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-rw-r--r--tests/testthat/test_occuCOP.R439
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diff --git a/tests/testthat/test_occuCOP.R b/tests/testthat/test_occuCOP.R
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+context("occuCOP fitting function")
+skip_on_cran()
+
+COPsimul <- function(psi = 0.5,
+ lambda = 1,
+ M = 100,
+ J = 5) {
+
+ z_i <- sample(
+ x = c(0, 1),
+ size = M,
+ prob = c(1 - psi, psi),
+ replace = T
+ )
+
+ y = matrix(rpois(n = M * J, lambda = lambda), nrow = M, ncol = J) * z_i
+
+ return(y)
+}
+
+
+# COPsimulfit <- function(psi = 0.5,
+# lambda = 1,
+# M = 100,
+# J = 5,
+# obsLength = NULL,
+# ...) {
+# occuCOP(data = unmarkedFrameCOP(
+# y = simulDataCOP(
+# psi = psi,
+# lambda = lambda,
+# M = M,
+# J = J
+# ),
+# obsLength = obsLength
+# ), ...)
+# }
+
+test_that("unmarkedFrameCOP is constructed correctly", {
+ set.seed(123)
+
+ # Simulate data
+ M = 100
+ J = 5
+ y = COPsimul(psi = .5,
+ lambda = 1,
+ M = M,
+ J = J)
+ obsLength = y * 0 + 1
+
+ psiCovs = data.frame(
+ "psiNum" = rnorm(M),
+ "psiInt" = as.integer(rpois(n = M, lambda = 5)),
+ "psiBool" = sample(c(T, F), size = M, replace = T),
+ "psiChar" = sample(letters[1:5], size = M, replace = T),
+ "psiFactUnord" = factor(sample(
+ letters[5:10], size = M, replace = T
+ )),
+ "psiFactOrd" = sample(factor(c("A", "B", "C"), ordered = T), size =
+ M, replace = T)
+ )
+
+ lambdaCovs = list(
+ "lambdaNum" = matrix(
+ rnorm(M * J),
+ nrow = M, ncol = J
+ ),
+ "lambdaInt" = matrix(
+ as.integer(rpois(n = M * J, lambda = 1e5)),
+ nrow = M, ncol = J
+ ),
+ "lambdaBool" = matrix(
+ sample(c(T, F), size = M * J, replace = T),
+ nrow = M, ncol = J
+ ),
+ "lambdaChar" = matrix(
+ sample(letters[1:5], size = M * J, replace = T),
+ nrow = M, ncol = J
+ ),
+ "lambdaFactUnord" = matrix(
+ factor(sample(letters[5:10], size = M * J, replace = T)),
+ nrow = M, ncol = J
+ ),
+ "lambdaFactOrd" = matrix(
+ sample(factor(c("A", "B", "C"), ordered = T), size = M * J, replace = T),
+ nrow = M, ncol = J
+ )
+ )
+
+
+ # Creating a unmarkedFrameCOP object
+ expect_warning(umf <- unmarkedFrameCOP(y = y))
+ expect_no_error(umf <- unmarkedFrameCOP(y = y, obsLength = obsLength))
+
+
+ # Create subsets
+ expect_no_error(umf_sub_i <- umf[1:3, ])
+ expect_no_error(umf_sub_j <- umf[, 1:2])
+ expect_no_error(umf_sub_ij <- umf[1:3, 1:2])
+
+ # unmarkedFrameCOP organisation ----------------------------------------------
+ expect_true(inherits(umf, "unmarkedFrameCOP"))
+ expect_equivalent(numSites(umf_sub_i), 3)
+ expect_equivalent(obsNum(umf_sub_j), 2)
+ expect_equivalent(numSites(umf_sub_ij), 3)
+ expect_equivalent(obsNum(umf_sub_ij), 2)
+
+ # unmarkedFrameCOP display ---------------------------------------------------
+
+ # print method
+ expect_no_error(print(umf))
+ expect_no_warning(print(umf))
+ expect_no_message(print(umf))
+ expect_no_error(print(umf_sub_i))
+ expect_no_error(print(umf_sub_j))
+ expect_no_error(print(umf_sub_ij))
+ expect_no_error(print(umf[1,]))
+ expect_no_error(print(umf[,1]))
+ expect_no_error(print(umf[1,1]))
+
+ # summary method for unmarkedFrameCOP
+ expect_no_error(summary(umf))
+ expect_no_error(summary(umf_sub_ij))
+
+ # plot method for unmarkedFrameCOP
+ expect_no_error(plot(umf))
+ expect_no_error(plot(umf_sub_ij))
+
+
+ # Input handling: covariates -------------------------------------------------
+ expect_no_error(umfCovs <- unmarkedFrameCOP(
+ y = y,
+ obsLength = obsLength,
+ siteCovs = psiCovs,
+ obsCovs = lambdaCovs
+ ))
+ expect_no_error(print(umfCovs))
+ expect_no_error(summary(umfCovs))
+ expect_no_error(plot(umfCovs))
+
+ # Input handling: NA ---------------------------------------------------------
+
+ # NA should not pose problems when creating the unmarkedFrameCOP object.
