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Detection_Effects.R
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EffectsDetection<-function(P_effects, b)
{
# various quantities that will be useful later on
if(P_effects=="cst detection")
{
pB <- 1/(1+exp(-(b[16])))
pAb <- 1/(1+exp(-(b[16])))
pAB <- 1/(1+exp(-(b[16])))
B <- matrix(c(
1,0,0,0,
1-pAb,pAb,0,0,
1-pB,0,pB,0,
1-pAB,0,0,pAB),
nrow = n.states)
npar<-16 # Define the number of regression coefficient for this effect
}
else if(P_effects=="no prey effects on detection of poachers")
{
pB <- 1/(1+exp(-(b[16])))
pAb <- 1/(1+exp(-(b[17])))
pAB <- 1/(1+exp(-(b[17])))
B <- matrix(c(
1,0,0,0,
1-pAb,pAb,0,0,
1-pB,0,pB,0,
1-pAB,0,0,pAB),
nrow = n.states)
npar<-17 # Define the number of regression coefficient for this effect
}
else if(P_effects=="no poacher effects on detection of preys")
{
pB <- 1/(1+exp(-(b[16])))
pAb <- 1/(1+exp(-(b[17])))
pAB <- 1/(1+exp(-(b[16])))
B <- matrix(c(
1,0,0,0,
1-pAb,pAb,0,0,
1-pB,0,pB,0,
1-pAB,0,0,pAB),
nrow = n.states)
npar<-17 # Define the number of regression coefficient for this effect
}
else if(P_effects=="species interaction effects on detection")
{
pB <- 1/(1+exp(-(b[16])))
pAb <- 1/(1+exp(-(b[17])))
pAB <- 1/(1+exp(-(b[18])))
B <- matrix(c(
1,0,0,0,
1-pAb,pAb,0,0,
1-pB,0,pB,0,
1-pAB,0,0,pAB),
nrow = n.states)
npar<-18 # Define the number of regression coefficient for this effect
}
else if(P_effects=="spatial effect of patroling effort on detection")
{
pB <-vector(mode="logical", length=R)
pAb <-vector(mode="logical", length=R)
pAB <-vector(mode="logical", length=R)
B <-array(0, dim=c(n.states,n.states,R))
for(s in 1:R)
{
pB[s] <- 1/(1+exp(-(b[16] + b[19]*mean(tempcov[s,]))))
pAb[s] <- 1/(1+exp(-(b[17] + b[20]*mean(tempcov[s,]))))
pAB[s] <- 1/(1+exp(-(b[18] + b[21]*mean(tempcov[s,]))))
B[,,s] <- matrix(c(1,0,0,0,
1-pAb[s],pAb[s],0,0,
1-pB[s],0,pB[s],0,
1-pAB[s],0,0,pAB[s]),
nrow = n.states)
}
npar<-21 # Define the number of regression coefficient for this effect
} else if(P_effects=="time effect of patroling effort on detection")
{
pB <-vector(mode="logical", length=N)
pAb <-vector(mode="logical", length=N)
pAB <-vector(mode="logical", length=N)
# obs prob
B <-array(0, dim=c(n.states,n.states,N))
for(n in 1:N)
{
pB[n] <- 1/(1+exp(-(b[16] + b[19]*mean(tempcov[,n]))))
pAb[n] <- 1/(1+exp(-(b[17] + b[20]*mean(tempcov[,n]))))
pAB[n] <- 1/(1+exp(-(b[18] + b[21]*mean(tempcov[,n]))))
B[,,n] <- matrix(c(
1,0,0,0,
1-pAb[n],pAb[n],0,0,
1-pB[n],0,pB[n],0,
1-pAB[n],0,0,pAB[n]),
nrow = n.states)
npar<-21 # Define the number of regression coefficient for this effect
}
} else if(P_effects=="spatio-temporal effect of patroling effort on detection")
{
pB <-matrix(0, nrow=R, ncol=N)
pAb <-matrix(0, nrow=R, ncol=N)
pAB <-matrix(0, nrow=R, ncol=N)
# obs prob
B <-array(0, dim=c(n.states,n.states,R,N))
for(s in 1:R)
{
for(n in 1:N)
{
pB[s,n] <- 1/(1+exp(-(b[16] + b[19]*tempcov[s,n])))
pAb[s,n] <- 1/(1+exp(-(b[17] + b[20]*tempcov[s,n])))
pAB[s,n] <- 1/(1+exp(-(b[18] + b[21]*tempcov[s,n])))
B[,,s,n] <- matrix(c(
1,0,0,0,
1-pAb[s,n],pAb[s,n],0,0,
1-pB[s,n],0,pB[s,n],0,
