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sojournEstimation.R
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sojourn <- function(counts,perc.cut,perc.cut.2,perc.cut.3,too.short,sit.cut,long.soj)
{
y <- counts
# identify sojourns.
inds <- 1:length(y)
mmm <- length(y)
one <- y[-mmm]
two <- y[-1]
# transitions from 0 to >0
trans.up <- (one==0)&(two>0)
# transitions from >0 to 0
trans.down <- (one>0)&(two==0)
trans <- c(0,trans.up+trans.down)
trans.inds <- (1:mmm)[trans==1]
# indices where transitions take place
trans.inds <- c(1,trans.inds,(mmm+1))
# how long are the sojourns and the zeros
durations <- trans.inds[-1]-trans.inds[-length(trans.inds)]
# identify if interval is zeros or >0s (they alternate)
type <- rep("zeros",length=length(durations))
if (y[1]==0)
type <- rep(c("zeros","act"),length=length(durations))
if (y[1]>0)
type <- rep(c("act","zeros"),length=length(durations))
soj.table <- data.frame(type,durations,trans.inds=trans.inds[-length(trans.inds)])
soj.table$act.type.1 <- "undetermined"
soj.table$act.type.1[(soj.table$type=="zeros")&(soj.table$durations>sit.cut)] <- "sedentary"
soj.table$act.type.1[(soj.table$type=="act")&(soj.table$durations>too.short)] <- "activity"
# combine neighboring undetermineds
mmm <- dim(soj.table)[1]
prev.was.undet.inds <-
(2:mmm)[(soj.table$act.type.1[2:mmm]=="undetermined")&
(soj.table$act.type.1[1:(mmm-1)]=="undetermined")]
if (length(prev.was.undet.inds)>0)
rev.soj.table <- soj.table[-prev.was.undet.inds,]
mmm <- dim(rev.soj.table)[1]
rev.soj.table$durations <-
c((rev.soj.table$trans.inds[-1]-
rev.soj.table$trans.inds[-mmm]),
rev.soj.table$durations[mmm])
mmm <- dim(rev.soj.table)[1]
# find too short undetermineds
too.short.undet.inds <- (1:mmm)[(rev.soj.table$durations<too.short)&(rev.soj.table$act.type.1=="undetermined")]
if (length(too.short.undet.inds)>0)
{
if (too.short.undet.inds[1]==1)
{
too.short.undet.inds <- too.short.undet.inds[-1]
rev.soj.table <- rev.soj.table[-1,]
rev.soj.table$trans.inds[1] <- 1
mmm <- dim(rev.soj.table)[1]
}
last <- length(too.short.undet.inds)
if (too.short.undet.inds[last]==mmm)
{
too.short.undet.inds <- too.short.undet.inds[-last]
junk <- rev.soj.table$durations[(mmm-1)]
rev.soj.table <- rev.soj.table[-mmm,]
mmm <- dim(rev.soj.table)[1]
rev.soj.table$durations[mmm] <- junk+rev.soj.table$durations[mmm]
}
# short undetermineds between two acts of same type
to.delete.inds <-
(too.short.undet.inds)[rev.soj.table$act.type.1[too.short.undet.inds-1]==rev.soj.table$act.type.1[too.short.undet.inds+1]]
done.inds <- (1:length(too.short.undet.inds))[rev.soj.table$act.type.1[too.short.undet.inds-1]==rev.soj.table$act.type.1[too.short.undet.inds+1]]
too.short.undet.inds <- too.short.undet.inds[-done.inds]
# between two acts of different types
junk <- rev.soj.table[too.short.undet.inds,]
junk$act.type.1 <- "sedentary"
junk$act.type.1[junk$type=="act"] <- "activity"
rev.soj.table[too.short.undet.inds,] <- junk
rev.soj.table <- rev.soj.table[-to.delete.inds,]
}
mmm <- dim(rev.soj.table)[1]
junk <- c(rev.soj.table$act.type.1[2:mmm]==rev.soj.table$act.type.1[1:(mmm-1)])
same.as.prev.inds <- (2:mmm)[junk]
if (length(same.as.prev.inds)>0)
{
rev.soj.table <- rev.soj.table[-same.as.prev.inds,]
mmm <- dim(rev.soj.table)[1]
rev.soj.table$durations <-
c((rev.soj.table$trans.inds[-1]-
rev.soj.table$trans.inds[-mmm]),
rev.soj.table$durations[mmm])
