tests/testthat/test-inctools.R
In SACEMA/inctools: Incidence Estimation Tools
test_that("mdri estimation works", {
expect_equal(mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("logit_cubic"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE)$MDRI$PE,
235.8039,
tolerance = 1e-05)
expect_equal(mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("cloglog_linear"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE)$MDRI$PE,
248.1445,
tolerance = 1e-05)
expect_equal(mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("logit_cubic"),
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE)$MDRI$PE,
259.2234,
tolerance = 1e-05)
expect_equal(mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("cloglog_linear"),
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE)$MDRI$PE,
271.7752,
tolerance = 1e-05)
expect_equal(class(mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("cloglog_linear"),
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
n_bootstraps = 0,
plot = TRUE)$Plots$cloglog_linear),
c("gg","ggplot"))
expect_equal(class(mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("logit_cubic"),
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
n_bootstraps = 0,
plot = TRUE)$Plots$logit_cubic),
c("gg","ggplot"))
})
test_that("mdri bootstrapping works", {
skip_on_cran()
skip_if(as.numeric(R.Version()$major) == 3 & as.numeric(R.Version()$minor) < 6,
message = "Bootstrapping tests skipped on R versions before 3.6") # Workaround for unexplained failures
expect_equal({
mdri <- mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("logit_cubic"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 100,
random_seed = 123,
plot = FALSE,
parallel = FALSE,
output_bs_parms = FALSE)
unname(c(mdri$MDRI$SE, mdri$MDRI$CI_LB, mdri$MDRI$CI_UB))
},
c(12.4942, 212.1693, 257.5222),
tolerance = 1e-05)
expect_equal({
mdri <- mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("cloglog_linear"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 100,
random_seed = 123,
plot = FALSE,
parallel = FALSE,
output_bs_parms = FALSE)
unname(c(mdri$MDRI$SE, mdri$MDRI$CI_LB, mdri$MDRI$CI_UB))
},
c(12.58502, 221.64980, 268.07910),
tolerance = 1e-05)
expect_equal({
mdri <- mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("logit_cubic"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 100,
random_seed = 123,
plot = FALSE,
parallel = FALSE,
output_bs_parms = TRUE)
c(nrow(mdri$BSparms$logit_cubic),
mean(mdri$BSparms$logit_cubic$beta0),
mean(mdri$BSparms$logit_cubic$beta1),
mean(mdri$BSparms$logit_cubic$beta2),
mean(mdri$BSparms$logit_cubic$beta3))
},
c(100, 2.243834, -0.01571105, 2.212851e-05, -1.509442e-08),
tolerance = 1e-05)
expect_equal({
mdri <- mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("cloglog_linear"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 100,
random_seed = 123,
plot = FALSE,
parallel = FALSE,
output_bs_parms = TRUE)
c(nrow(mdri$BSparms$cloglog_linear),
mean(mdri$BSparms$cloglog_linear$beta0),
mean(mdri$BSparms$cloglog_linear$beta1))
},
c(100, 4.2747332, -0.9427134),
tolerance = 1e-05)
