Added 400 m buffer

Code/JAGS/SCR_Allsites.txt

@@ -1,17 +1,26 @@
 
      model {
 
-    for(g in 1:length(Sites)) {
-
-     alpha1[g, t] ~ dnorm(mu_a1, 1 / sd_a1 / sd_a1 )
      mu_a1 ~ dnorm(0, 1 / (25^2))I(0, ) ## half normal
      sd_a1 ~ dunif(0, 5)
+     mu_a2 ~ dnorm(0, 0.01)
+     # sd_a2 ~ dt(0, pow(5, -2), 1)T(0, ) # half cauchy prior with scale = 5 (25?)
+     sd_a2 ~ dunif(0, 5)
+
+    for(g in 1:n_sites) {
+
+for(i in 1:M) {
+     Sex[g, i] ~ dbern(psi.sex[g])
+     Sex2[g, i] <- Sex[g, i] + 1
+}
 
      for(t in 1:2){
-     sigma[g, t] <- pow(1 / (2*alpha1[t]), 0.5) # sd of half normal
+      alpha1[g, t] ~ dnorm(0, 1 / (25^2))I(0, ) ## half normal independent across sites and sexes
+     # alpha1[g, t] ~ dnorm(mu_a1, 1 / sd_a1 / sd_a1 )I(0, ) # BUGS I(,) notation is only allowed if all parameters are fixed
+      sigma[g, t] <- pow(1 / (2*alpha1[g, t]), 0.5) # sd of half normal
      } # t
 
-     psi[g] ~ dunif(0, 1) # giving numbers between 0 and 1, 
+     psi[g] ~ dunif(0, 1) # prob of individual being in the population (for augmentation since N unknown)
 # logit(psi[g]) ~ dnorm(mu_psi, 1 / sd_psi / sd_psi) # consider drawing from a normal distribution across sites
 # mu_psi ~ dnorm(0, 0.01)
 # sd_psi ~ dunif(0, 10)
@@ -22,7 +31,7 @@
 
     for(i in 1:M) {
      for(k in 1:K) {
-     eta[g,i,k] ~ dnorm(0, 1 / (sigma_ind * sigma_ind))
+     eta[g,i,k] ~ dnorm(0, 1 / (sigma_ind[g] * sigma_ind[g]))
      } # i
      } # k
 
@@ -34,9 +43,6 @@
      for(i in 1:M) {
      alpha2[g, i] ~ dnorm(mu_a2, 1 / sd_a2 / sd_a2) # take out g here? Trap behavior universal b/w sites?
      } # m
-     mu_a2 ~ dnorm(0, 0.01)
-     # sd_a2 ~ dt(0, pow(5, -2), 1)T(0, ) # half cauchy prior with scale = 5 (25?)
-     sd_a2 ~ dunif(0, 5)
 
      for(i in 1:M) {
      z[g, i] ~ dbern(psi[g])
@@ -47,27 +53,25 @@
 
      for(k in 1:K) {
      for(t in 1:2) {
-     logit(p0[g, i, j, k, t]) <- alpha0[t] + (alpha2[g, i] * C[i, k, g]) + eta[g, i, k]  # alpha2*C to rep. global behav. response
-     } # i
+     logit(p0[g, i, j, k, t]) <- alpha0[g, t] + (alpha2[g, i] * C[i, k, g]) + eta[g, i, k]  # alpha2*C to rep. global behav. response
+     } # t
+     } # k
      } # j
-     } # k 
-     } # t    #### error?
+     } # i    
 
      for(i in 1:M) {
-     Sex[i] ~ dbern(psi.sex)
-     Sex2[i] <- Sex[i] + 1
      for (j in 1:max_trap[g]) {
      for (k in 1:K) {
-     y[g, i, j, k] ~ dbern(p[g, i, j, k])
-     p[g, i, j, k] <- z[i]*p0[g, i, j, k, Sex2[i]* exp(- alpha1[Sex2[i]] * d[g, i,j] * d[g, i,j])
+     y[i, j, k, g] ~ dbern(p[g, i, j, k])
+     p[g, i, j, k] <- z[g, i] * p0[g, i, j, k, Sex2[g, i]] * exp(- alpha1[g, Sex2[g, i]] * d[g, i,j] * d[g, i,j])
      } # i
      } # j
      } # k
 
      # Derived parameters
-    N[g] <- sum(z[ , g])
+    N[g] <- sum(z[g , ])
      # N[g] <- inprod(z[1:M_allsites], sitedummy[ , t]) ## see panel 9.2
-     density[g] <- N[g] / (xlim[[g]][2] - xlim[[g]][1]) # divided distances by 100 so calculates turtles per 100 m of canal
+     density[g] <- N[g] / (xlim[g, 2] - xlim[g, 1]) # divided distances by 100 so calculates turtles per 100 m of canal
 
     } # g
 }

Code/MultiSessionModel_NHedits.R

@@ -149,7 +149,7 @@ trap_locs[i, 1:max_trap[i]] <- trap_dist_list[[i]] / 100
 
 xlim <- matrix(NA, 12, 2)
 for(i in 1:12){
-  xlim[i, 1:2] <- c(min(trap_dist_list[[i]]), max(trap_dist_list[[i]]) + 50) / 100 # need to have buffer on each side without being negative. Just added 50 to the end for testing but will have to think through
+  xlim[i, 1:2] <- c(min(trap_dist_list[[i]]) - 400, max(trap_dist_list[[i]]) + 400) / 100 # need to have buffer on each side without being negative. Just added 50 to the end for testing but will have to think through
 }