Nothing
tests/testthat/test-Logbound.R
In Colossus: "Risk Model Regression and Analysis with Complex Non-Linear Models"
test_that("Coxph strata_basic_single_CR_null log_bound", {
fname <- "ll_comp_0.csv"
colTypes <- c("double", "double", "double", "integer", "integer")
df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
time1 <- "t0"
time2 <- "t1"
df$censor <- (df$lung == 0)
event <- "censor"
names <- c("dose", "fac")
term_n <- c(0, 0)
tform <- c("loglin", "loglin")
keep_constant <- c(1, 0)
a_n <- c(0, 0)
modelform <- "M"
control <- list("ncores" = 2, "lr" = 0.75, "maxiter" = 20, "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
plot_options <- list("name" = paste(tempfile(), "run", sep = ""), "verbose" = FALSE, "studyid" = "studyid", "age_unit" = "years")
dft <- GetCensWeight(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, control, plot_options)
#
#
t_ref <- dft$t
surv_ref <- dft$surv
t_c <- df$t1
cens_weight <- approx(t_ref, surv_ref, t_c, rule = 2)$y
df$weighting <- cens_weight
#
event <- "lung"
a_n <- c(-0.1, -0.1)
keep_constant <- c(0, 0)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(-1, -1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
verbose <- FALSE
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
for (i in c(TRUE, FALSE)) {
for (j in c(TRUE, FALSE)) {
for (k in c(FALSE, FALSE)) {
for (l in c(TRUE, FALSE)) {
for (m in c(TRUE, FALSE)) {
model_control <- list("strata" = i, "basic" = j, "single" = k, "cr" = l, "log_bound" = TRUE, "manual" = m)
a_n <- c(-0.1, -0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 1, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting"))
a_n <- c(-0.1, -0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 1, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "efron", "double_step" = 0)
expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting"))
}
}
}
}
}
for (m in c(TRUE, FALSE)) {
model_control <- list("null" = TRUE, "log_bound" = TRUE, "manual" = m)
a_n <- c(-0.1, -0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
expect_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting"))
model_control <- list("single" = TRUE, "log_bound" = TRUE, "manual" = m)
expect_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting"))
model_control <- list("gradient" = TRUE, "log_bound" = TRUE, "manual" = m)
expect_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting"))
}
})
test_that("Poisson strata_single log_bound", {
fname <- "ll_comp_0.csv"
colTypes <- c("double", "double", "double", "integer", "integer")
df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
df$pyr <- df$t1 - df$t0
time1 <- "t0"
time2 <- "t1"
pyr <- "pyr"
df$censor <- (df$lung == 0)
event <- "censor"
names <- c("dose", "fac")
term_n <- c(0, 0)
tform <- c("loglin", "loglin")
keep_constant <- c(1, 0)
a_n <- c(0, 0)
modelform <- "M"
control <- list("ncores" = 2, "lr" = 0.75, "maxiter" = 20, "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
#
event <- "lung"
a_n <- c(-0.1, -0.1)
keep_constant <- c(0, 0)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(-1, -1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
verbose <- FALSE
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
for (i in c(TRUE, FALSE)) {
for (k in c(FALSE, FALSE)) {
for (m in c(TRUE, FALSE)) {
model_control <- list("strata" = i, "single" = k, "log_bound" = TRUE, "manual" = m)
a_n <- c(-0.1, -0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control))
}
}
}
for (m in c(TRUE, FALSE)) {
for (k in c(TRUE)) {
model_control <- list("strata" = FALSE, "single" = k, "log_bound" = TRUE, "manual" = m)
a_n <- c(-0.1, -0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control))
model_control <- list("strata" = FALSE, "gradient" = k, "log_bound" = TRUE, "manual" = m)
a_n <- c(-0.1, -0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control))
}
}
})
test_that("Coxph EPICURE validated answers, loglin", {
fname <- "base_example.csv"
df <- fread(fname)
time1 <- "entry"
time2 <- "exit"
event <- "event"
names <- c("dose0", "dose1")
term_n <- c(0, 0)
tform <- c("loglin", "loglin")
keep_constant <- c(0, 0)
a_n <- c(0, 0)
modelform <- "M"
#
a_n <- c(-0.6067, 5.019)
model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = 0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
v_lower <- c(-0.6305960, -0.6572672, -0.6817293, -0.6929630, -0.7300938, -0.7537744, -0.7749381, -0.8001031, -0.8175117)
v_upper <- c(-0.5828725, -0.5562505, -0.5318645, -0.5206756, -0.4837373, -0.4602148, -0.4392159, -0.4142752, -0.3970399)
alphas <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005)
for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