# Run estimation and perform a few tests on the result (is this bad?)
expect_equal({
mdri_parallel <- mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("cloglog_linear", "logit_cubic"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 500,
random_seed = 123,
plot = FALSE,
parallel = TRUE,
cores = 2,
output_bs_parms = FALSE)
unname(c(mdri_parallel$MDRI$SE[1], mdri_parallel$MDRI$SE[2],
mdri_parallel$MDRI$CI_LB[1], mdri_parallel$MDRI$CI_LB[2],
mdri_parallel$MDRI$CI_UB[1], mdri_parallel$MDRI$CI_UB[2]))
},
c(13.05429, 13.01022, 221.56480, 209.66825, 271.29934, 259.05984),
tolerance = 1e-05)
expect_equal({
mdri_parallel_bsparams <- mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("cloglog_linear", "logit_cubic"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 250,
random_seed = NULL,
plot = FALSE,
parallel = TRUE,
cores = 2,
output_bs_parms = TRUE)
c(nrow(mdri_parallel_bsparams$BSparms$cloglog_linear),
nrow(mdri_parallel_bsparams$BSparms$logit_cubic))
},
c(250,250)
)
})
test_that("mdrical() error messages work", {
expect_error(
mdrical(subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("cloglog_linear"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE),
"No input data has been specified", fixed = TRUE
)
expect_error(
mdrical(data=excalibdata,
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("cloglog_linear"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE),
"No subject identifier has been specified", fixed = TRUE
)
expect_error(
mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("logit_cubic"),
recency_rule = "garbage_rule",
recency_vars = "Recent",
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE),
"Please specify a valid recency rule", fixed = TRUE
)
expect_error(
mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("logit_cubic"),
recency_rule = "binary_data",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE),
"Binary data should have one recency variable", fixed = TRUE
)
expect_error(
mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("logit_cubic"),
recency_rule = "independent_thresholds",
recency_vars = "Recent",
recency_params =c(10,0,1000,1),
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE),
"The number of recency variables must match the number of recency paramaters", fixed = TRUE
)
expect_error(
mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("logit"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE),
"Please specify valid functional form(s)", fixed = TRUE
)
})
test_that("frr estimation works", {
expect_equal(frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
alpha = 0.05)$FRRest, 0.03007519)
expect_equal(frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
alpha = 0.05,
method = "probit")$ci_method, "probit")
expect_equal(frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
alpha = 0.05,
method = "probit")$CI_LB, 0.01460227)
expect_equal(frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
alpha = 0.05,
method = "probit")$CI_UB, 0.05720647)
})
test_that("frrcal() error messages work", {
expect_error(
frrcal(subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "binary_data",
recency_vars = "Recent"),
"No dataframe provided", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "binary_data",
recency_vars = "Recent"),
"Subject identifier must be specified", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "SubjectID",
recency_cutoff_time = 730.5,
recency_rule = "binary_data",
recency_vars = "Recent"),
"Time variable must be specified", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "GarbageVar",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "binary_data",
recency_vars = "Recent"),
"There is no column GarbageVar in the data frame", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "GarbageVar",
recency_cutoff_time = 730.5,
recency_rule = "binary_data",
recency_vars = "Recent"),
"There is no column GarbageVar in the data frame", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "binary_data",
recency_vars = "GarbageVar"),
