Nothing
# test that we can use glms as models
lnl.tol<-1e-4
par.tol<-1e-6
context("Do GLMs work")
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$distance),
adjustment = NULL))
test_that("Do we get the same results?",{
# fit a simple smooth of x and y
mod1<-dsm(count~x+y+depth, hn.model, segdata, obsdata, engine="glm")
#summary(mod1)
res_coef <- c(-1.445646175e+01, -6.034068174e-07,
2.279085802e-06, 6.007334200e-04)
names(res_coef) <- c("(Intercept)", "x", "y", "depth")
expect_equal(coef(mod1), res_coef, tolerance=par.tol)
})
test_that("Density weighting",{
# compare when we set the weights
mod1<-dsm(count~x+y, hn.model, segdata, obsdata, engine="glm")
mod1.w <- dsm(density.est ~ x + y + depth, hn.model, segdata, obsdata,
weights=mod1$data$segment.area, engine="glm")
res_coef <- c(-1.445646196e+01, -6.034067589e-07,
2.279085648e-06, 6.007334023e-04)
names(res_coef) <- c("(Intercept)", "x", "y", "depth")
expect_equal(coef(mod1.w), res_coef, tolerance=par.tol)
})
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