a_n <- c(-0.6067, 5.019)
model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = alphas[alpha_i], "para_number" = 0)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4)
expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4)
}
v_lower <- c(4.981497, 4.939337, 4.900838, 4.883211, 4.825191, 4.788380, 4.755608, 4.716794, 4.690041)
v_upper <- c(5.057414, 5.100032, 5.139239, 5.157283, 5.217094, 5.255376, 5.289680, 5.330581, 5.358945)
for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
a_n <- c(-0.6067, 5.019)
model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = alphas[alpha_i], "para_number" = 1)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4)
expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4)
}
})
test_that("Coxph EPICURE validated answers, loglin manual", {
fname <- "base_example.csv"
df <- fread(fname)
time1 <- "entry"
time2 <- "exit"
event <- "event"
names <- c("dose0", "dose1")
term_n <- c(0, 0)
tform <- c("loglin", "loglin")
keep_constant <- c(0, 0)
a_n <- c(0, 0)
modelform <- "M"
#
a_n <- c(-0.6067, 5.019)
model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = 0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
v_lower <- c(-0.6305960, -0.6572672, -0.6817293, -0.6929630, -0.7300938, -0.7537744, -0.7749381, -0.8001031, -0.8175117)
v_upper <- c(-0.5828725, -0.5562505, -0.5318645, -0.5206756, -0.4837373, -0.4602148, -0.4392159, -0.4142752, -0.3970399)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
alphas <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005)
for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
a_n <- c(-0.6067, 5.019)
model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = alphas[alpha_i], "para_number" = 0, "manual" = TRUE)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4)
expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4)
}
v_lower <- c(4.981497, 4.939337, 4.900838, 4.883211, 4.825191, 4.788380, 4.755608, 4.716794, 4.690041)
v_upper <- c(5.057414, 5.100032, 5.139239, 5.157283, 5.217094, 5.255376, 5.289680, 5.330581, 5.358945)
for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
a_n <- c(-0.6067, 5.019)
model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = alphas[alpha_i], "para_number" = 1, "manual" = TRUE)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4)
expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4)
}
})
test_that("Coxph, lin both", {
fname <- "base_example.csv"
df <- fread(fname)
time1 <- "entry"
time2 <- "exit"
event <- "event"
names <- c("dose0", "dose1", "dose0")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "lin")
keep_constant <- c(0, 0, 0)
# a_n <- c(0.2462, 5.020, -0.5909)
a_n <- c(-1.493177, 5.020007, 1.438377)
modelform <- "M"
#
model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = FALSE, "alpha" = 0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(2, 2), "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)
alpha <- 0.005
a_n <- c(-1.493177, 5.020007, 1.438377)
model_control <- list("basic" = FALSE, "maxstep" = 2, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = FALSE)
expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control))
a_n <- c(-1.493177, 5.020007, 1.438377)
model_control <- list("basic" = FALSE, "maxstep" = 2, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = TRUE)
expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control))
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
alpha_list <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 5, "epsilon" = 1e-4, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)
v_lower <- c(4.98213, 4.939947, 4.901443, 4.883815, 4.825766, 4.788947, 4.756168, 4.717337, 4.690578)
# c(4.97252283668956, 4.9349945105648, 4.89804715665926, 4.88084912208962, 4.82369762341988, 4.78721237571926, 4.7546530342797, 4.71603055250556, 4.68938287303871)
v_upper <- c(5.058015, 5.100656, 5.139868, 5.157913, 5.217754, 5.256045, 5.290357, 5.331277, 5.359649)
# c(5.06762896572498, 5.10561529697034, 5.14327069556976, 5.16088604918614, 5.21982880792394, 5.2577860471215, 5.29187760184654, 5.33258757872226, 5.36084782852899)
for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
alpha <- alpha_list[alpha_i]
a_n <- c(-1.493177, 5.020007, 1.438377)
model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = TRUE)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4)
expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4)
}
v_lower <- c(1.174142, 0.8706736, 0.565792, 0.4011414, -0.2129912, 0.2830731, -0.07262359, -0.1306881, 0.3065214)
