# 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") # # }) # # }