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context("multmixOpen fitting function")
skip_on_cran()
simData <- function(lambda=1, gamma=0.5, omega=0.8, p=0.5, M=100, T=5,
p2=NULL, type="removal")
{
y <- array(NA, c(M, 3, T))
N <- matrix(NA, M, T)
S <- G <- matrix(NA, M, T-1)
if(type=='removal'){
for(i in 1:M) {
N[i,1] <- rpois(1, lambda)
y[i,1,1] <- rbinom(1, N[i,1], p) # Observe some
Nleft1 <- N[i,1] - y[i,1,1] # Remove them
y[i,2,1] <- rbinom(1, Nleft1, p) # ...
Nleft2 <- Nleft1 - y[i,2,1]
y[i,3,1] <- rbinom(1, Nleft2, p)
for(t in 1:(T-1)) {
S[i,t] <- rbinom(1, N[i,t], omega)
G[i,t] <- rpois(1, gamma)
N[i,t+1] <- S[i,t] + G[i,t]
y[i,1,t+1] <- rbinom(1, N[i,t+1], p) # Observe some
Nleft1 <- N[i,t+1] - y[i,1,t+1] # Remove them
y[i,2,t+1] <- rbinom(1, Nleft1, p) # ...
Nleft2 <- Nleft1 - y[i,2,t+1]
y[i,3,t+1] <- rbinom(1, Nleft2, p)
}
}
} else if(type == "double"){
cp <- c(p*(1-p2), p2*(1-p), p*p2)
for(i in 1:M) {
N[i,1] <- rpois(1, lambda)
y[i,,1] <- rmultinom(1, N[i,1], c(cp, 1-sum(cp)))[1:3]
for(t in 1:(T-1)) {
S[i,t] <- rbinom(1, N[i,t], omega)
G[i,t] <- rpois(1, gamma)
N[i,t+1] <- S[i,t] + G[i,t]
y[i,,t+1] <- rmultinom(1, N[i,t+1], c(cp, 1-sum(cp)))[1:3]
}
}
}
return(list(y=matrix(y, M),N=N))
}
test_that("multmixOpen can fit removal models",{
set.seed(123)
simy <- simData(lambda=4, gamma=0.5, omega=0.8, p=0.5,
M=100, T=5)
sc <- data.frame(x1=rnorm(100))
umf <- unmarkedFrameMMO(y=simy$y, numPrimary=5, siteCovs=sc,
type="removal")
fit <- multmixOpen(~x1, ~1, ~1, ~x1, K=20, data=umf)
expect_equivalent(coef(fit), c(1.38860,0.043406,-0.68448,
1.40703,0.03174,-0.00235), tol=1e-5)
#Check predict
pr <- predict(fit, type='lambda')
expect_equivalent(as.numeric(pr[1,]),
c(3.79942,0.298279,3.25808,4.43193), tol=1e-4)
#Check getP
pv <- getP(fit)
expect_equivalent(dim(pv), dim(umf@y))
expect_equivalent(pv[1,1:3], pv[1,4:6])
expect_equivalent(pv[1,1:3], c(0.5086598,0.2499250,0.1227982), tol=1e-5)
#Check residuals
r <- residuals(fit)
expect_equivalent(r[1,1:3], c(0.067122,-0.9497006,0.533337), tol=1e-4)
#Check simulate
set.seed(123)
sim <- simulate(fit, nsim=2)
expect_equivalent(sim[[1]][3,1:3], c(3,0,0))
expect_equivalent(dim(sim[[1]]), c(100,15))
#Check ranef
set.seed(123)
ran <- ranef(fit)
expect_equivalent(bup(ran)[1,1], 3.450738, tol=1e-5)
#Check error when random effect in formula
expect_error(multmixOpen(~(1|dummy), ~1, ~1, ~1, umf))
})
test_that("multmixOpen handles NAs",{
set.seed(123)
simy <- simData(lambda=4, gamma=0.5, omega=0.8, p=0.5,
M=100, T=5)
sc <- data.frame(x1=rnorm(100))
oc <- data.frame(x2=rnorm(100*3*5))
simy$y[1,1:3] <- NA
simy$y[2,1] <- NA
sc$x1[3] <- NA
#This breaks things. I think it has to do with delta
#probably a common issue for all open population functions
#oc$x2[49] <- NA
umf <- unmarkedFrameMMO(y=simy$y, numPrimary=5, siteCovs=sc,
obsCov=oc, type="removal")
fit <- expect_warning(multmixOpen(~x1, ~1, ~1, ~x2, K=20, data=umf))
