Nothing
lnl.tol<-1e-4
par.tol<-1e-6
context("prediction")
library(Distance)
# load the Gulf of Mexico dolphin data
data(mexdolphins)
# fit a detection function and look at the summary
hn.model <- suppressMessages(ds(distdata,
max(distdata$distance),
adjustment = NULL))
# check predictions when density is the response
test_that("predictions from density",{
# fit a simple smooth of x and y
mod1 <- dsm(density.est~s(depth), hn.model, segdata, obsdata)
fake_dat <- mod1$data
fake_dat$off.set <- NULL
expect_equal(predict(mod1), predict(mod1, fake_dat, off.set=1), tol=par.tol)
# check for error if we don't supply newdata but do supply off.set
expect_warning(predict(mod1, off.set=1),
"Ignoring supplied off.set as newdata was not supplied")
# check you get different answers from different offsets
expect_equal(2*predict(mod1, fake_dat, off.set=2),
predict(mod1, fake_dat, off.set=4))
fake_dat$off.set <- 1
# check lpmatrix thing
expect_equal(predict(mod1, fake_dat, type="link"),
(predict(mod1, fake_dat, type="lpmatrix")%*%coef(mod1))[,1],
check.attributes=FALSE)
})
# and for count
test_that("predictions for count",{
# fit a simple smooth of x and y
mod1 <- dsm(count~s(depth), hn.model, segdata, obsdata)
fake_dat <- mod1$data
fake_dat$off.set <- exp(fake_dat$off.set)
expect_equal(predict(mod1), predict(mod1, fake_dat))
})
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