"There is no column GarbageVar in the data frame", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "garbage_rule",
recency_vars = "Recent"),
"Please specify a valid recency rule", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "binary_data",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1)),
"Binary data should be specified in one recency (outcome) variable.", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "independent_thresholds",
recency_vars = "Recent",
recency_params =c(10,0,1000,1)),
"The number of recency variables must match the number of recency paramaters.", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
alpha = 0.05,
method = NULL),
"Confidence interval method must be specified", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
alpha = 0.05,
method = c("exact", "ac")),
"Exactly one confidence interval method must be specified", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
alpha = 0.05,
method = "garbage"),
"Confidence interval method must be one of 'exact', 'ac', 'asymptotic', 'wilson', 'prop.test', 'bayes', 'logit', 'cloglog', 'probit'. See help of binom::binom.test() for further details", fixed = TRUE
)
})
test_that("incidence estimation works", {
expect_equal(incprops(PrevH = 0.20,
RSE_PrevH = 0.028,
PrevR = 0.10,
RSE_PrevR = 0.09,
Boot = FALSE,
MDRI = 200,
RSE_MDRI = 0.05,
FRR = 0.01,
RSE_FRR = 0.2,
BigT = 730.5,
debug = FALSE)$Incidence.Statistics$Incidence,
0.04264836)
expect_equal(incprops(PrevH = c(0.20,0.21,0.18),
RSE_PrevH = c(0.028,0.03,0.022),
PrevR = c(0.10,0.13,0.12),
RSE_PrevR = c(0.094,0.095,0.05),
Boot = FALSE,
BMest = 'MDRI.FRR.indep',
MDRI = c(200,180,180),
RSE_MDRI = c(0.05,0.07,0.06),
FRR = c(0.01,0.009,0.02),
RSE_FRR = c(0.2,0.2,0.1),
BigT = 730.5)$Incidence.Difference.Statistics$p_value,
c(0.01491562,0.39928244,0.01491562,0.05327965,0.39928244,0.05327965))
})
test_that("inccounts() and incprops() give the same answers", {
expect_equal(inccounts(N = 10000,
N_H = 1000,
N_testR = 990,
N_R = 99,
DE_H = 1.2,
DE_R = 1.2,
Boot = FALSE,
alpha = 0.05,
MDRI= 200,
RSE_MDRI = 0.1,
FRR = 0.01,
RSE_FRR = 0.25,
BigT = 730.5,
Covar_HR = 0,
debug = FALSE)$Incidence.Statistics$Incidence,
incprops(PrevH = 0.1,
RSE_PrevH = 0.03286335,
PrevR = 0.1,
RSE_PrevR = 0.1044466,
Boot = FALSE,
alpha = 0.05,
MDRI= 200,
RSE_MDRI = 0.1,
FRR = 0.01,
RSE_FRR = 0.25,
BigT = 730.5,
Covar_HR = 0,
debug = FALSE)$Incidence.Statistics$Incidence)
expect_equal(inccounts(N = 10000,
N_H = 1000,
N_testR = 990,
N_R = 99,
DE_H = 1.2,
DE_R = 1.2,
Boot = FALSE,
alpha = 0.05,
MDRI= 200,
RSE_MDRI = 0.1,
FRR = 0.01,
RSE_FRR = 0.25,
BigT = 730.5,
Covar_HR = 0,
debug = FALSE)$Annual.Risk.of.Infection$ARI,
incprops(PrevH = 0.1,
RSE_PrevH = 0.03286335,
PrevR = 0.1,
RSE_PrevR = 0.1044466,
Boot = FALSE,
alpha = 0.05,
MDRI= 200,
RSE_MDRI = 0.1,
FRR = 0.01,
RSE_FRR = 0.25,
BigT = 730.5,
Covar_HR = 0,
debug = FALSE)$Annual.Risk.of.Infection$ARI)
})
test_that("power calculation works", {
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = 5000,
alpha = 0.05,
Power = "out",
SS = NULL,
DE_H = c(1,1.1),
DE_R = 1,
BMest = "same.test",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = 0.01,
RSE_FRR = 0.20,
BigT = 730.5)$Inc.Difference.Statistics$Power,
0.8569811,
tolerance = 1e-07)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = 5000,
alpha = 0.05,
Power = "out",
SS = NULL,
DE_H = c(1,1.1),
DE_R = 1,
BMest = "same.test",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = 0.01,
RSE_FRR = 0.20,
BigT = 730.5)$Inc.Difference.Statistics$RSE_deltaI,
0.3303799,
tolerance = 1e-07)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = 5000,
alpha = 0.05,
Power = "out",
SS = NULL,
DE_H = c(1,1.1),
DE_R = 1,
BMest = "same.test",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = 0.01,
RSE_FRR = 0.20,
BigT = 730.5)$Inc.Difference.Statistics$RSE_deltaI.infSS,
0.05244642,
tolerance = 1e-07)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
alpha = 0.05,
Power = 0.8,
SS = "out",
DE_H = c(1,1.1),
DE_R = 1,