1-pAB[s,n],0,0,pAB[s,n]),
nrow = n.states)
}
}
npar<-21 # Define the number of regression coefficient for this effect
}
else if(P_effects=="interactive effects of years")
{
pB <-vector(mode="logical", length=K)
pAb <-vector(mode="logical", length=K)
pAB <-vector(mode="logical", length=K)
B <-array(0, dim=c(n.states,n.states,K))
pB[1] <- 1/(1+exp(-(b[16])))
pAb[1] <- 1/(1+exp(-(b[17])))
pAB[1] <- 1/(1+exp(-(b[18])))
for(k in 2:K)
{
pB[k] <- 1/(1+exp(-(b[(19 + k)])))
pAb[k] <- 1/(1+exp(-(b[(20 + (K-1) + k)])))
pAB[k] <- 1/(1+exp(-(b[(21 + 2*(K-1) + k)])))
B[,,k] <- matrix(c(
1,0,0,0,
1-pAb[k],pAb[k],0,0,
1-pB[k],0,pB[k],0,
1-pAB[k],0,0,pAB[k]),
nrow = n.states)
}
npar <- 20 + 3*(K) # Define the number of regression coefficient for this effect
}
else if(P_effects=="additive effects of years")
{
pB <-vector(mode="logical", length=K)
pAb <-vector(mode="logical", length=K)
pAB <-vector(mode="logical", length=K)
B <-array(0, dim=c(n.states,n.states,K))
pB[1] <- 1/(1+exp(-(b[16])))
pAb[1] <- 1/(1+exp(-(b[17])))
pAB[1] <- 1/(1+exp(-(b[18])))
for(k in 2:K)
{
pB[k] <- 1/(1+exp(-(b[(19 + k)])))
pAb[k] <- 1/(1+exp(-(b[(19 + k)])))
pAB[k] <- 1/(1+exp(-(b[(19 + k)])))
B[,,k] <- matrix(c(
1,0,0,0,
1-pAb[k],pAb[k],0,0,
1-pB[k],0,pB[k],0,
1-pAB[k],0,0,pAB[k]),
nrow = n.states)
}
npar <- 20 + 3*(K) # Define the number of regression coefficient for this effect
}
else if(P_effects=="distance to road effect on detection")
{
pB <-vector(mode="logical", length=R)
pAb <-vector(mode="logical", length=R)
pAB <-vector(mode="logical", length=R)
B <-array(0, dim=c(n.states,n.states,R))
for(s in 1:R)
{
pB[s] <- 1/(1+exp(-(b[16] + b[19]*roadcov[s])))
pAb[s] <- 1/(1+exp(-(b[17] + b[20]*roadcov[s])))
pAB[s] <- 1/(1+exp(-(b[18] + b[21]*roadcov[s])))
B[,,s] <- matrix(c(1,0,0,0,
1-pAb[s],pAb[s],0,0,
1-pB[s],0,pB[s],0,
1-pAB[s],0,0,pAB[s]),
nrow = n.states)
}
npar<-21 # Define the number of regression coefficient for this effect
}
else if(P_effects=="stream length effect on detection")
{
pB <-vector(mode="logical", length=R)
pAb <-vector(mode="logical", length=R)
pAB <-vector(mode="logical", length=R)
B <-array(0, dim=c(n.states,n.states,R))
for(s in 1:R)
{
pB[s] <- 1/(1+exp(-(b[16] + b[19]*streamcov[s])))
pAb[s] <- 1/(1+exp(-(b[17] + b[20]*streamcov[s])))
pAB[s] <- 1/(1+exp(-(b[18] + b[21]*streamcov[s])))
B[,,s] <- matrix(c(1,0,0,0,
1-pAb[s],pAb[s],0,0,
1-pB[s],0,pB[s],0,
1-pAB[s],0,0,pAB[s]),
nrow = n.states)
}
npar<-21 # Define the number of regression coefficient for this effect
}
else if(P_effects=="distance to station effect on detection")
{
pB <-vector(mode="logical", length=R)
pAb <-vector(mode="logical", length=R)
pAB <-vector(mode="logical", length=R)
B <-array(0, dim=c(n.states,n.states,R))
for(s in 1:R)
{
pB[s] <- 1/(1+exp(-(b[16] + b[19]*stationcov[s])))
pAb[s] <- 1/(1+exp(-(b[17] + b[20]*stationcov[s])))
pAB[s] <- 1/(1+exp(-(b[18] + b[21]*stationcov[s])))
B[,,s] <- matrix(c(1,0,0,0,
1-pAb[s],pAb[s],0,0,
1-pB[s],0,pB[s],0,
1-pAB[s],0,0,pAB[s]),
nrow = n.states)
}
npar<-21 # Define the number of regression coefficient for this effect
}
return(list(pB, pAb, pAB, B, npar))
}