acc (version 1.2.4)

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
## Not run: 
# plotAcc(summary,markbouts='FALSE')
# ## End(Not run)

##
## Example: Simulate a dataset for two days, for an individual with high MVPA level.
##
## Not run: 
# 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')
# ## End(Not run)


##
## Example: Simulate a tri-axial dataset for five days.
##
## Not run: 
#   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)