dat <- subset(HART6 , days_intersect >0)

#### begin the booty

bootiter = 3000 # no. of bootstrap iterations

# empty matrix of resampling scheme to select the observations that will be bootied
bootmatrix=matrix(0,bootiter,length(dat $CARIBOU_ID ))  ## 

# loop for the resampling scheme, essentially selecting observation numbers
for (i in 1:bootiter) {
	bootmatrix[i,] = sample(1:length(dat $CARIBOU_ID),replace=TRUE)  
#DON'T FORGET TO CHANGE MOOSE_ID TO CARIBOU_ID & VICE VERSA
}

iterdata<-data.frame()

df1 <-  data.frame()

for (j in 1:bootiter) {

	iterdata<-data.frame(dat$CARIBOU_ID[bootmatrix[j,]], dat$days_intersect[bootmatrix[j,]],
		dat$died_in_range[bootmatrix[j,]], dat$days_intersect_5_10[bootmatrix[j,]],
		(dat$days_intersect_11_4[bootmatrix[j,]]),(dat$died_in_range_5_10[bootmatrix[j,]]),(dat$died_in_range_11_4[bootmatrix[j,]]))
	names(iterdata) <- c("ID","days_intersect","died_in_range","days_intersect_5_10","days_intersect_11_4",
		"died_in_range_5_10","died_in_range_11_4") # 



# need to add annual here with appropriate vars above

# 1 ANNUAL RISK PERIOD

days_surv_rate <- 1- sum(iterdata$died_in_range)/sum(iterdata$days_intersect) 
sum(iterdata$days_intersect)
sum(iterdata$died_in_range)
annual <- days_surv_rate ^ 364.25
annual
nobou <- length(iterdata$days_intersect[iterdata$days_intersect>=1]) # no. bou collared
nobou 



# split by the 2 RISK periods (SUMMER AND WINTER)


days_surv_rateS <- 1- sum(iterdata$died_in_range_5_10)/sum(iterdata$days_intersect_5_10)  # S means summer
sum(iterdata$days_intersect_5_10)
sum(iterdata$died_in_range_5_10)
S <- days_surv_rateS ^ 184
S
nobou <- length(iterdata$days_intersect[iterdata$days_intersect>=1]) # no. bou collared
nobou 

# W means winter

days_surv_rateW <- 1- sum(iterdata$died_in_range_11_4)/sum(iterdata$days_intersect_11_4)  # W means winter
sum(iterdata$days_intersect_11_4)
sum(iterdata$died_in_range_11_4)
W <- days_surv_rateW ^ 181.25
W

annual_2per <- W*S
annual_2per 
annual

df <- data.frame(as1per = annual, as2per = annual_2per)

df1 <- rbind(df1,df)

}

######

#df1

#write.csv(df1,"moose_COL_2per.csv")

mean_ann1per <- mean(df1$as1per)

mean_ann2per <- mean(df1$as2per)

perc1 <- c(quantile(df1$as1per,probs = c(0.025,0.5,0.975)))

perc2 <- c(quantile(df1$as2per,probs = c(0.025,0.5,0.975))) # uses the percentile method to produce


summary.1per<- c(perc1,mean_ann1per,herd=paste(HART6$SUBPOPULATION[1]))
summary.2per<- c(perc2,mean_ann2per,nobou,sum(HART6$days_intersect)/364.25)

summary.2per     ###### THESE ARE THE FINAL SURVIVAL VALUES

sum(HART6$days_intersect)/364.25
nobou

summary.1per  ## 1 RISK PERIOD, NOT USED, USE THE 2 RISK PERIODS