R Under development (unstable) (2024-12-03 r87418 ucrt) -- "Unsuffered Consequences"
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> 
> ##
> ## models with missing outcome data
> ##
> 
> #devtools::load_all("~/dev/geostan")
> library(geostan)
This is geostan version 0.8.1
> 
> iter = 25
> 
> # CAR Poisson model
> data(georgia)
> C <- shape2mat(georgia, "B", quiet = TRUE)
> cars <- prep_car_data(C, quiet = TRUE)
> N = nrow(georgia)
> georgia$deaths.female[sample.int(N, size = 12)] <- NA
> georgia$deaths.female[sample.int(N, size = 3)] <- 0
> 
> fit <- stan_car(deaths.female ~ offset(log(pop.at.risk.female)),
+                 data = georgia,
+                 car_parts = cars,
+                 chains = 1,
+                 family = poisson(),
+                 iter = iter,
+                 quiet = TRUE) |>
+     suppressWarnings()
15 NA values identified in the outcome variable
Found in rows: 6, 18, 20, 41, 46, 52, 55, 58, 91, 126, 134, 136, 141, 151, 157
> 
> 
> # Binomial model with missing y
> data(georgia)
> A = shape2mat(georgia, "B")
Contiguity condition: queen
Number of neighbors per unit, summary:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   4.000   5.000   5.409   6.000  10.000 

Spatial weights, summary:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      1       1       1       1       1       1 
> N = nrow(A)
> georgia$deaths.female[sample.int(N, size = 25)] <- NA    
> georgia$y <- round(georgia$deaths.female / 10)
> georgia$y[sample.int(N, 5)] <- 0
> georgia$f <- round(4 * georgia$deaths.female / 10)
> 
> 
> # glm
> fit <- stan_glm(cbind(y, f) ~ 1,
+                 data = georgia,
+                 chains = 1,
+                 family = binomial(),
+                 iter = iter,
+                 quiet = TRUE) |>
+     suppressWarnings()
28 NA values identified in the outcome variable
Found in rows: 3, 10, 30, 35, 38, 49, 53, 55, 60, 64, 78, 79, 83, 85, 90, 92, 93, 95, 112, 115, 121, 123, 126, 132, 134, 146, 156, 157
> 
> # icar
> fit <- stan_icar(cbind(deaths.female, pop.at.risk.female) ~ 1,
+                  data = georgia,
+                  type = 'bym',
+                  C = A,
+                  chains = 1,
+                  family = binomial(),
+                  iter = iter,
+                  quiet = TRUE) |>
+     suppressWarnings()
28 NA values identified in the outcome variable
Found in rows: 3, 10, 30, 35, 38, 49, 53, 55, 60, 64, 78, 79, 83, 85, 90, 92, 93, 95, 112, 115, 121, 123, 126, 132, 134, 146, 156, 157
> 
> # esf
> data(georgia)
> georgia$deaths.female[1:10] <- NA
> georgia$y <- georgia$deaths.female
> georgia$f <- round(4 * georgia$deaths.female)
> 
> fit <- stan_esf(cbind(y, f) ~ log(income),
+                 data = georgia,
+                 C = shape2mat(georgia, "B"),
+                 chains = 1,
+                 family = binomial(),
+                 iter = iter,
+                 quiet = TRUE) |>
+     suppressWarnings()
Contiguity condition: queen
Number of neighbors per unit, summary:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   4.000   5.000   5.409   6.000  10.000 

Spatial weights, summary:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      1       1       1       1       1       1 
13 NA values identified in the outcome variable
Found in rows: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 55, 126, 157
> 
> 
> proc.time()
   user  system elapsed 
   9.62    0.43   10.00