acc (version 1.3.3)

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

## Not run: 
# ##
# ## Example: Simulate a dataset for two days, for an individual with low MVPA level.
# ##
# mvpaLowData <- simAcc(timelength=(60*24*2),paLevel='low')
# summary <- accSummary(data=mvpaLowData)
# summary$validDates
# plotAcc(summary,markbouts='FALSE')
# 
# ##
# ## Example: Simulate a dataset for two days, for an individual with moderate MVPA level.
# ##
# mvpaModData <- simAcc(timelength=(60*24*2),paLevel='moderate')
# summary <- accSummary(data=mvpaModData, tri='FALSE', axis=NULL,
#              spuriousDef=20, nonwearDef=60, minWear=600, 
#              patype='MVPA',pacut=c(1952,Inf), boutsize=10, 
#              tolerance='TRUE', returnbout='TRUE')
# summary$validDates
# plotAcc(summary,markbouts='FALSE')
# 
# ##
# ## Example: Simulate a dataset for two days, for an individual with high MVPA level.
# ##
# mvpaHighData <- simAcc(timelength=(60*24*2),paLevel='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')
# ## End(Not run)