+ # The warnings and potential errors will be displayed when fitting the model.
+ # Except when y only contains NA: then there's an error.
+
+ ## NA in y
+ yNA <- y
+ yNA[1:2,] <- NA
+ yNA[3:5, 3:4] <- NA
+ yNA[,3] <- NA
+ expect_no_error(umfNA <- unmarkedFrameCOP(y = yNA, obsLength = obsLength))
+ expect_no_error(print(umfNA))
+ expect_no_error(summary(umfNA))
+ expect_no_error(plot(umfNA))
+
+ ## NA in obsLength
+ obsLengthNA <- obsLength
+ obsLengthNA[1:2,] <- NA
+ obsLengthNA[3:5, 3:4] <- NA
+ obsLengthNA[,5] <- NA
+ expect_no_error(umfNA <- unmarkedFrameCOP(y = y, obsLength = obsLengthNA))
+ expect_no_error(print(umfNA))
+ expect_no_error(summary(umfNA))
+ expect_no_error(plot(umfNA))
+
+ ## NA also in covariates
+ psiCovsNA <- psiCovs
+ psiCovsNA[1:5,] <- NA
+ psiCovsNA[c(8,10,12), 3] <- NA
+ psiCovsNA[,1] <- NA
+ lambdaCovsNA <- lambdaCovs
+ lambdaCovsNA[[1]][1:5,] <- NA
+ lambdaCovsNA[[1]][,3] <- NA
+ lambdaCovsNA[[3]][,5] <- NA
+ expect_no_error(umfCovsNA <- unmarkedFrameCOP(
+ y = yNA,
+ obsLength = obsLengthNA,
+ siteCovs = psiCovsNA,
+ obsCovs = lambdaCovsNA
+ ))
+ expect_no_error(print(umfCovsNA))
+ expect_no_error(summary(umfCovsNA))
+ expect_no_error(plot(umfCovsNA))
+
+ ## all NA in y
+ yallNA <- y
+ yallNA[1:M, 1:J] <- NA
+ expect_error(unmarkedFrameCOP(y = yallNA, obsLength = obsLength))
+
+ # Input handling: error and warnings -----------------------------------------
+ # Error when there are decimals in y
+ y_with_decimals = y
+ y_with_decimals[1, 1] = .5
+ expect_error(unmarkedFrameCOP(y = y_with_decimals, obsLength = obsLength))
+
+ # Warning if y is detection/non-detection instead of count
+ y_detec_nodetec = (y > 0) * 1
+ expect_warning(unmarkedFrameCOP(y = y_detec_nodetec, obsLength = obsLength))
+
+ # Error if the dimension of obsLength is different than that of y
+ expect_error(unmarkedFrameCOP(y = y, obsLength = obsLength[1:2, 1:2]))
+})
+
+
+test_that("occuCOP can fit simple models", {
+ # Simulate data
+ set.seed(123)
+ M = 100
+ J = 5
+ y = COPsimul(psi = .5,
+ lambda = 1,
+ M = M,
+ J = J)
+ obsLength = y * 0 + 1
+
+ # Create umf
+ umf <- unmarkedFrameCOP(y = y, obsLength = obsLength)
+
+ # Fitting options ----
+
+ # With default parameters
+ expect_message(fit_default <- occuCOP(data = umf))
+ expect_no_error(occuCOP(data = umf, psiformula = ~ 1, lambdaformula = ~ 1, psistarts = 0, lambdastarts = 0))
+
+ # With chosen starting points
+ expect_no_error(occuCOP(data = umf,
+ psiformula = ~ 1, lambdaformula = ~ 1,
+ psistarts = qlogis(.7),
+ lambdastarts = log(0.1)))
+ expect_error(occuCOP(data = umf,
+ psiformula = ~ 1, lambdaformula = ~ 1,
+ psistarts = qlogis(c(0.7, 0.5), lambdastarts = 0)))