last.obs <- rev.soj.table$durations[mmm]-1+rev.soj.table$trans.inds[mmm]
if (last.obs != length(y))
rev.soj.table$durations[mmm] <- length(y)-rev.soj.table$trans.inds[mmm]+1
}
trans.inds <- c(rev.soj.table$trans.inds,length(y)+1)
durations <- trans.inds[-1]-trans.inds[-length(trans.inds)]
soj.table <- data.frame(durations)
sojourns <- rep(1:length(soj.table$durations),soj.table$durations)
perc.gt.0 <- tapply(y>0,sojourns,mean)
soj.table$perc.gt.0 <- perc.gt.0
soj.table$revised.type <- "sedentary"
soj.table$revised.type[soj.table$perc.gt.0>perc.cut.3] <- "activity"
soj.table$revised.type[(soj.table$perc.gt.0>perc.cut)&(soj.table$perc.gt.0<=perc.cut.2)&(soj.table$durations>sit.cut)] <- "sedentary2"
soj.table$revised.type[(soj.table$perc.gt.0>perc.cut)&(soj.table$perc.gt.0<=perc.cut.2)&(soj.table$durations<=sit.cut)] <- "sedentary3"
soj.table$revised.type[(soj.table$perc.gt.0>perc.cut.2)&(soj.table$perc.gt.0<=perc.cut.3)] <- "sedentary4"
durations <- soj.table$durations
type <- soj.table$revised.type
sojourns <- rep(1:length(durations),durations)
type <- rep(type,durations)
perc.gt.0 <- rep(perc.gt.0,durations)
durations <- rep(durations,durations)
nnn <- length(sojourns)
longer.acts <- unique(sojourns[(durations>(long.soj-1))])
f <- function(s)
{
dur <- unique(durations[sojourns==s])
sub.sojourns <- rep(1:floor(dur/(long.soj/2)),
times=c(rep((long.soj/2),floor(dur/(long.soj/2))-1),
dur-(floor(dur/(long.soj/2))-1)*(long.soj/2)))
sub.sojourns <- s + sub.sojourns/(max(sub.sojourns)+1)
return(sub.sojourns)
}
new.values <- sapply(longer.acts,f)
starts <- sapply(match(longer.acts,sojourns),paste,":",sep="")
ends <- length(sojourns) - match(longer.acts,rev(sojourns)) + 1
indices <- mapply(paste,starts,ends,MoreArgs=list(sep=""),USE.NAMES=F)
indices <- unlist(lapply(parse(text = indices), eval))
sojourns[indices] <- unlist(new.values)
# apply METs to zeros
METs <- rep(NA,length(type))
METs[(type=="sedentary")] <- 1
METs[(type=="sedentary2")] <- 1.2
METs[(type=="sedentary3")] <- 1.5
METs[(type=="sedentary4")] <- 1.7
data <- data.frame(counts=y,sojourns=sojourns,durations=durations,type=type,METs=METs,perc.gt.0=perc.gt.0)
# prepare to apply nnet to the activity sojourns
nnn <- dim(data)[1]
act.inds <- (1:nnn)[(data$type=="activity")]
act.data <- data[act.inds,]
act.durations <- table(act.data$sojourns)
quantiles <- tapply(act.data$counts,act.data$sojourns,quantile,p=c(.1,.25,.5,.75,.9))
nn.data <- as.data.frame(do.call("rbind",quantiles))
nn.data$acf <- tapply(act.data$counts,act.data$sojourns,acf.lag1)
nn.data <- nn.data[,c(1:6)]
names(nn.data) <- c("X10.","X25.","X50.","X75.","X90.","acf")
nnetinputs <- scale(nn.data,center=cent,scale=scal)
# apply nnet and put it back into the dataset
est.mets.1 <- NA #predict(MA.reg.nn,nnetinputs)
est.mets.2 <- predict(ALL.reg.nn,nnetinputs)
act.mets.1 <- NA #rep(est.mets.1,act.durations)
act.mets.2 <- rep(est.mets.2,act.durations)
data$METs <- METs
data$METs.2 <- METs
data$METs[act.inds] <- act.mets.1
data$METs.2[act.inds] <- act.mets.2
data$level <- "sed"
data$level[data$METs>=1.5] <- "light"
data$level[data$METs>=3] <- "mod"
data$level[data$METs>=6] <- "vig"
data$level <- factor(data$level,levels=c("sed","light","mod","vig"))
data$level.2 <- "sed"
data$level.2[data$METs.2>=1.5] <- "light"
data$level.2[data$METs.2>=3] <- "mod"
data$level.2[data$METs.2>=6] <- "vig"
data$level.2 <- factor(data$level.2,levels=c("sed","light","mod","vig"))
data
}