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#' @export
#' @importFrom utils head tail
#' @importFrom graphics par axis title plot rect legend
#' @importFrom mhsmm simulate.hmmspec hmmspec dnorm.hsmm rnorm.hsmm
#' @importFrom PhysicalActivity dataCollapser
#' @importFrom Rcpp evalCpp
#' @useDynLib acc
simAcc <- function(timelength,paLevel='moderate',epoch="1 min",
startDate=ISOdate(2017,1,1,hour=0,min=0,sec=0,tz="GMT"),
endDate=ISOdate(2017,1,10,hour=24,min=0,sec=0,tz="GMT"),
mu = c(0, 30, 2500),
sigma = c(0, 30, 1000),
seedset=1234,tpm=NULL){
randomTime <- seq(startDate,endDate,epoch)
# User specified
if( is.null(paLevel)==TRUE ){
b <- list(mu = mu, sigma = sigma)
J <- length(mu); initial <- rep(1/J, J)
model <- hmmspec(init = initial, trans = tpm, parms.emission = b,dens.emission = dnorm.hsmm)
train <- simulate.hmmspec(model, nsim = (timelength), seed = seedset, rand.emission = rnorm.hsmm)
simdata <- data.frame(TimeStamp = randomTime[1:timelength], counts = round(train$x,0))
}
# Using pre set parameters
if( is.null(paLevel)==FALSE ){
mu = c(0, 30, 2500)
sigma = c(0, 30, 1000)
J <- 3; initial <- rep(1/J, J)
P <- matrix(rep(NA,9),byrow='TRUE',nrow=J)
if(paLevel=='low'){
P <- matrix(c(0.95, 0.04, 0.01,
0.09, 0.9, 0.01,
0.1, 0.2, 0.7), byrow='TRUE',nrow = J)
}
if(paLevel=='moderate'){
P <- matrix(c(0.95, 0.04, 0.01,
0.09, 0.8, 0.11,
0.1, 0.1, 0.8), byrow='TRUE',nrow = J)
}
if(paLevel=='high'){
P <- matrix(c(0.95, 0.04, 0.01,
0.09, 0.7, 0.21,
0.1, 0.1, 0.8), byrow='TRUE',nrow = J)
}
b <- list(mu = mu, sigma = sigma)
model <- hmmspec(init = initial, trans = P, parms.emission = b,dens.emission = dnorm.hsmm)
train <- simulate.hmmspec(model, nsim = (timelength), seed = seedset, rand.emission = rnorm.hsmm)
simdata <- data.frame(TimeStamp = randomTime[1:timelength], counts = round(train$x,0))
}
simdata
}
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