+ expect_error(occuCOP(data = umf,
+ psiformula = ~ 1, lambdaformula = ~ 1,
+ lambdastarts = log(c(1, 2)), psistarts = 0))
+
+ # With different options
+ expect_no_error(occuCOP(data = umf, method = "Nelder-Mead", psistarts = 0, lambdastarts = 0))
+ expect_error(occuCOP(data = umf, method = "ABC", psistarts = 0, lambdastarts = 0))
+
+ expect_no_error(occuCOP(data = umf, se = F, psistarts = 0, lambdastarts = 0))
+ expect_error(occuCOP(data = umf, se = "ABC"))
+
+ expect_no_error(occuCOP(data = umf, engine = "R", psistarts = 0, lambdastarts = 0))
+ expect_error(occuCOP(data = umf, engine = "julia", psistarts = 0, lambdastarts = 0))
+
+ expect_no_error(occuCOP(data = umf, na.rm = F, psistarts = 0, lambdastarts = 0))
+ expect_error(occuCOP(data = umf, na.rm = "no", psistarts = 0, lambdastarts = 0))
+
+ # Looking at at COP model outputs ----
+ expect_is(fit_default, "unmarkedFitCOP")
+
+ ## backTransform
+ expect_no_error(backTransform(fit_default, type = "psi"))
+ expect_no_error(backTransform(fit_default, type = "lambda"))
+ expect_error(backTransform(fit_default, type = "state"))
+ expect_error(backTransform(fit_default, type = "det"))
+ expect_is(backTransform(fit_default, type = "psi"), "unmarkedBackTrans")
+
+ ## predict with newdata = fit@data
+ expect_no_error(predict(object = fit_default, type = "psi"))
+ expect_no_error(predict(object = fit_default, type = "lambda"))
+ expect_error(predict(object = fit_default, type = "state"))
+
+ ## predict with newdata = 1
+ expect_no_error(predict(
+ object = fit_default,
+ newdata = data.frame(1),
+ type = "psi"
+ ))
+ expect_no_error(predict(
+ object = fit_default,
+ newdata = data.frame(1),
+ type = "lambda"
+ ))
+ expect_no_error(predict(
+ object = fit_default,
+ newdata = data.frame("a"=1:5,"b"=10:14),
+ type = "psi"
+ ))
+
+ # Fitting accurately ----
+
+ ## psi = 0.50, lambda = 1 ----
+ psi_test = .5
+ lambda_test = 1
+ fit_accur <- occuCOP(data = unmarkedFrameCOP(
+ y = COPsimul(
+ psi = psi_test,
+ lambda = lambda_test,
+ M = 1000,
+ J = 10
+ ),
+ obsLength = matrix(1, nrow = 1000, ncol = 10)
+ ), psistarts = 0, lambdastarts = 0)
+ psi_estimate = backTransform(fit_accur, type = "psi")@estimate
+ lambda_estimate = backTransform(fit_accur, type = "lambda")@estimate
+ expect_equivalent(
+ psi_estimate,
+ psi_test,
+ tol = 0.05
+ )
+ expect_equivalent(
+ lambda_estimate,
+ lambda_test,
+ tol = 0.05
+ )
+
+ ## psi = 0.25, lambda = 5 ----
+ psi_test = 0.25
+ lambda_test = 5
+ fit_accur <- occuCOP(data = unmarkedFrameCOP(
+ y = COPsimul(
+ psi = psi_test,
+ lambda = lambda_test,
+ M = 1000,
+ J = 10
+ ),
+ obsLength = matrix(1, nrow = 1000, ncol = 10)