BMest = "same.test",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = 0.01,
RSE_FRR = 0.20,
BigT = 730.5)$Minimum.Common.SS,
4268)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = 5000,
alpha = 0.05,
Power = "out",
SS = NULL,
DE_H = c(1,1.1),
DE_R = 1,
BMest = "FRR.indep",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Inc.Difference.Statistics$Power,
0.8238909,
tolerance = 1e-07)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = 5000,
alpha = 0.05,
Power = "out",
SS = NULL,
DE_H = c(1,1.1),
DE_R = 1,
BMest = "FRR.indep",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Inc.Difference.Statistics$RSE_deltaI,
0.3459897,
tolerance = 1e-07)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = 5000,
alpha = 0.05,
Power = "out",
SS = NULL,
DE_H = c(1,1.1),
DE_R = 1,
BMest = "FRR.indep",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Inc.Difference.Statistics$RSE_deltaI.infSS,
0.07965797,
tolerance = 1e-07)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
alpha = 0.05,
Power = 0.8,
SS = "out",
DE_H = c(1,1.1),
DE_R = 1,
BMest = "FRR.indep",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Minimum.Common.SS,
4683)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = 5000,
alpha = 0.05,
Power = "out",
SS = NULL,
DE_H = c(1,1.1),
DE_R = 1,
BMest = "MDRI.FRR.indep",
MDRI = c(200, 180),
RSE_MDRI = c(0.05, 0.1),
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Inc.Difference.Statistics$Power,
0.6818551,
tolerance = 1e-07)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = 5000,
alpha = 0.05,
Power = "out",
SS = NULL,
DE_H = c(1,1.1),
DE_R = 1,
BMest = "MDRI.FRR.indep",
MDRI = c(200, 180),
RSE_MDRI = c(0.05, 0.1),
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Inc.Difference.Statistics$RSE_deltaI,
0.4110394,
tolerance = 1e-07)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = 5000,
alpha = 0.05,
Power = "out",
SS = NULL,
DE_H = c(1,1.1),
DE_R = 1,
BMest = "MDRI.FRR.indep",
MDRI = c(200, 180),
RSE_MDRI = c(0.05, 0.1),
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Inc.Difference.Statistics$RSE_deltaI.infSS,
0.2196649,
tolerance = 1e-07)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
alpha = 0.05,
Power = 0.8,
SS = "out",
DE_H = c(1,1.1),
DE_R = 1,
BMest = "MDRI.FRR.indep",
MDRI = c(200, 180),
RSE_MDRI = c(0.05, 0.1),
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Minimum.Common.SS,
7625)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = "out",
n2 = 5000,
SS = "out",
alpha = 0.05,
Power = 0.8,
DE_H = c(1.2, 1.3),
DE_R = 1,
BMest = "same.test",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = 0.01,
RSE_FRR = 0.20,
BigT = 730.5)$Minimum.SS,
4027)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = "out",
SS = "out",
alpha = 0.05,
Power = 0.8,
DE_H = c(1.2, 1.3),
DE_R = 1,
BMest = "same.test",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = 0.01,
RSE_FRR = 0.20,
BigT = 730.5)$Minimum.SS,
3608)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = "out",
n2 = 5000,
SS = "out",
alpha = 0.05,
Power = 0.8,
DE_H = c(1.2, 1.3),
DE_R = 1,
BMest = "FRR.indep",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Minimum.SS,
4615)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = "out",
SS = "out",
alpha = 0.05,
Power = 0.8,
DE_H = c(1.2, 1.3),
DE_R = 1,
BMest = "FRR.indep",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Minimum.SS,
4488)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = "out",
n2 = 5000,
SS = "out",
alpha = 0.05,
Power = 0.8,
DE_H = c(1.2, 1.3),
DE_R = 1,
BMest = "MDRI.FRR.indep",
MDRI = c(200, 180),
RSE_MDRI = c(0.05, 0.1),
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Minimum.SS,
14470)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = "out",
SS = "out",
alpha = 0.05,
Power = 0.8,
DE_H = c(1.2, 1.3),
DE_R = 1,
BMest = "MDRI.FRR.indep",
MDRI = c(200, 180),
RSE_MDRI = c(0.05, 0.1),
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Minimum.SS,
22579)
})
SACEMA/inctools documentation built on Dec. 18, 2021, 11:56 a.m.
