Nothing
library(EcoSimR)
context("Size ratio null model tests")
test_that("size_uniform algorithm works:",{
expect_true(is.vector(size_uniform()))
expect_true(is.vector(size_uniform(dataRodents$Sonoran)))
}
)
test_that("size_uniform_user algorithm works:",{
expect_true(is.vector(size_uniform_user()))
expect_true(is.vector(size_uniform_user(dataRodents$Sonoran)))
expect_true(is.vector(do.call(size_uniform_user,list(speciesData = dataRodents$Sonoran,userLow=4,userHigh=20))))
expect_true(min(do.call(size_uniform_user,list(speciesData = dataRodents$Sonoran,userLow=4,userHigh=20))) > 4)
expect_true(max(do.call(size_uniform_user,list(speciesData = dataRodents$Sonoran,userLow=4,userHigh=20))) < 20)
}
)
test_that("size_source_pool algorithm works:",{
expect_true(is.vector(size_source_pool()))
expect_true(is.vector(size_source_pool(dataRodents$Sonoran)))
expect_true(is.vector(do.call(size_source_pool,list(speciesData = dataRodents$Sonoran,
sourcePool = runif(1000,min(dataRodents$Sonoran),max(dataRodents$Sonoran)),
speciesProbs = rbeta(1000,1,1) ))))
}
)
test_that("size_size_gamma algorithm works:",{
expect_true(is.vector(size_gamma()))
expect_true(is.vector(size_gamma(dataRodents$Sonoran)))
}
)
test_that("min_diff metric works",{
### Test that proper object is returned
expect_true(is.numeric(min_diff()))
### Test that minimum difference is accurate
expect_equal(min_diff(c(1,1.2,-3,3)),.2)
})
test_that("min_diff metric works",{
### Test that proper object is returned
expect_true(is.numeric(min_diff()))
### Test that minimum difference is accurate
expect_equal(min_diff(c(1,1.2,-3,3)),.2)
expect_equal(min_diff(c(1,1.2,1.1,-3,3)),.1)
})
test_that("min_ratio metric works",{
### Test that proper object is returned
expect_true(is.numeric(min_ratio()))
### Test that minimum ratio is accurate
expect_equal(min_ratio(c(1,2,3,4,5,6)),(6/5))
})
test_that("var_diff metric works",{
### Test that proper object is returned
expect_true(is.numeric(var_diff()))
})
test_that("var_ratio metric works",{
### Test that proper object is returned
expect_true(is.numeric(var_ratio()))
})
test_that("size_null_model works with all combinations of metrics and algorithms",{
### Test that proper object is returned
expect_is(size_null_model(dataRodents,metric ="min_diff" ,algo = "size_uniform",nRep=10),"sizenullmod")
expect_is(size_null_model(dataRodents,metric ="min_ratio" ,algo = "size_uniform",nRep=10),"sizenullmod")
expect_is(size_null_model(dataRodents,metric ="var_diff" ,algo = "size_uniform",nRep=10),"sizenullmod")
expect_is(size_null_model(dataRodents,metric ="var_ratio" ,algo = "size_uniform",nRep=10),"sizenullmod")
expect_is(size_null_model(dataRodents,metric ="min_diff" ,algo = "size_uniform_user",algoOpts = list(userLow = 3,userHigh=15),nRep=10),"sizenullmod")
expect_is(size_null_model(dataRodents,metric ="min_ratio" ,algo = "size_uniform_user",algoOpts = list(userLow = 3,userHigh=15),nRep=10),"sizenullmod")
expect_is(size_null_model(dataRodents,metric ="var_diff" ,algo = "size_uniform_user",algoOpts = list(userLow = 3,userHigh=15),nRep=10),"sizenullmod")
expect_is(size_null_model(dataRodents,metric ="var_ratio" ,algo = "size_uniform_user",algoOpts = list(userLow = 3,userHigh=15),nRep=10),"sizenullmod")
expect_is(size_null_model(dataRodents,metric ="min_diff" ,algo = "size_source_pool",algoOpts = list(sourcePool = runif(1000,min(dataRodents$Sonoran),max(dataRodents$Sonoran)),
speciesProbs = rbeta(1000,1,1) ),nRep=10),"sizenullmod")
expect_is(size_null_model(dataRodents,metric ="min_ratio" ,algo = "size_source_pool",algoOpts = list(sourcePool = runif(1000,min(dataRodents$Sonoran),max(dataRodents$Sonoran)),
speciesProbs = rbeta(1000,1,1) ),nRep=10),"sizenullmod")
expect_is(size_null_model(dataRodents,metric ="var_diff" ,algo = "size_source_pool",algoOpts = list(sourcePool = runif(1000,min(dataRodents$Sonoran),max(dataRodents$Sonoran)),
speciesProbs = rbeta(1000,1,1) ),nRep=10),"sizenullmod")
expect_is(size_null_model(dataRodents,metric ="var_ratio" ,algo = "size_source_pool",algoOpts = list(sourcePool = runif(1000,min(dataRodents$Sonoran),max(dataRodents$Sonoran)),
speciesProbs = rbeta(1000,1,1) ),nRep=10),"sizenullmod")
expect_is(size_null_model(dataRodents,metric ="min_diff" ,algo = "size_gamma",nRep=10),"sizenullmod")
expect_is(size_null_model(dataRodents,metric ="min_ratio" ,algo = "size_gamma",nRep=10),"sizenullmod")
expect_is(size_null_model(dataRodents,metric ="var_diff" ,algo = "size_gamma",nRep=10),"sizenullmod")
expect_is(size_null_model(dataRodents,metric ="var_ratio" ,algo = "size_gamma",nRep=10),"sizenullmod")
smod <- size_null_model(dataRodents,metric ="var_diff" ,algo = "size_gamma",nRep=100)
expect_output(summary(smod),"Metric: var_diff")
expect_true(is.null(plot(smod,type="hist")))
expect_true(is.list(plot(smod,type="size")))
})
test_that("all text data frames are handled proprely",{
expect_is(size_null_model(dataRodents,metric ="var_ratio" ,algo = "size_gamma",nRep=10),"sizenullmod")
})
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