hsmm)
  x <- simulate.hmmspec(model1, nsim = (minutes), seed = seedset, rand.emis = rnorm.hsmm)

  seedset=12345
  P2 <- matrix(c(0.95, 0.04, 0.01, 
                  0.09, 0.8, 0.11, 
                  0.1, 0.1, 0.8), byrow='TRUE',nrow = J)
  model2 <- hmmspec(init = initial, trans = P2, parms.emis = b,dens.emis = dnorm.hsmm)
  y <- simulate.hmmspec(model2, nsim = (minutes), seed = seedset, rand.emis = rnorm.hsmm)

  seedset=123456
  P3 <- matrix(c(0.95, 0.04, 0.01, 
                  0.09, 0.8, 0.11, 
                  0.1, 0.1, 0.8), byrow='TRUE',nrow = J)
  model3 <- hmmspec(init = initial, trans = P3, parms.emis = b,dens.emis = dnorm.hsmm)
  z <- simulate.hmmspec(model3, nsim = (minutes), seed = seedset, rand.emis = rnorm.hsmm)

  counts <- data.frame(TimeStamp = randomTime[1:minutes], x=x$x, y=y$x, z=z$x)
  summary <- accSummary(data=counts, tri='TRUE', axis='vm',
                        spuriousDef=20, nonwearDef=60, minWear=600, 
                        patype='MVPA',pacut=c(1952,Inf), boutsize=10, tolerance='TRUE',
                        returnbout='TRUE')
summary$validDates

plotAcc(summary,markbouts='FALSE')