tests/testthat/test-extras-ssn_lm.R

In SSN2: Spatial Modeling on Stream Networks

# test_local <- FALSE # FALSE for CRAN
#
# if (test_local) {
#
#   ssn_create_distmat(
#     ssn.object = mf04p,
#     predpts = c("pred1km", "CapeHorn"),
#     overwrite = TRUE
#   )
#
#   test_that("random effects work", {
#     ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
#       tailup_type = "exponential",
#       taildown_type = "exponential", euclid_type = "exponential",
#       nugget_type = "nugget", additive = "afvArea",
#       random = ~ as.factor(netID)
#     )
#     expect_s3_class(ssn_mod, "ssn_lm")
#     expect_vector(predict(ssn_mod, "pred1km"))
#   })
#
#   test_that("partition factors work", {
#     ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
#       tailup_type = "exponential",
#       nugget_type = "nugget", additive = "afvArea",
#       partition_factor = ~ as.factor(netID)
#     )
#     expect_s3_class(ssn_mod, "ssn_lm")
#     expect_vector(predict(ssn_mod, "pred1km"))
#   })
#
#   test_that("anisotropy works", {
#     ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
#       taildown_type = "exponential",
#       nugget_type = "nugget", estmethod = "ml",
#       anisotropy = TRUE
#     )
#     expect_s3_class(ssn_mod, "ssn_lm")
#     expect_vector(predict(ssn_mod, "pred1km"))
#   })
#
#   test_that("fixing parameters works", {
#     tu <- tailup_initial("exponential", de = 1, known = "de")
#     ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
#       tailup_initial = tu,
#       taildown_type = "exponential", euclid_type = "exponential",
#       nugget_type = "none", additive = "afvArea"
#     )
#     expect_s3_class(ssn_mod, "ssn_lm")
#     expect_vector(predict(ssn_mod, "pred1km"))
#     expect_equal(coef(ssn_mod, type = "tailup")[["de"]], 1)
#   })
#
#   test_that("missing data works", {
#     mf04p$obs$Summer_mn[1] <- NA
#     ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p,
#       tailup_type = "exponential",
#       nugget_type = "nugget", additive = "afvArea"
#     )
#     expect_s3_class(ssn_mod, "ssn_lm")
#     expect_vector(predict(ssn_mod, "pred1km"))
#     expect_vector(predict(ssn_mod, ".missing"))
#   })
#
#
#   # previously from test-extras
#   test_that("extra test fits", {
#     ssn_mod <- ssn_lm(Summer_mn ~ ELEV_DEM, mf04p, family = "binomial",
#                       tailup_type = "exponential", additive = "afvArea",
#                       random = ~ as.factor(netID))
#     expect_output(print(ssn_mod))
#     expect_output(print(summary(ssn_mod)))
#     expect_type(fitted(ssn_mod, type = "randcov"), "list")
#
#     ssn_mod <- ssn_glm(Summer_mn ~ ELEV_DEM, mf04p, family = "Gamma",
#                        random = ~ as.factor(netID))
#     expect_output(print(ssn_mod))
#     expect_output(print(summary(ssn_mod)))
#     expect_type(fitted(ssn_mod, type = "randcov"), "list")
#
#   })
#
# }