Added test for pinferunemjmcmc (#8)

tests/testthat/test-pinferunemjmcmc-example-match-1.4.3.R

@@ -1,37 +1,29 @@
-# TODO #8: fill empty tests
-
-# inference
-
-X <- read.csv("inst/extdata/exa1.csv")
+X <- read.csv("../../inst/extdata/exa1.csv")
 data.example <- as.data.frame(X)
 
 # specify the initial formula
 formula1 <- as.formula(
   paste(colnames(X)[5], "~ 1 +", paste0(colnames(X)[-5], collapse = "+"))
 )
 
-# a set of nonlinearities that will be used in the DBRM model
+# a set of nonlinearities that will be used in the DBRM model as well as the
+# "relations" argument of pinferunemjmcmc
 sini <- function(x) sin(x / 180 * pi)
 expi <- function(x) exp(-abs(x))
 logi <- function(x) log(abs(x) + 1)
 troot <- function(x) abs(x)^(1 / 3)
 to23 <- function(x) abs(x)^(2.3)
 to35 <- function(x) abs(x)^(3.5)
 
-# define the number or cpus
-M <- 4
-# define the size of the simulated samples
-NM <- 1000
-# define \k_{max} + 1 from the paper
-compmax <- 16
-# define treshold for preinclusion of the tree into the analysis
-th <- (10)^(-5)
-# define a final treshold on the posterior marginal probability for reporting a
-# tree
-thf <- 0.05
+M <- 4 # define the number or cpus
+NM <- 1000 # define the size of the simulated samples
+compmax <- 16 # define \k_{max} + 1 from the paper
+th <- (10)^(-5) # define treshold for preinclusion of the tree into the analysis
+thf <- 0.05 # final treshold on the posterior marginal prob for reporting a tree
 # specify tuning parameters of the algorithm for exploring DBRM of interest
 # notice that allow_offsprings=3 corresponds to the GMJMCMC runs and
 # allow_offsprings=4 -to the RGMJMCMC runs
+set.seed(9239838)
 res1 <- pinferunemjmcmc(
   n.cores = M, report.level = 0.5, num.mod.best = NM, simplify = TRUE,
   runemjmcmc.params = list(
@@ -56,8 +48,14 @@ res1 <- pinferunemjmcmc(
   )
 )
 
-print(res1$feat.stat)
 
 test_that("pinferunemjmcmc output matches version 1.4.3", {
-
+  expect_named(
+    res1,
+    c("feat.stat", "predictions", "allposteriors", "threads.stats")
+  )
+  expect_length(res1, 4)
+  expect_length(res1$feat.stat, 14)
+  expect_equal(mean(res1$allposteriors$posterior), 0.280891, tolerance = 1e-4)
+  expect_equal(mean(res1$threads.stats[[1]]$p.post), 0.543057, tolerance = 1e-4)
 })