# c(-0.643365949558998, -0.677336655540846, -0.706075250211414, -0.718165409196492, -0.753647332793819, -0.773208334303991, -0.789018704115451, -0.806061085000755, -0.816875114954096)
v_upper <- c(1.702503, 2.0018595, 2.283068, 2.4149146, 2.8653084, 3.1659745, 3.44475771, 3.7898521, 4.0379529)
# c(-0.521472203247917, -0.444964438813732, -0.327862977142017, -0.235044092073815, 2.91573713669059, 3.21014641617297, 3.48490803194128, 3.82648584413642, 4.07272009904963)
for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
alpha <- alpha_list[alpha_i]
a_n <- c(-1.493177, 5.020007, 1.438377)
model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 2, "manual" = TRUE)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
# expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4)
expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4)
}
})
test_that("Poisson, lin both", {
fname <- "base_example.csv"
df <- fread(fname)
pyr <- "exit"
event <- "event"
names <- c("dose0", "dose1")
term_n <- c(0, 1)
tform <- c("loglin", "lin")
keep_constant <- c(0, 0)
a_n <- c(-2.917, 0.06526)
modelform <- "M"
#
model_control <- list("basic" = FALSE, "maxstep" = 10, "log_bound" = FALSE, "alpha" = 0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)
alpha <- 0.005
a_n <- c(-2.917, 0.06526)
model_control <- list("basic" = FALSE, "maxstep" = 3, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = FALSE)
expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control))
a_n <- c(-2.917, 0.06526)
model_control <- list("basic" = FALSE, "maxstep" = 3, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = TRUE)
expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control))
})
test_that("Coxph, lin both, check ordering", {
fname <- "base_example.csv"
df <- fread(fname)
time1 <- "entry"
time2 <- "exit"
event <- "event"
names <- c("dose0", "dose1", "dose0")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "lin")
keep_constant <- c(0, 0, 0)
# a_n <- c(0.2462, 5.020, -0.5909)
a_n <- c(0.2462, 5.020, -0.7)
modelform <- "M"
#
model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = FALSE, "alpha" = 0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)
alpha <- 0.005
a_n <- c(0.2462, 5.020, -0.599)
model_control <- list("basic" = FALSE, "maxstep" = 5, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = FALSE)
expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control))
a_n <- c(0.2462, 5.020, -0.599)
model_control <- list("basic" = FALSE, "maxstep" = 5, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = TRUE)
expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control))
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
alpha_list <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 5, "epsilon" = 1e-4, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)
v_lower <- c(4.97252283668956, 4.9349945105648, 4.89804715665926, 4.88084912208962, 4.82369762341988, 4.78721237571926, 4.7546530342797, 4.71603055250556, 4.68938287303871)
v_upper <- c(5.06762896572498, 5.10561529697034, 5.14327069556976, 5.16088604918614, 5.21982880792394, 5.2577860471215, 5.29187760184654, 5.33258757872226, 5.36084782852899)
alpha <- alpha_list[1]
options(warn = -1)
### ----------------------------------------- ###
names <- c("dose0", "dose1", "dose0")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "lin")
a_n <- c(0.2462, 5.020, -0.599)
model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = TRUE)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[1], tolerance = 1e-4)
expect_equal(a[2], v_upper[1], tolerance = 1e-4)
### ----------------------------------------- ###
names <- c("dose1", "dose0", "dose0")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "lin")
a_n <- c(5.020, 0.2462, -0.599)
model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 0, "manual" = TRUE)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[1], tolerance = 1e-4)
expect_equal(a[2], v_upper[1], tolerance = 1e-4)
### ----------------------------------------- ###
names <- c("dose0", "dose0", "dose1")
term_n <- c(0, 1, 0)
tform <- c("loglin", "lin", "loglin")
a_n <- c(0.2462, -0.599, 5.020)
model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 2, "manual" = TRUE)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[1], tolerance = 1e-4)
expect_equal(a[2], v_upper[1], tolerance = 1e-4)
})
test_that("Coxph, lin both, curve search", {
fname <- "base_example.csv"
df <- fread(fname)
time1 <- "entry"
time2 <- "exit"
event <- "event"
names <- c("dose0", "dose1", "dose0")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "lin")
keep_constant <- c(0, 0, 0)
# a_n <- c(0.2462, 5.020, -0.5909)
a_n <- c(-1.493177, 5.020007, 1.438377)
modelform <- "M"
#
model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = FALSE, "alpha" = 0.005)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)
