add some tests for aspects of #265 and #266

tests/testthat/_snaps/data-sim.md

@@ -0,0 +1,21 @@
+# data sim for gfam works
+
+    Code
+      print(sim)
+    Output
+      # A tibble: 400 x 12
+               y index family     x0     x1     x2     x3     f     f0    f1        f2
+           <dbl> <int> <chr>   <dbl>  <dbl>  <dbl>  <dbl> <dbl>  <dbl> <dbl>     <dbl>
+       1  1          1 binary  0.897 0.148  0.348  0.0457 1.59  0.128  0.269 1.20     
+       2  0.237      2 tweedie 0.266 0.659  0.859  0.367  1.10  0.296  0.747 0.0596   
+       3  0.0156     3 normal  0.372 0.185  0.0344 0.741  0.715 0.368  0.290 0.0575   
+       4  1          1 binary  0.573 0.954  0.971  0.934  1.74  0.390  1.35  0.0000172
+       5  1.98       3 normal  0.908 0.898  0.745  0.673  1.75  0.114  1.20  0.432    
+       6 11.1        2 tweedie 0.202 0.944  0.273  0.701  3.24  0.237  1.32  1.68     
+       7  0.904      2 tweedie 0.898 0.724  0.677  0.848  1.61  0.126  0.850 0.629    
+       8  0          1 binary  0.945 0.370  0.348  0.706  1.69  0.0692 0.419 1.20     
+       9  0          1 binary  0.661 0.781  0.947  0.859  1.30  0.350  0.954 0.000486 
+      10  1          1 binary  0.629 0.0111 0.339  0.446  1.84  0.368  0.205 1.27     
+      # i 390 more rows
+      # i 1 more variable: f3 <dbl>
+

tests/testthat/test-data-sim.R

@@ -19,3 +19,10 @@ test_that("test data_sim() reproduces reference output", {
     expect_equal(new_sims[[!!(i)]], ref_sims[[!!(i)]])
   }
 })
+
+test_that("data sim for gfam works", {
+  expect_silent(sim <- data_sim("gfam", n = 400, seed = 2))
+  skip_on_ci()
+  skip_on_cran()
+  expect_snapshot(print(sim))
+})

tests/testthat/test-gfam.R

@@ -0,0 +1,12 @@
+# testing gratia's ability to handle gfam models - not much yet
+test_that("gfam data sim and model works", {
+  expect_silent(
+    gfam_df <- data_sim("gfam", n = 400, seed = 2,
+      gfam_families = c("binary", "tweedie", "normal"))
+  )
+  expect_silent(
+    m_gfam <- gam(cbind(y, index) ~ s(x0) + s(x1) + s(x2) + s(x3),
+      family = gfam(list(binomial, tw, gaussian)),
+      data = gfam_df, method = "REML")
+  )
+})