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test_that("mdri estimation works", {
expect_equal(mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("logit_cubic"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE)$MDRI$PE,
235.8039,
tolerance = 1e-05)
expect_equal(mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("cloglog_linear"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE)$MDRI$PE,
248.1445,
tolerance = 1e-05)
expect_equal(mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("logit_cubic"),
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE)$MDRI$PE,
259.2234,
tolerance = 1e-05)
expect_equal(mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("cloglog_linear"),
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE)$MDRI$PE,
271.7752,
tolerance = 1e-05)
expect_equal(class(mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("cloglog_linear"),
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
n_bootstraps = 0,
plot = TRUE)$Plots$cloglog_linear),
c("gg","ggplot"))
expect_equal(class(mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("logit_cubic"),
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
n_bootstraps = 0,
plot = TRUE)$Plots$logit_cubic),
c("gg","ggplot"))
})
test_that("mdri bootstrapping works", {
skip_on_cran()
skip_if(as.numeric(R.Version()$major) == 3 & as.numeric(R.Version()$minor) < 6,
message = "Bootstrapping tests skipped on R versions before 3.6") # Workaround for unexplained failures
expect_equal({
mdri <- mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("logit_cubic"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 100,
random_seed = 123,
plot = FALSE,
parallel = FALSE,
output_bs_parms = FALSE)
unname(c(mdri$MDRI$SE, mdri$MDRI$CI_LB, mdri$MDRI$CI_UB))
},
c(12.4942, 212.1693, 257.5222),
tolerance = 1e-05)
expect_equal({
mdri <- mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("cloglog_linear"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 100,
random_seed = 123,
plot = FALSE,
parallel = FALSE,
output_bs_parms = FALSE)
unname(c(mdri$MDRI$SE, mdri$MDRI$CI_LB, mdri$MDRI$CI_UB))
},
c(12.58502, 221.64980, 268.07910),
tolerance = 1e-05)
expect_equal({
mdri <- mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("logit_cubic"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 100,
random_seed = 123,
plot = FALSE,
parallel = FALSE,
output_bs_parms = TRUE)
c(nrow(mdri$BSparms$logit_cubic),
mean(mdri$BSparms$logit_cubic$beta0),
mean(mdri$BSparms$logit_cubic$beta1),
mean(mdri$BSparms$logit_cubic$beta2),
mean(mdri$BSparms$logit_cubic$beta3))
},
c(100, 2.243834, -0.01571105, 2.212851e-05, -1.509442e-08),
tolerance = 1e-05)
expect_equal({
mdri <- mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("cloglog_linear"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 100,
random_seed = 123,
plot = FALSE,
parallel = FALSE,
output_bs_parms = TRUE)
c(nrow(mdri$BSparms$cloglog_linear),
mean(mdri$BSparms$cloglog_linear$beta0),
mean(mdri$BSparms$cloglog_linear$beta1))
},
c(100, 4.2747332, -0.9427134),
tolerance = 1e-05)
# Run estimation and perform a few tests on the result (is this bad?)
expect_equal({
mdri_parallel <- mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("cloglog_linear", "logit_cubic"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 500,
random_seed = 123,
plot = FALSE,
parallel = TRUE,
cores = 2,
output_bs_parms = FALSE)
unname(c(mdri_parallel$MDRI$SE[1], mdri_parallel$MDRI$SE[2],
mdri_parallel$MDRI$CI_LB[1], mdri_parallel$MDRI$CI_LB[2],
mdri_parallel$MDRI$CI_UB[1], mdri_parallel$MDRI$CI_UB[2]))
},
c(13.05429, 13.01022, 221.56480, 209.66825, 271.29934, 259.05984),
tolerance = 1e-05)
expect_equal({
mdri_parallel_bsparams <- mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("cloglog_linear", "logit_cubic"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 250,
random_seed = NULL,