alpha <- 0.005
a_n <- c(-1.493177, 5.020007, 1.438377)
alpha_list <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 2, "epsilon" = 1e-4, "deriv_epsilon" = 1e-4, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
model_control <- list("basic" = FALSE, "maxstep" = 10, "log_bound" = FALSE, "alpha" = 0.005, "para_number" = 2, "step_size" = 0.5)
v_lower <- c(1.174125, 0.8706585, 0.5657879, 0.401115, -0.749947, -0.7704548, -0.7868122, -0.8043293, -0.8154377)
v_upper <- c(1.702507, 2.0018841, 2.283073, 2.414909, 2.865287, 3.1660076, 3.4447552, 3.7898479, 4.0379559)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
alpha <- alpha_list[alpha_i]
a_n <- c(-1.493177, 5.020007, 1.438377)
model_control <- list("basic" = FALSE, "maxstep" = 20, "log_bound" = FALSE, "alpha" = alpha, "para_number" = 2, "step_size" = 0.5)
e <- CoxCurveSolver(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-3)
expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-3)
}
})
test_that("Poisson, curve search", {
fname <- "base_example.csv"
df <- fread(fname)
pyr <- "exit"
event <- "event"
names <- c("dose0", "dose1")
term_n <- c(0, 1)
tform <- c("loglin", "lin")
keep_constant <- c(0, 0)
a_n <- c(-2.917, 0.06526)
modelform <- "M"
#
model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = FALSE, "alpha" = 0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 5, "epsilon" = 1e-4, "deriv_epsilon" = 1e-4, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
alpha_list <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005)
v_lower <- c(0.05418212, 0.04221923, 0.03135497, 0.02635009, 0.0100537, -0.0002002051, -0.009355479, -0.02009767, -0.02748292)
v_upper <- c(0.07591063, 0.0881787, 0.09953124, 0.10471923, 0.1221753, 0.1333447168, 0.143415518, 0.15550048, 0.16386229)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
alpha <- alpha_list[alpha_i]
a_n <- c(-2.917, 0.06526)
model_control <- list("basic" = FALSE, "maxstep" = 20, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = FALSE)
e <- PoissonCurveSolver(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-3)
expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-3)
}
})
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test_that("Coxph strata_basic_single_CR_null log_bound", {
fname <- "ll_comp_0.csv"
colTypes <- c("double", "double", "double", "integer", "integer")
df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
time1 <- "t0"
time2 <- "t1"
df$censor <- (df$lung == 0)
event <- "censor"
names <- c("dose", "fac")
term_n <- c(0, 0)
tform <- c("loglin", "loglin")
keep_constant <- c(1, 0)
a_n <- c(0, 0)
modelform <- "M"
control <- list("ncores" = 2, "lr" = 0.75, "maxiter" = 20, "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
plot_options <- list("name" = paste(tempfile(), "run", sep = ""), "verbose" = FALSE, "studyid" = "studyid", "age_unit" = "years")
dft <- GetCensWeight(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, control, plot_options)
#
#
t_ref <- dft$t
surv_ref <- dft$surv
t_c <- df$t1
cens_weight <- approx(t_ref, surv_ref, t_c, rule = 2)$y
df$weighting <- cens_weight
#
event <- "lung"
a_n <- c(-0.1, -0.1)
keep_constant <- c(0, 0)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(-1, -1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
verbose <- FALSE
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
for (i in c(TRUE, FALSE)) {
for (j in c(TRUE, FALSE)) {
for (k in c(FALSE, FALSE)) {
for (l in c(TRUE, FALSE)) {
for (m in c(TRUE, FALSE)) {
model_control <- list("strata" = i, "basic" = j, "single" = k, "cr" = l, "log_bound" = TRUE, "manual" = m)
a_n <- c(-0.1, -0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 1, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting"))
a_n <- c(-0.1, -0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 1, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "efron", "double_step" = 0)
expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting"))
}
}
}
}
}
for (m in c(TRUE, FALSE)) {
model_control <- list("null" = TRUE, "log_bound" = TRUE, "manual" = m)
a_n <- c(-0.1, -0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
expect_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting"))
model_control <- list("single" = TRUE, "log_bound" = TRUE, "manual" = m)
expect_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting"))
model_control <- list("gradient" = TRUE, "log_bound" = TRUE, "manual" = m)
expect_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting"))
}
})
test_that("Poisson strata_single log_bound", {
fname <- "ll_comp_0.csv"
colTypes <- c("double", "double", "double", "integer", "integer")
