Nothing
context("IT Proper")
guts <- guts_setup(
C = c(4, 2, 4, 6, 6),
Ct = seq_len(5) - 1,
y = c(10,3,2,1,0),
yt = seq_len(5) - 1,
dist = "loglogistic",
model = "Proper",
N = 10000,
M = 10000,
study = "Test proper loglogistic",
Clevel = "arbitrary"
)
para <- c(hb = 0, kd = 1.3, kk = 0.07, alpha = 3, beta = 2)
#print(guts)
test_that("new and old versions are similar (up to tolerance 1e-5)", {
expect_equal(
guts_calc_survivalprobs(guts, par = para),
c(1.0000000, 0.9910818, 0.9678977, 0.9052989, 0.7982619),
tolerance = 1e-5
)
expect_equal(
guts_calc_loglikelihood(guts, par = para),
-41.80747,
tolerance = 1e-5
)
expect_equal(
guts_report_sppe(guts),
-79.82619,
tolerance = 1e-5
)
expect_equal(
guts_report_squares(guts),
235.2989,
tolerance = 1e-5
)
})
guts <- guts_setup(
C = c(4, 2, 4, 6, 6),
Ct = seq_len(5) - 1,
y = c(10,3,2,1,0),
yt = seq_len(5) - 1,
dist = "lognormal",
model = "Proper",
N = 10000,
M = 10000,
study = "Test lognormal",
Clevel = "arbitrary"
)
para <- c(hb = 0, kd = 1.3, kk = 0.07, mn = 3, sd = 2)
#print(guts)
test_that("new and old versions are similar (up to tolerance 1e-5)", {
expect_equal(
guts_calc_survivalprobs(guts, par = para),
c(1.0000000, 0.9923859, 0.9683345, 0.8941076, 0.7645970),
tolerance = 1e-5
)
expect_equal(
guts_calc_loglikelihood(guts, par = para),
-42.51648,
tolerance = 1e-5
)
expect_equal(
guts_report_sppe(guts),
-76.4597,
tolerance = 1e-5
)
expect_equal(
guts_report_squares(guts),
228.4952,
tolerance = 1e-5
)
})
guts <- guts_setup(
C = c(4, 2, 4, 6, 6),
Ct = seq_len(5) - 1,
y = c(10,3,2,1,0),
yt = seq_len(5) - 1,
dist = "delta",
model = "Proper",
N = 10000,
M = 10000,
study = "Test proper delta",
Clevel = "arbitrary"
)
para <- c(hb = 1e-5, kd = 1.3, kk = 0.1, t1 = 3)
#print(guts)
test_that("new and old versions are similar (up to tolerance 1e-5)", {
expect_equal(
guts_calc_survivalprobs(guts, par = para),
c(1.0000000, 0.9999900, 0.9999800, 0.9319453, 0.7475945),
tolerance = 1e-5
)
expect_equal(
guts_calc_loglikelihood(guts, par = para),
-96.48211,
tolerance = 1e-5
)
expect_equal(
guts_report_sppe(guts),
-74.75945,
tolerance = 1e-5
)
expect_equal(
guts_report_squares(guts),
238.0985,
tolerance = 1e-5
)
})
withr::with_seed(1, {
guts <- guts_setup(
C = c(4, 2, 4, 6, 6),
Ct = seq_len(5) - 1,
y = c(10,3,2,1,0),
yt = seq_len(5) - 1,
dist = "external",
model = "Proper",
N = 10000,
M = 10000,
study = "Test proper external",
Clevel = "arbitrary"
)
para <- c(0.051, 0.126, 1.618, 19.099, 6.495)
sigma2 <- log( 1 + 6.495^2 / 19.099^2)
mu <- log(19.099) - 0.5 * sigma2
lognormal.thresholds <- sort(rlnorm(100, meanlog = mu, sdlog = sqrt(sigma2)))
lognormal.thresholds.orig <- lognormal.thresholds
gts.lognormal <- guts_setup(
C = c(4, 2, 4, 6, 6),
Ct = seq_len(5) - 1,
y = c(10,3,2,1,0),
yt = seq_len(5) - 1,
dist = "lognormal", model = "Proper")
test_that("lognormal external distribution and internal distribution are similar (up to tolerance 1e-2 -- might fail due to random evaluation)", {
expect_equal(
guts_calc_loglikelihood(guts, par = para[1:3], external_dist = lognormal.thresholds),
guts_calc_loglikelihood(gts.lognormal, c(0.051, 0.126, 1.618, 19.099, 6.495)),
tolerance = 1e-2
)
expect_equal(
guts_calc_survivalprobs(guts, par = para[1:3], external_dist = lognormal.thresholds),
guts_calc_survivalprobs(gts.lognormal, c(0.051, 0.126, 1.618, 19.099, 6.495)),
tolerance = 1e-2
)
})
test_that("lognormal external distribution has not changed during manipulation (Rcpp correctly clones the distribution vector)", {
expect_equal(
lognormal.thresholds,
lognormal.thresholds.orig
)
})
data(diazinon)
gts.lognormal <- guts_setup(
C = diazinon$C1, Ct = diazinon$Ct1,
y = diazinon$y1, yt = diazinon$yt1,
M = 10000,
dist = "lognormal", model = "Proper")
sigma2 <- log( 1 + 6.495^2 / 19.099^2)
mu <- log(19.099) - 0.5 * sigma2
lognormal.thresholds <- sort(rlnorm(10000, meanlog = mu, sdlog = sqrt(sigma2)))
gts.external <- guts_setup(
C = diazinon$C1, Ct = diazinon$Ct1,
y = diazinon$y1, yt = diazinon$yt1,
dist = "external", model = "Proper",
N = 1000,
M = 10000,
study = "Test proper external",
Clevel = "arbitrary")
test_that("lognormal external distribution and internal distribution are similar for diazinon (up to tolerance 1e-2 -- might fail due to random evaluation)", {
expect_equal(
guts_calc_loglikelihood(gts.external, c(0.051, 0.126, 1.618), lognormal.thresholds),
guts_calc_loglikelihood(gts.lognormal, c(0.051, 0.126, 1.618, 19.099, 6.495)),
tolerance = 1e-2
)
expect_equal(
guts_calc_survivalprobs(gts.external, c(0.051, 0.126, 1.618), lognormal.thresholds),
guts_calc_survivalprobs(gts.lognormal, c(0.051, 0.126, 1.618, 19.099, 6.495)),
tolerance = 1e-2
)
})
})
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