plot = FALSE,
parallel = TRUE,
cores = 2,
output_bs_parms = TRUE)
c(nrow(mdri_parallel_bsparams$BSparms$cloglog_linear),
nrow(mdri_parallel_bsparams$BSparms$logit_cubic))
},
c(250,250)
)
})
test_that("mdrical() error messages work", {
expect_error(
mdrical(subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("cloglog_linear"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE),
"No input data has been specified", fixed = TRUE
)
expect_error(
mdrical(data=excalibdata,
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("cloglog_linear"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE),
"No subject identifier has been specified", fixed = TRUE
)
expect_error(
mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("logit_cubic"),
recency_rule = "garbage_rule",
recency_vars = "Recent",
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE),
"Please specify a valid recency rule", fixed = TRUE
)
expect_error(
mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("logit_cubic"),
recency_rule = "binary_data",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE),
"Binary data should have one recency variable", fixed = TRUE
)
expect_error(
mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("logit_cubic"),
recency_rule = "independent_thresholds",
recency_vars = "Recent",
recency_params =c(10,0,1000,1),
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE),
"The number of recency variables must match the number of recency paramaters", fixed = TRUE
)
expect_error(
mdrical(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
inclusion_time_threshold = 800,
functional_forms = c("logit"),
recency_rule = "binary_data",
recency_vars = "Recent",
n_bootstraps = 0,
plot = FALSE,
parallel = FALSE),
"Please specify valid functional form(s)", fixed = TRUE
)
})
test_that("frr estimation works", {
expect_equal(frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
alpha = 0.05)$FRRest, 0.03007519)
expect_equal(frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
alpha = 0.05,
method = "probit")$ci_method, "probit")
expect_equal(frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
alpha = 0.05,
method = "probit")$CI_LB, 0.01460227)
expect_equal(frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
alpha = 0.05,
method = "probit")$CI_UB, 0.05720647)
})
test_that("frrcal() error messages work", {
expect_error(
frrcal(subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "binary_data",
recency_vars = "Recent"),
"No dataframe provided", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "binary_data",
recency_vars = "Recent"),
"Subject identifier must be specified", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "SubjectID",
recency_cutoff_time = 730.5,
recency_rule = "binary_data",
recency_vars = "Recent"),
"Time variable must be specified", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "GarbageVar",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "binary_data",
recency_vars = "Recent"),
"There is no column GarbageVar in the data frame", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "GarbageVar",
recency_cutoff_time = 730.5,
recency_rule = "binary_data",
recency_vars = "Recent"),
"There is no column GarbageVar in the data frame", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "binary_data",
recency_vars = "GarbageVar"),
"There is no column GarbageVar in the data frame", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "garbage_rule",
recency_vars = "Recent"),
"Please specify a valid recency rule", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "binary_data",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1)),
"Binary data should be specified in one recency (outcome) variable.", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "independent_thresholds",
recency_vars = "Recent",
recency_params =c(10,0,1000,1)),
