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context("gpcount fitting function")
skip_on_cran()
test_that("unmarkedFrameGPC subset works",{
y <- matrix(1:27, 3)
sc <- data.frame(x1 = 1:3)
ysc <- list(x2 = matrix(1:9, 3))
oc <- list(x3 = matrix(1:27, 3))
umf1 <- unmarkedFrameGPC(
y = y,
siteCovs = sc,
yearlySiteCovs = ysc,
obsCovs = oc,
numPrimary = 3)
dat <- as(umf1, "data.frame")
umf1.site1 <- umf1[1,]
expect_equal(umf1.site1@y, y[1,, drop=FALSE])
expect_equal(umf1.site1@siteCovs, sc[1,, drop=FALSE])
expect_equivalent(unlist(umf1.site1@obsCovs), oc$x3[1,])
expect_equivalent(unlist(umf1.site1@yearlySiteCovs),
ysc$x2[1,, drop=FALSE])
expect_equal(umf1.site1@numPrimary, 3)
umf1.sites1and3 <- umf1[c(1,3),]
expect_is(umf1.site1, "unmarkedFrameGPC")
umf1.sites1and1 <- umf1[c(1,1),]
umf1.obs1and2 <- umf1[,c(1,2)]
expect_equivalent(dim(getY(umf1.obs1and2)), c(3,6))
expect_equivalent(dim(siteCovs(umf1.obs1and2)), c(3,1))
expect_equivalent(dim(obsCovs(umf1.obs1and2)), c(18,1))
umf1.sites1and2.obs1and2 <- umf1[c(1,2),c(1,2)]
expect_equal(class(umf1.sites1and2.obs1and2)[1], "unmarkedFrameGPC")
expect_equivalent(dim(getY(umf1.sites1and2.obs1and2)), c(2,6))
expect_equivalent(dim(siteCovs(umf1.sites1and2.obs1and2)), c(2,1))
expect_equivalent(dim(obsCovs(umf1.sites1and2.obs1and2)), c(12,1))
# THis doesn't work
umf1.sites1and1.obs1and1 <- umf1[c(1,1),c(1,1)]
})
test_that("gpcount function works", {
y <- matrix(c(0,0,0, 1,0,1, 2,2,2,
3,2,3, 2,2,2, 1,1,1,
NA,0,0, 0,0,0, 0,0,0,
3,3,3, 3,1,3, 2,2,1,
0,0,0, 0,0,0, 0,0,0), 5, 9, byrow=TRUE)
siteCovs <- data.frame(x = c(0,2,-1,4,-1))
obsCovs <- list(o1 = matrix(seq(-3, 3, length=length(y)), 5, 9))
obsCovs$o1[5,4:6] <- NA
yrSiteCovs <- list(yr=matrix(c('1','2','2'), 5, 3, byrow=TRUE))
yrSiteCovs$yr[4,2] <- NA
expect_warning(umf <- unmarkedFrameGPC(y = y, siteCovs = siteCovs, obsCovs = obsCovs,
yearlySiteCovs = yrSiteCovs, numPrimary=3))
expect_warning(fm <- gpcount(~x, ~yr, ~o1, data = umf, K=23))
expect_equal(fm@sitesRemoved, integer(0))
expect_equivalent(coef(fm),
c(1.14754541, 0.44499137, -1.52079283, -0.08881542,
2.52037155, -0.10950615), tol = 1e-5)
# Check methods
expect_warning(gp <- getP(fm))
expect_equal(dim(gp), dim(y))
expect_warning(pr <- predict(fm, 'lambda'))
expect_equal(dim(pr), c(nrow(y), 4))
nd <- data.frame(x=c(0,1))
pr <- predict(fm, 'lambda', newdata=nd)
expect_equal(dim(pr), c(2,4))
expect_equal(pr[1,1], c(3.15045), tol=1e-4)
res <- residuals(fm)
expect_equal(dim(res), dim(y))
expect_warning(r <- ranef(fm))
expect_equal(dim(r@post), c(nrow(y), 24, 1))
expect_equal(bup(r), c(7.31, 12.63, 1.30, 16.12, 2.04), tol=1e-3)
expect_warning(s <- simulate(fm, 2))
expect_equal(length(s), 2)
expect_equal(dim(s[[1]]), dim(y))
expect_warning(pb <- parboot(fm, nsim=1))
expect_is(pb, "parboot")
# Check error when random effect in formula
expect_error(gpcount(~(1|dummy),~1,~1,umf))
})
test_that("gpcount R and C++ engines give same results",{
y <- matrix(c(0,0,0, 1,0,1, 2,2,2,
3,2,3, 2,2,2, 1,1,1,
NA,0,0, 0,0,0, 0,0,0,
3,3,3, 3,1,3, 2,2,1,
0,0,0, 0,0,0, 0,0,0), 5, 9, byrow=TRUE)
siteCovs <- data.frame(x = c(0,2,-1,4,-1))
obsCovs <- list(o1 = matrix(seq(-3, 3, length=length(y)), 5, 9))
yrSiteCovs <- list(yr=matrix(c('1','2','2'), 5, 3, byrow=TRUE))
expect_warning(umf <- unmarkedFrameGPC(y = y, siteCovs = siteCovs, obsCovs = obsCovs,
yearlySiteCovs = yrSiteCovs, numPrimary=3))
fm <- gpcount(~x, ~yr, ~o1, data = umf, K=23, control=list(maxit=1))
fmR <- gpcount(~x, ~yr, ~o1, data = umf, K=23, engine="R", control=list(maxit=1))
expect_equal(coef(fm), coef(fmR))
})
test_that("gpcount ZIP mixture works", {
set.seed(123)
M <- 100
J <- 5
T <- 3
lam <- 3
psi <- 0.3
p <- 0.5
phi <- 0.7
y <- array(NA, c(M, J, T))
N <- unmarked:::rzip(M, lambda=lam, psi=psi)
for (i in 1:M){
for (t in 1:T){
n <- rbinom(1, N[i], phi)
for (j in 1:J){
y[i,j,t] <- rbinom(1, n, p)
}
}
}
ywide <- cbind(y[,,1], y[,,2], y[,,3])
umf <- unmarkedFrameGPC(y=ywide, numPrimary=T)
# check R and C engines match
fitC <- gpcount(~1, ~1, ~1, umf, mixture="ZIP", K=10, engine="C",
se=FALSE, control=list(maxit=1))
fitR <- gpcount(~1, ~1, ~1, umf, mixture="ZIP", K=10, engine="R",
se=FALSE, control=list(maxit=1))
expect_equal(coef(fitC), coef(fitR))
# Properly fit model
fit <- gpcount(~1, ~1, ~1, umf, mixture="ZIP", K=10)
expect_equivalent(coef(fit), c(1.02437, 0.85104, -0.019588, -1.16139), tol=1e-4)
# Check methods
ft <- fitted(fit)
r <- ranef(fit)
b <- bup(r)
#plot(N, b)
#abline(a=0, b=1)
s <- simulate(fit)
})
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