+ ), psistarts = 0, lambdastarts = 0)
+ psi_estimate = backTransform(fit_accur, type = "psi")@estimate
+ lambda_estimate = backTransform(fit_accur, type = "lambda")@estimate
+ expect_equivalent(
+ psi_estimate,
+ psi_test,
+ tol = 0.05
+ )
+ expect_equivalent(
+ lambda_estimate,
+ lambda_test,
+ tol = 0.05
+ )
+
+ ## psi = 0.75, lambda = 0.5 ----
+ psi_test = 0.75
+ lambda_test = 0.5
+ fit_accur <- occuCOP(data = unmarkedFrameCOP(
+ y = COPsimul(
+ psi = psi_test,
+ lambda = lambda_test,
+ M = 1000,
+ J = 10
+ ),
+ obsLength = matrix(1, nrow = 1000, ncol = 10)
+ ), psistarts = 0, lambdastarts = 0)
+ psi_estimate = backTransform(fit_accur, type = "psi")@estimate
+ lambda_estimate = backTransform(fit_accur, type = "lambda")@estimate
+ expect_equivalent(
+ psi_estimate,
+ psi_test,
+ tol = 0.05
+ )
+ expect_equivalent(
+ lambda_estimate,
+ lambda_test,
+ tol = 0.05
+ )
+
+ # With NAs ----
+ yNA <- y
+ yNA[1,] <- NA
+ yNA[3, 1] <- NA
+ yNA[4, 3] <- NA
+ yNA[, 5] <- NA
+ expect_no_error(umfNA <- unmarkedFrameCOP(y = yNA, obsLength = obsLength))
+
+ expect_warning(fit_NA <- occuCOP(data = umfNA, psistarts = 0, lambdastarts = 0))
+ expect_error(occuCOP(data = umfNA, psistarts = 0, lambdastarts = 0, na.rm = F))
+
+
+ #TODO
+})
+
+
+test_that("occuCOP can fit models with covariates", {
+
+})
+
+
+test_that("We can simulate COP data", {
+
+ # From scratch ----
+
+ # With no covariates
+ expect_no_error(simulate(
+ "COP",
+ formulas = list(psi = ~ 1, lambda = ~ 1),
+ coefs = list(
+ psi = c(intercept = 0),
+ lambda = c(intercept = 0)
+ ),
+ design = list(M = 100, J = 100)
+ ))
+
+ # With quantitative covariates
+ expect_no_error(simulate(
+ "COP",
+ formulas = list(psi = ~ elev, lambda = ~ rain),
+ coefs = list(
+ psi = c(intercept = qlogis(.5), elev = -0.5),
+ lambda = c(intercept = log(3), rain = -1)
+ ),
+ design = list(M = 100, J = 5)
+ ))
+
+ # With guides
+ expect_no_error(simulate(
+ "COP",
+ formulas = list(psi = ~ elev, lambda = ~ rain),
+ coefs = list(
+ psi = c(intercept = qlogis(.5), elev = -0.5),
+ lambda = c(intercept = log(3), rain = -1)
+ ),
+ design = list(M = 100, J = 5),
+ guide = list(elev=list(dist=rnorm, mean=12, sd=0.5))
+ ))
+
+ # With qualitative covariates
+ expect_no_error(simulate(
+ "COP",
+ formulas = list(psi = ~ elev + habitat, lambda = ~ 1),
+ coefs = list(
+ psi = c(
+ intercept = qlogis(.2),
+ elev = -0.5,
+ habitatB = .5,
+ habitatC = .8
+ ),
+ lambda = c(intercept = log(3))
+ ),
+ design = list(M = 100, J = 5),
+ guide = list(habitat = factor(levels = c("A", "B", "C")))
+ ))
+
+ # From unmarkedFitCOP ----
+ #TODO
+})
+
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