"The number of recency variables must match the number of recency paramaters.", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
alpha = 0.05,
method = NULL),
"Confidence interval method must be specified", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
alpha = 0.05,
method = c("exact", "ac")),
"Exactly one confidence interval method must be specified", fixed = TRUE
)
expect_error(
frrcal(data=excalibdata,
subid_var = "SubjectID",
time_var = "DaysSinceEDDI",
recency_cutoff_time = 730.5,
recency_rule = "independent_thresholds",
recency_vars = c("Result","VL"),
recency_params = c(10,0,1000,1),
alpha = 0.05,
method = "garbage"),
"Confidence interval method must be one of 'exact', 'ac', 'asymptotic', 'wilson', 'prop.test', 'bayes', 'logit', 'cloglog', 'probit'. See help of binom::binom.test() for further details", fixed = TRUE
)
})
test_that("incidence estimation works", {
expect_equal(incprops(PrevH = 0.20,
RSE_PrevH = 0.028,
PrevR = 0.10,
RSE_PrevR = 0.09,
Boot = FALSE,
MDRI = 200,
RSE_MDRI = 0.05,
FRR = 0.01,
RSE_FRR = 0.2,
BigT = 730.5,
debug = FALSE)$Incidence.Statistics$Incidence,
0.04264836)
expect_equal(incprops(PrevH = c(0.20,0.21,0.18),
RSE_PrevH = c(0.028,0.03,0.022),
PrevR = c(0.10,0.13,0.12),
RSE_PrevR = c(0.094,0.095,0.05),
Boot = FALSE,
BMest = 'MDRI.FRR.indep',
MDRI = c(200,180,180),
RSE_MDRI = c(0.05,0.07,0.06),
FRR = c(0.01,0.009,0.02),
RSE_FRR = c(0.2,0.2,0.1),
BigT = 730.5)$Incidence.Difference.Statistics$p_value,
c(0.01491562,0.39928244,0.01491562,0.05327965,0.39928244,0.05327965))
})
test_that("inccounts() and incprops() give the same answers", {
expect_equal(inccounts(N = 10000,
N_H = 1000,
N_testR = 990,
N_R = 99,
DE_H = 1.2,
DE_R = 1.2,
Boot = FALSE,
alpha = 0.05,
MDRI= 200,
RSE_MDRI = 0.1,
FRR = 0.01,
RSE_FRR = 0.25,
BigT = 730.5,
Covar_HR = 0,
debug = FALSE)$Incidence.Statistics$Incidence,
incprops(PrevH = 0.1,
RSE_PrevH = 0.03286335,
PrevR = 0.1,
RSE_PrevR = 0.1044466,
Boot = FALSE,
alpha = 0.05,
MDRI= 200,
RSE_MDRI = 0.1,
FRR = 0.01,
RSE_FRR = 0.25,
BigT = 730.5,
Covar_HR = 0,
debug = FALSE)$Incidence.Statistics$Incidence)
expect_equal(inccounts(N = 10000,
N_H = 1000,
N_testR = 990,
N_R = 99,
DE_H = 1.2,
DE_R = 1.2,
Boot = FALSE,
alpha = 0.05,
MDRI= 200,
RSE_MDRI = 0.1,
FRR = 0.01,
RSE_FRR = 0.25,
BigT = 730.5,
Covar_HR = 0,
debug = FALSE)$Annual.Risk.of.Infection$ARI,
incprops(PrevH = 0.1,
RSE_PrevH = 0.03286335,
PrevR = 0.1,
RSE_PrevR = 0.1044466,
Boot = FALSE,
alpha = 0.05,
MDRI= 200,
RSE_MDRI = 0.1,
FRR = 0.01,
RSE_FRR = 0.25,
BigT = 730.5,
Covar_HR = 0,
debug = FALSE)$Annual.Risk.of.Infection$ARI)
})
test_that("power calculation works", {
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = 5000,
alpha = 0.05,
Power = "out",
SS = NULL,
DE_H = c(1,1.1),
DE_R = 1,
BMest = "same.test",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = 0.01,
RSE_FRR = 0.20,
BigT = 730.5)$Inc.Difference.Statistics$Power,
0.8569811,
tolerance = 1e-07)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = 5000,
alpha = 0.05,
Power = "out",
SS = NULL,
DE_H = c(1,1.1),
DE_R = 1,
BMest = "same.test",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = 0.01,
RSE_FRR = 0.20,
BigT = 730.5)$Inc.Difference.Statistics$RSE_deltaI,
0.3303799,
tolerance = 1e-07)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = 5000,
alpha = 0.05,
Power = "out",
SS = NULL,
DE_H = c(1,1.1),
DE_R = 1,
BMest = "same.test",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = 0.01,
RSE_FRR = 0.20,
BigT = 730.5)$Inc.Difference.Statistics$RSE_deltaI.infSS,
0.05244642,
tolerance = 1e-07)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
alpha = 0.05,
Power = 0.8,
SS = "out",
DE_H = c(1,1.1),
DE_R = 1,
BMest = "same.test",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = 0.01,
RSE_FRR = 0.20,
BigT = 730.5)$Minimum.Common.SS,
4268)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = 5000,