df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
df$pyr <- df$t1 - df$t0
time1 <- "t0"
time2 <- "t1"
pyr <- "pyr"
df$censor <- (df$lung == 0)
event <- "censor"
names <- c("dose", "fac")
term_n <- c(0, 0)
tform <- c("loglin", "loglin")
keep_constant <- c(1, 0)
a_n <- c(0, 0)
modelform <- "M"
control <- list("ncores" = 2, "lr" = 0.75, "maxiter" = 20, "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
#
event <- "lung"
a_n <- c(-0.1, -0.1)
keep_constant <- c(0, 0)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(-1, -1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
verbose <- FALSE
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
for (i in c(TRUE, FALSE)) {
for (k in c(FALSE, FALSE)) {
for (m in c(TRUE, FALSE)) {
model_control <- list("strata" = i, "single" = k, "log_bound" = TRUE, "manual" = m)
a_n <- c(-0.1, -0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control))
}
}
}
for (m in c(TRUE, FALSE)) {
for (k in c(TRUE)) {
model_control <- list("strata" = FALSE, "single" = k, "log_bound" = TRUE, "manual" = m)
a_n <- c(-0.1, -0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control))
model_control <- list("strata" = FALSE, "gradient" = k, "log_bound" = TRUE, "manual" = m)
a_n <- c(-0.1, -0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control))
}
}
})
test_that("Coxph EPICURE validated answers, loglin", {
fname <- "base_example.csv"
df <- fread(fname)
time1 <- "entry"
time2 <- "exit"
event <- "event"
names <- c("dose0", "dose1")
term_n <- c(0, 0)
tform <- c("loglin", "loglin")
keep_constant <- c(0, 0)
a_n <- c(0, 0)
modelform <- "M"
#
a_n <- c(-0.6067, 5.019)
model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = 0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
v_lower <- c(-0.6305960, -0.6572672, -0.6817293, -0.6929630, -0.7300938, -0.7537744, -0.7749381, -0.8001031, -0.8175117)
v_upper <- c(-0.5828725, -0.5562505, -0.5318645, -0.5206756, -0.4837373, -0.4602148, -0.4392159, -0.4142752, -0.3970399)
alphas <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005)
for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
a_n <- c(-0.6067, 5.019)
model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = alphas[alpha_i], "para_number" = 0)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4)
expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4)
}
v_lower <- c(4.981497, 4.939337, 4.900838, 4.883211, 4.825191, 4.788380, 4.755608, 4.716794, 4.690041)
v_upper <- c(5.057414, 5.100032, 5.139239, 5.157283, 5.217094, 5.255376, 5.289680, 5.330581, 5.358945)
for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
a_n <- c(-0.6067, 5.019)
model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = alphas[alpha_i], "para_number" = 1)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4)
expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4)
}
})
test_that("Coxph EPICURE validated answers, loglin manual", {
fname <- "base_example.csv"
df <- fread(fname)
time1 <- "entry"
time2 <- "exit"
event <- "event"
names <- c("dose0", "dose1")
term_n <- c(0, 0)
tform <- c("loglin", "loglin")
keep_constant <- c(0, 0)
a_n <- c(0, 0)
modelform <- "M"
#
a_n <- c(-0.6067, 5.019)
model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = 0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
v_lower <- c(-0.6305960, -0.6572672, -0.6817293, -0.6929630, -0.7300938, -0.7537744, -0.7749381, -0.8001031, -0.8175117)
v_upper <- c(-0.5828725, -0.5562505, -0.5318645, -0.5206756, -0.4837373, -0.4602148, -0.4392159, -0.4142752, -0.3970399)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
alphas <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005)
for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
a_n <- c(-0.6067, 5.019)
model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = alphas[alpha_i], "para_number" = 0, "manual" = TRUE)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4)
expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4)
}
v_lower <- c(4.981497, 4.939337, 4.900838, 4.883211, 4.825191, 4.788380, 4.755608, 4.716794, 4.690041)
v_upper <- c(5.057414, 5.100032, 5.139239, 5.157283, 5.217094, 5.255376, 5.289680, 5.330581, 5.358945)
for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
a_n <- c(-0.6067, 5.019)
model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = alphas[alpha_i], "para_number" = 1, "manual" = TRUE)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4)
expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4)
}
})
test_that("Coxph, lin both", {
fname <- "base_example.csv"
df <- fread(fname)
time1 <- "entry"
time2 <- "exit"
event <- "event"
