library(lme4)
library(lmerTest)

setwd("\caribou_planning\\journal papers\\Chapter 6 a PeerJ")

RECRUIT <- read.csv("\caribou_planning\\journal papers\\Chapter 6 a PeerJ\\recruitment.csv")

RECRUIT$TIME <- "BEFORE"
RECRUIT$TIME[RECRUIT$Year>=2004]<-'AFTER'        # SETS 2004 AS THE AFTER YEARS
RECRUIT$R.P <- RECRUIT$R/100
RECRUIT$R.P[RECRUIT$R.P == 0]<-  0.01
RECRUIT$R.LN  <- log(RECRUIT$R.P / (1 - RECRUIT$R.P))  # THE LOGIT TRANSFORMATION
 



### STATISTIC FOR THE TREATMENT DATA
RECRUIT.TREAT <- subset (RECRUIT, Unit == "A) Treatment")


mod2 <- lmer(R.LN  ~  TIME +(1|Subpopulation), 
	weights = Popsize,                            # WEIGHT ANALYSIS BY POPULATION SIZE
	data = RECRUIT.TREAT)  
	


summary(mod2)# gives the output
anova(mod2)

# BACKTRANSFORM THE LOGIT COEFFICIENTS FOR NORMAL PREDICTIONS
al <- -2.0432 + 0.2531
1 / (1+ exp(-al))
bet<- -2.0432
1 / (1+ exp(-bet))


### STATISTIC FOR THE REFERENCE DATA
RECRUIT.REF <- subset (RECRUIT, Unit == "B) Reference")


mod3 <- lmer(R.LN  ~  TIME +(1|Subpopulation), 
	weights = Popsize,                               # WEIGHT ANALYSIS BY POPULATION SIZE
	data = RECRUIT.REF)  
	


summary(mod3)# gives the output
anova(mod3)