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context("Marginal effect plots")
on_mac <- tolower(Sys.info()[["sysname"]]) == "darwin"
on_cran <- !identical(Sys.getenv("NOT_CRAN"), "true")
skip_if(on_mac & on_cran, "On CRAN mac")
#Setup umf
set.seed(123)
sc <- data.frame(x1=rnorm(3), x2=factor(c("a","b","b")))
oc <- data.frame(x3=rnorm(9))
umf <- unmarkedFrameOccu(y=matrix(c(1,0,0,1,1,0,0,1,0), nrow=3),
siteCovs=sc, obsCovs=oc)
#Fit model
good_fit <- TRUE
tryCatch({
fit <- suppressWarnings(stan_occu(~x3~x1+x2, umf,
chains=2, iter=40, refresh=0))
fit2 <- suppressWarnings(stan_occu(~1~1, umf,
chains=2, iter=40, refresh=0))
}, error=function(e){
good_fit <<- FALSE
})
skip_if(!good_fit, "Test setup failed")
test_that("plot_marginal creates gg or grid object",{
#Multiple covariates
pdf(NULL)
mp <- plot_marginal(fit, "state")
expect_is(mp, "gtable")
#Submodel with single covariate
mp2 <- plot_marginal(fit, "det")
expect_is(mp2, "gg")
#Specific covariate
mp3 <- plot_marginal(fit, "state", "x1")
expect_is(mp3, "gg")
#No covariates
expect_error(plot_marginal(fit2, "state"))
dev.off()
})
test_that("marginal_covariate_plot builds plot for either cov type",{
pdf(NULL)
mcp <- marginal_covariate_plot(fit,"state","x1", draws=100)
expect_is(mcp, "gg")
mcp2 <- marginal_covariate_plot(fit,"state","x2", draws=100)
expect_is(mcp, "gg")
#Covariate not in submodel
expect_error(marginal_covariate_plot(fit,"state","fake"))
dev.off()
})
test_that("marg_numeric_plot builds plot for numeric covariate",{
pdf(NULL)
mnp <- marg_numeric_plot(fit, "state", "x1", c(0.025,0.975), draws=100)
expect_is(mnp, "gg")
expect_error(marg_numeric_plot(fit,"state","x2",c(0.025,0.975)), draws=100)
dev.off()
})
test_that("marg_factor_plot builds plot for factor covariate",{
pdf(NULL)
mfp <- marg_factor_plot(fit, "state", "x2", c(0.025,0.975), draws=100)
expect_is(mfp, "gg")
expect_error(marg_factor_plot(fit,"state","x1",c(0.025,0.975), draws=100))
dev.off()
})
test_that("get_baseline_df gets baseline data frame for plot data",{
base_df <- get_baseline_df(fit["state"])
expect_equal(base_df, data.frame(x1=-0.2301775,
x2=factor("a",levels=c("a","b"))), tol=1e-6)
})
test_that("col_is_factor identifies factor column",{
expect_true(col_is_factor("x2", sc))
expect_false(col_is_factor("x1", sc))
})
test_that("Marginal plot data is generated correctly",{
nd <- data.frame(x1=c(-1,1), x2=c("a","b"))
#Continuous
mdata <- get_margplot_data(fit, "state", "x1", c(0.025,0.975),
samples=1:3, nd)
expect_equal(dim(mdata), c(2,4))
expect_is(mdata, "data.frame")
expect_equal(names(mdata), c("covariate","mn","lower","upper"))
#Factor
mdata2 <- get_margplot_data(fit, "state", "x2", c(0.025,0.975),
samples=1:3, nd)
expect_equal(dim(mdata), c(2,4))
expect_is(mdata2, "data.frame")
expect_equal(names(mdata2), c("covariate","mn","lower","upper"))
})
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