Fix multinomPois ranef bug when NAs in removal sampling

4 files changed, 28 insertions, 12 deletions

diff --git a/DESCRIPTION b/DESCRIPTION
index c8511af..de0ed2f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: unmarked
-Version: 1.2.5.9012
-Date: 2023-03-08
+Version: 1.2.5.9013
+Date: 2023-04-05
 Type: Package
 Title: Models for Data from Unmarked Animals
 Authors@R: c(
diff --git a/R/ranef.R b/R/ranef.R
index 9dca184..669dc1a 100644
--- a/R/ranef.R
+++ b/R/ranef.R
@@ -249,24 +249,35 @@ setMethod("ranef", "unmarkedFitMPois",
     lam <- predict(object, type="state")[,1]
     R <- length(lam)
     cp <- getP(object)
-    cp <- cbind(cp, 1-rowSums(cp))
+    cp <- cbind(cp, 1-rowSums(cp, na.rm=TRUE))
     N <- 0:K
-    post <- array(0, c(R, K+1, 1))
+    post <- array(NA, c(R, K+1, 1))
     colnames(post) <- N
     for(i in 1:R) {
         f <- dpois(N, lam[i])
         g <- rep(1, K+1)
-        if(any(is.na(y[i,])) | any(is.na(cp[i,])))
+        yi <- y[i,]
+        cpi <- cp[i,]
+        if(any(is.na(y[i,])) | any(is.na(cp[i,]))){
+          # This only handles cases when all NAs are at the end of the count vector
+          # Not when they are interspersed. I don't know how to handle that situation
+          if(object@data@piFun == "removalPiFun"){
+            warning("NAs in counts and/or covariates for site ",i, ". Keeping only counts before the first NA", call.=FALSE)
+            notNA <- min(which(is.na(y[i,]))[1], which(is.na(cp[i,]))[1], na.rm=TRUE) - 1
+            yi <- yi[c(1:notNA)]
+            cpi <- cpi[c(1:notNA, length(cpi))]
+          } else {
             next
+          }
+        }
         for(k in 1:(K+1)) {
-            yi <- y[i,]
             ydot <- N[k] - sum(yi)
             if(ydot<0) {
                 g[k] <- 0
                 next
             }
-            yi <- c(yi, ydot)
-            g[k] <- g[k] * dmultinom(yi, size=N[k], prob=cp[i,])
+            yik <- c(yi, ydot)
+            g[k] <- g[k] * dmultinom(yik, size=N[k], prob=cpi)
         }
         fudge <- f*g
         post[i,,1] <- fudge / sum(fudge)
diff --git a/tests/testthat/test_multinomPois.R b/tests/testthat/test_multinomPois.R
index 6b4b693..1103839 100644
--- a/tests/testthat/test_multinomPois.R
+++ b/tests/testthat/test_multinomPois.R
@@ -101,9 +101,14 @@ test_that("multinomPois can fit a removal model",{
     res <- residuals(m2_C)
     expect_equal(dim(res), dim(umf1@y))
 
-    expect_warning(r <- ranef(m2_C))
-    expect_equal(dim(r@post), c(3,56,1))
-    expect_equal(bup(r), c(10.794,0.000,2.655), tol=1e-4)
+    expect_warning(r <- ranef(m2_C, K=50))
+    expect_equal(dim(r@post), c(3,51,1))
+    expect_equal(bup(r), c(10.794,6.9317,2.655), tol=1e-4)
+
+    umf2 <- unmarkedFrameMPois(y=y, siteCovs=data.frame(x1=rnorm(5)), type="removal")
+    m4 <- multinomPois(~1~x1, umf2)
+    r <- ranef(m4, K=30)
+    expect_equal(dim(r@post), c(5,31,1))
 
     expect_warning(s <- simulate(m2_C, 2, na.rm=FALSE))
     expect_equal(length(s), 2)
diff --git a/vignettes/occuMulti.Rmd b/vignettes/occuMulti.Rmd
index 25eae85..59f602e 100644
--- a/vignettes/occuMulti.Rmd
+++ b/vignettes/occuMulti.Rmd
@@ -64,7 +64,7 @@ The data are a simplified version of the data used in @Rota2016, with the data c
 The dataset is included with `unmarked` and is called `MesoCarnivores`.
 First, we need to load in the dataset, which is a list with several components.
 
-```{r, eval=FALSE}
+```{r}
 library(unmarked)
 data(MesoCarnivores)
 names(MesoCarnivores)