plotAcc | R Documentation |
Plots accelerometer data. This function receives summary object from function accsummary.
plotAcc(object,markbouts)
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. |
A plot is returned.
Jaejoon Song <jjsong2@mdanderson.org>
## 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)
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