names <- c("dose0", "dose1", "dose0")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "lin")
keep_constant <- c(0, 0, 0)
# a_n <- c(0.2462, 5.020, -0.5909)
a_n <- c(-1.493177, 5.020007, 1.438377)
modelform <- "M"
#
model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = FALSE, "alpha" = 0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(2, 2), "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)
alpha <- 0.005
a_n <- c(-1.493177, 5.020007, 1.438377)
model_control <- list("basic" = FALSE, "maxstep" = 2, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = FALSE)
expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control))
a_n <- c(-1.493177, 5.020007, 1.438377)
model_control <- list("basic" = FALSE, "maxstep" = 2, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = TRUE)
expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control))
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
alpha_list <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 5, "epsilon" = 1e-4, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)
v_lower <- c(4.98213, 4.939947, 4.901443, 4.883815, 4.825766, 4.788947, 4.756168, 4.717337, 4.690578)
# c(4.97252283668956, 4.9349945105648, 4.89804715665926, 4.88084912208962, 4.82369762341988, 4.78721237571926, 4.7546530342797, 4.71603055250556, 4.68938287303871)
v_upper <- c(5.058015, 5.100656, 5.139868, 5.157913, 5.217754, 5.256045, 5.290357, 5.331277, 5.359649)
# c(5.06762896572498, 5.10561529697034, 5.14327069556976, 5.16088604918614, 5.21982880792394, 5.2577860471215, 5.29187760184654, 5.33258757872226, 5.36084782852899)
for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
alpha <- alpha_list[alpha_i]
a_n <- c(-1.493177, 5.020007, 1.438377)
model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = TRUE)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4)
expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4)
}
v_lower <- c(1.174142, 0.8706736, 0.565792, 0.4011414, -0.2129912, 0.2830731, -0.07262359, -0.1306881, 0.3065214)
# c(-0.643365949558998, -0.677336655540846, -0.706075250211414, -0.718165409196492, -0.753647332793819, -0.773208334303991, -0.789018704115451, -0.806061085000755, -0.816875114954096)
v_upper <- c(1.702503, 2.0018595, 2.283068, 2.4149146, 2.8653084, 3.1659745, 3.44475771, 3.7898521, 4.0379529)
# c(-0.521472203247917, -0.444964438813732, -0.327862977142017, -0.235044092073815, 2.91573713669059, 3.21014641617297, 3.48490803194128, 3.82648584413642, 4.07272009904963)
for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
alpha <- alpha_list[alpha_i]
a_n <- c(-1.493177, 5.020007, 1.438377)
model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 2, "manual" = TRUE)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
# expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4)
expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4)
}
})
test_that("Poisson, lin both", {
fname <- "base_example.csv"
df <- fread(fname)
pyr <- "exit"
event <- "event"
names <- c("dose0", "dose1")
term_n <- c(0, 1)
tform <- c("loglin", "lin")
keep_constant <- c(0, 0)
a_n <- c(-2.917, 0.06526)
modelform <- "M"
#
model_control <- list("basic" = FALSE, "maxstep" = 10, "log_bound" = FALSE, "alpha" = 0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)
alpha <- 0.005
a_n <- c(-2.917, 0.06526)
model_control <- list("basic" = FALSE, "maxstep" = 3, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = FALSE)
expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control))
a_n <- c(-2.917, 0.06526)
model_control <- list("basic" = FALSE, "maxstep" = 3, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = TRUE)
expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control))
})
test_that("Coxph, lin both, check ordering", {
fname <- "base_example.csv"
df <- fread(fname)
time1 <- "entry"
time2 <- "exit"
event <- "event"
names <- c("dose0", "dose1", "dose0")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "lin")
keep_constant <- c(0, 0, 0)
# a_n <- c(0.2462, 5.020, -0.5909)
a_n <- c(0.2462, 5.020, -0.7)
modelform <- "M"
#
model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = FALSE, "alpha" = 0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)
alpha <- 0.005
a_n <- c(0.2462, 5.020, -0.599)
model_control <- list("basic" = FALSE, "maxstep" = 5, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = FALSE)
expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control))
a_n <- c(0.2462, 5.020, -0.599)
model_control <- list("basic" = FALSE, "maxstep" = 5, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = TRUE)
expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control))
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