expect_equivalent(coef(fit), c(1.3800182,0.0390053,
-0.679937,1.398098,
0.02802259,0.010705), tol=1e-4)
# Check ranef
set.seed(123)
simy <- simData(lambda=4, gamma=0.5, omega=0.8, p=0.5,
M=100, T=5)
sc <- data.frame(x1=rnorm(100))
oc <- data.frame(x2=rnorm(100*3*5))
simy$y[2,1] <- NA
umf <- unmarkedFrameMMO(y=simy$y, numPrimary=5, siteCovs=sc,
obsCov=oc, type="removal")
fit <- multmixOpen(~x1, ~1, ~1, ~x2, K=20, data=umf)
r <- ranef(fit)
expect_true(cor(simy$N[,1], bup(r)[,1]) > 0.9)
})
test_that("multmixOpen can fit double observer models",{
set.seed(123)
simy <- simData(lambda=4, gamma=0.5, omega=0.8, p=0.5, p2=0.5,
M=300, T=5, type="double")
umf <- unmarkedFrameMMO(y=simy$y, numPrimary=5,
obsCovs=data.frame(x2=rnorm(300*2*5)),
siteCovs=data.frame(x1=rnorm(300)),
type="double")
# Check that subset works
umf2 <- umf[1:10,]
expect_equal(numSites(umf2), 10)
fit <- multmixOpen(~x1, ~1, ~1, ~x1, K=20, data=umf)
expect_equivalent(coef(fit), c(1.4051,-0.04087,-0.52504,
1.31978,0.072867,0.020069), tol=1e-4)
pv <- getP(fit)
expect_equivalent(dim(pv), dim(umf@y))
expect_equivalent(pv[1,1:3], pv[1,4:6])
expect_equivalent(pv[1,1:3], c(0.24951,0.24951,0.272669), tol=1e-5)
# Check that obs cov is handled correctly
fit2 <- multmixOpen(~x1, ~1, ~1, ~x2, K=20, data=umf)
expect_equivalent(coef(fit2), c(1.4046, -0.04114,-0.5238,1.3198,0.07129,-0.00405),
tol=1e-4)
})
test_that("multmixOpen can fit negative binomial models",{
set.seed(123)
simy <- simData(lambda=4, gamma=0.5, omega=0.8, p=0.5,
M=100, T=5)
sc <- data.frame(x1=rnorm(100))
umf <- unmarkedFrameMMO(y=simy$y, numPrimary=5, siteCovs=sc,
type="removal")
fit <- multmixOpen(~x1, ~1, ~1, ~x1, K=20, mixture="NB", data=umf)
expect_equivalent(coef(fit), c(1.38861,0.0433983,-0.68451,1.40705,
0.031728,-0.002354,9.81437),tol=1e-5)
})
test_that("pop dynamics work with multmixOpen",{
set.seed(123)
simy <- simData(lambda=4, gamma=2, omega=0.5, p=0.5,
M=100, T=5)
sc <- data.frame(x1=rnorm(100))
oc <- data.frame(x2=rnorm(100*3*5))
umf <- unmarkedFrameMMO(y=simy$y, numPrimary=5, siteCovs=sc,
obsCov=oc, type="removal")
fm <- multmixOpen(~1, ~1, ~1, ~1, data = umf, K=25, dynamics="notrend")
expect_equivalent(coef(fm), c(1.35929,-0.18441,-0.041613), tol=1e-4)
fm <- multmixOpen(~1, ~1, ~1, ~1, data = umf, K=25, dynamics="trend")
expect_equivalent(coef(fm), c(1.43740,-0.01538,-0.22348), tol=1e-5)
fm <- multmixOpen(~1, ~1, ~1, ~1, data = umf, K=25, dynamics="autoreg")
expect_equivalent(coef(fm), c(1.45539,-0.76353,0.075356,-0.277835), tol=1e-5)
#Sketchy estimates
#Maybe just because data were simulated using a different process?
#Leaving these in for now just to make sure they run without errors
fm <- multmixOpen(~1, ~1, ~1, ~1, data = umf, K=25, dynamics="gompertz")
expect_warning(fm <- multmixOpen(~1, ~1, ~1, ~1, data = umf, K=25, dynamics="ricker"))
})
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