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context("occuFP fitting function")
skip_on_cran()
test_that("occuFP model can be fit",{
n = 100
o = 10
o1 = 5
y = matrix(0,n,o)
p = .7
r = .5
fp = 0.05
y[1:(n*.5),(o-o1+1):o] <- rbinom((n*o1*.5),1,p)
y[1:(n*.5),1:(o-o1)] <- rbinom((o-o1)*n*.5,1,r)
y[(n*.5+1):n,(o-o1+1):o] <- rbinom((n*o1*.5),1,fp)
type <- c((o-o1),o1,0)
site <- c(rep(1,n*.5*.8),rep(0,n*.5*.2),rep(1,n*.5*.2),rep(0,n*.8*.5))
occ <- matrix(c(rep(0,n*(o-o1)),rep(1,n*o1)),n,o)
site <- data.frame(habitat = site)
occ <- list(METH = occ)
umf1 <- unmarkedFrameOccuFP(y,site,occ, type = type)
m1 <- occuFP(detformula = ~ METH, FPformula = ~1,
stateformula = ~ habitat, data = umf1)
expect_equal(names(m1), c("state","det","fp"))
expect_warning(fl <- fitList(m1,m1))
expect_is(fl,"unmarkedFitList")
expect_equal(length(fl@fits), 2)
pr <- predict(m1, "fp")
expect_equal(dim(pr), c(1000, 4))
# Check error when random effect in formula
expect_error(occuFP(~(1|dummy), ~1, ~1, data=umf1))
})
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