alpha = 0.05,
Power = "out",
SS = NULL,
DE_H = c(1,1.1),
DE_R = 1,
BMest = "FRR.indep",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Inc.Difference.Statistics$Power,
0.8238909,
tolerance = 1e-07)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = 5000,
alpha = 0.05,
Power = "out",
SS = NULL,
DE_H = c(1,1.1),
DE_R = 1,
BMest = "FRR.indep",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Inc.Difference.Statistics$RSE_deltaI,
0.3459897,
tolerance = 1e-07)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = 5000,
alpha = 0.05,
Power = "out",
SS = NULL,
DE_H = c(1,1.1),
DE_R = 1,
BMest = "FRR.indep",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Inc.Difference.Statistics$RSE_deltaI.infSS,
0.07965797,
tolerance = 1e-07)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
alpha = 0.05,
Power = 0.8,
SS = "out",
DE_H = c(1,1.1),
DE_R = 1,
BMest = "FRR.indep",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Minimum.Common.SS,
4683)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = 5000,
alpha = 0.05,
Power = "out",
SS = NULL,
DE_H = c(1,1.1),
DE_R = 1,
BMest = "MDRI.FRR.indep",
MDRI = c(200, 180),
RSE_MDRI = c(0.05, 0.1),
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Inc.Difference.Statistics$Power,
0.6818551,
tolerance = 1e-07)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = 5000,
alpha = 0.05,
Power = "out",
SS = NULL,
DE_H = c(1,1.1),
DE_R = 1,
BMest = "MDRI.FRR.indep",
MDRI = c(200, 180),
RSE_MDRI = c(0.05, 0.1),
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Inc.Difference.Statistics$RSE_deltaI,
0.4110394,
tolerance = 1e-07)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = 5000,
alpha = 0.05,
Power = "out",
SS = NULL,
DE_H = c(1,1.1),
DE_R = 1,
BMest = "MDRI.FRR.indep",
MDRI = c(200, 180),
RSE_MDRI = c(0.05, 0.1),
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Inc.Difference.Statistics$RSE_deltaI.infSS,
0.2196649,
tolerance = 1e-07)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
alpha = 0.05,
Power = 0.8,
SS = "out",
DE_H = c(1,1.1),
DE_R = 1,
BMest = "MDRI.FRR.indep",
MDRI = c(200, 180),
RSE_MDRI = c(0.05, 0.1),
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Minimum.Common.SS,
7625)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = "out",
n2 = 5000,
SS = "out",
alpha = 0.05,
Power = 0.8,
DE_H = c(1.2, 1.3),
DE_R = 1,
BMest = "same.test",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = 0.01,
RSE_FRR = 0.20,
BigT = 730.5)$Minimum.SS,
4027)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = "out",
SS = "out",
alpha = 0.05,
Power = 0.8,
DE_H = c(1.2, 1.3),
DE_R = 1,
BMest = "same.test",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = 0.01,
RSE_FRR = 0.20,
BigT = 730.5)$Minimum.SS,
3608)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = "out",
n2 = 5000,
SS = "out",
alpha = 0.05,
Power = 0.8,
DE_H = c(1.2, 1.3),
DE_R = 1,
BMest = "FRR.indep",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Minimum.SS,
4615)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = "out",
SS = "out",
alpha = 0.05,
Power = 0.8,
DE_H = c(1.2, 1.3),
DE_R = 1,
BMest = "FRR.indep",
MDRI = 200,
RSE_MDRI = 0.05,
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Minimum.SS,
4488)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = "out",
n2 = 5000,
SS = "out",
alpha = 0.05,
Power = 0.8,
DE_H = c(1.2, 1.3),
DE_R = 1,
BMest = "MDRI.FRR.indep",
MDRI = c(200, 180),
RSE_MDRI = c(0.05, 0.1),
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Minimum.SS,
14470)
expect_equal(incpower(I1 = 0.05,
I2 = 0.03,
PrevH1 = 0.20,
PrevH2 = 0.20,
n1 = 5000,
n2 = "out",
SS = "out",
alpha = 0.05,
Power = 0.8,
DE_H = c(1.2, 1.3),
DE_R = 1,
BMest = "MDRI.FRR.indep",
MDRI = c(200, 180),
RSE_MDRI = c(0.05, 0.1),
FRR = c(0.01, 0.02),
RSE_FRR = c(0.20, 0.15),
BigT = 730.5)$Minimum.SS,
22579)
})
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