Merge pull request #9 from dochvam/nMixture

Add Daniel's edits and resolve dDHMM issue
nimble-dev · Feb 20, 2020 · 1d3f96a · 1d3f96a
2 parents b5d3b06 + 9d65989
commit 1d3f96a
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Showing 3 changed files with 7 additions and 34 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -8,7 +8,7 @@ Authors@R: c(person("Benjamin R.", "Goldstein", role = c("aut", "cre"),
               person("Daniel", "Turek", role = "aut"),
               person("Lauren", "Ponisio", role = "aut"),
               person("Perry", "de Valpine", role = "aut"))
-Date: 2019-09-24
+Date: 2020-02-20
 Description: Common ecological distributions for 'nimble' models in the form of nimbleFunction objects. 
   Includes Cormack-Jolly-Seber, occupancy, dynamic occupancy, hidden Markov, and dynamic hidden Markov models.
   (Jolly (1965) <doi:10.2307/2333826>, Seber (1965) <10.2307/2333827>, Turek et al. (2016) <doi:10.1007/s10651-016-0353-z>).

diff --git a/R/dDHMM.R b/R/dDHMM.R
@@ -330,34 +330,3 @@ rDHMMo <- nimbleFunction(
 #     }
 #     return(ans)
 #   })
-
-
-
-registerDistributions(list(
-  dDHMM = list(
-    BUGSdist = "dDHMM(init, probObs, probTrans, len)",
-    Rdist = "dDHMM(init, probObs, probTrans, len)",
-    discrete = TRUE,
-    types = c('value = double(1)',
-              'init = double(1)',
-              'probObs = double(2)',
-              'probTrans = double(3)',
-              'len = double()'),
-    mixedSizes = TRUE,
-    pqAvail = FALSE))
-  )
-
-registerDistributions(list(
-  dDHMMo = list(
-    BUGSdist = "dDHMMo(init, probObs, probTrans, len)",
-    Rdist = "dDHMMo(init, probObs, probTrans, len)",
-    discrete = TRUE,
-    types = c('value = double(1)',
-              'init = double(1)',
-              'probObs = double(3)',
-              'probTrans = double(3)',
-              'len = double()'),
-    mixedSizes = TRUE,
-    pqAvail = FALSE))
-  )
-
diff --git a/R/dNmixture.R b/R/dNmixture.R
@@ -173,8 +173,10 @@ dNmixture_v <- nimbleFunction(
 
     ## For each x, the conditional distribution of (N - x | x) is pois(lambda * (1-p))
     ## We determine the lowest N and highest N at extreme quantiles and sum over those.
-    if (Nmin == -1 & Nmax == -1) {
+    if (Nmin == -1) {
       Nmin <- min(x + qpois(0.00001, lambda * (1 - prob)))
+    }
+    if (Nmax == -1) {
       Nmax <- max(x + qpois(0.99999, lambda * (1 - prob)))
     }
     Nmin <- max( max(x), Nmin ) ## set Nmin to at least the largest x
@@ -217,8 +219,10 @@ dNmixture_s <- nimbleFunction(
 
     ## For each x, the conditional distribution of (N - x | x) is pois(lambda * (1-p))
     ## We determine the lowest N and highest N at extreme quantiles and sum over those.
-    if (Nmin == -1 & Nmax == -1) {
+    if (Nmin == -1) {
       Nmin <- min(x + qpois(0.00001, lambda * (1 - prob)))
+    }
+    if (Nmax == -1) {
       Nmax <- max(x + qpois(0.99999, lambda * (1 - prob)))
     }
     Nmin <- max( max(x), Nmin ) ## set Nmin to at least the largest x