alpha_list <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 5, "epsilon" = 1e-4, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)
v_lower <- c(4.97252283668956, 4.9349945105648, 4.89804715665926, 4.88084912208962, 4.82369762341988, 4.78721237571926, 4.7546530342797, 4.71603055250556, 4.68938287303871)
v_upper <- c(5.06762896572498, 5.10561529697034, 5.14327069556976, 5.16088604918614, 5.21982880792394, 5.2577860471215, 5.29187760184654, 5.33258757872226, 5.36084782852899)
alpha <- alpha_list[1]
options(warn = -1)
### ----------------------------------------- ###
names <- c("dose0", "dose1", "dose0")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "lin")
a_n <- c(0.2462, 5.020, -0.599)
model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = TRUE)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[1], tolerance = 1e-4)
expect_equal(a[2], v_upper[1], tolerance = 1e-4)
### ----------------------------------------- ###
names <- c("dose1", "dose0", "dose0")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "lin")
a_n <- c(5.020, 0.2462, -0.599)
model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 0, "manual" = TRUE)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[1], tolerance = 1e-4)
expect_equal(a[2], v_upper[1], tolerance = 1e-4)
### ----------------------------------------- ###
names <- c("dose0", "dose0", "dose1")
term_n <- c(0, 1, 0)
tform <- c("loglin", "lin", "loglin")
a_n <- c(0.2462, -0.599, 5.020)
model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 2, "manual" = TRUE)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[1], tolerance = 1e-4)
expect_equal(a[2], v_upper[1], tolerance = 1e-4)
})
test_that("Coxph, lin both, curve search", {
fname <- "base_example.csv"
df <- fread(fname)
time1 <- "entry"
time2 <- "exit"
event <- "event"
names <- c("dose0", "dose1", "dose0")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "lin")
keep_constant <- c(0, 0, 0)
# a_n <- c(0.2462, 5.020, -0.5909)
a_n <- c(-1.493177, 5.020007, 1.438377)
modelform <- "M"
#
model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = FALSE, "alpha" = 0.005)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 5, "epsilon" = 1e-3, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10)
alpha <- 0.005
a_n <- c(-1.493177, 5.020007, 1.438377)
alpha_list <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 2, "epsilon" = 1e-4, "deriv_epsilon" = 1e-4, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
model_control <- list("basic" = FALSE, "maxstep" = 10, "log_bound" = FALSE, "alpha" = 0.005, "para_number" = 2, "step_size" = 0.5)
v_lower <- c(1.174125, 0.8706585, 0.5657879, 0.401115, -0.749947, -0.7704548, -0.7868122, -0.8043293, -0.8154377)
v_upper <- c(1.702507, 2.0018841, 2.283073, 2.414909, 2.865287, 3.1660076, 3.4447552, 3.7898479, 4.0379559)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
alpha <- alpha_list[alpha_i]
a_n <- c(-1.493177, 5.020007, 1.438377)
model_control <- list("basic" = FALSE, "maxstep" = 20, "log_bound" = FALSE, "alpha" = alpha, "para_number" = 2, "step_size" = 0.5)
e <- CoxCurveSolver(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "nan", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-3)
expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-3)
}
})
test_that("Poisson, curve search", {
fname <- "base_example.csv"
df <- fread(fname)
pyr <- "exit"
event <- "event"
names <- c("dose0", "dose1")
term_n <- c(0, 1)
tform <- c("loglin", "lin")
keep_constant <- c(0, 0)
a_n <- c(-2.917, 0.06526)
modelform <- "M"
#
model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = FALSE, "alpha" = 0.1)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 5, "epsilon" = 1e-4, "deriv_epsilon" = 1e-4, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
alpha_list <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005)
v_lower <- c(0.05418212, 0.04221923, 0.03135497, 0.02635009, 0.0100537, -0.0002002051, -0.009355479, -0.02009767, -0.02748292)
v_upper <- c(0.07591063, 0.0881787, 0.09953124, 0.10471923, 0.1221753, 0.1333447168, 0.143415518, 0.15550048, 0.16386229)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
for (alpha_i in c(1, 5)) { # seq_along(alphas)) {
alpha <- alpha_list[alpha_i]
a_n <- c(-2.917, 0.06526)
model_control <- list("basic" = FALSE, "maxstep" = 20, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = FALSE)
e <- PoissonCurveSolver(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control)
a <- e$Parameter_Limits
expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-3)
expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-3)
}
})
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