Nothing
tests/testthat/test-Omnibus.R
In Colossus: "Risk Model Regression and Analysis with Complex Non-Linear Models"
test_that("Coxph basic_single_null match", {
fname <- "ll_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)
time1 <- "t0"
time2 <- "t1"
event <- "lung"
names <- c("dose")
term_n <- c(0)
tform <- c("loglin")
keep_constant <- c(0)
a_n <- c(0.0)
modelform <- "M"
verbose <- FALSE
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)
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "null" = FALSE)
e0 <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = c("loglin"), keep_constant = keep_constant, a_n = a_n, modelform = "M", control = control, strat_col = "fac", model_control = model_control)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
for (j in c(TRUE, FALSE)) {
for (k in c(TRUE, FALSE)) {
for (l in c(TRUE, FALSE)) {
model_control <- list("strata" = FALSE, "basic" = j, "single" = k, "null" = l)
a_n <- c(0.0)
e1 <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = c("loglin"), keep_constant = keep_constant, a_n = a_n, modelform = "M", control = control, strat_col = "fac", model_control = model_control)
expect_equal(e0$LogLik, e1$LogLik, tolerance = 1e-2)
}
}
}
model_control <- list("strata" = TRUE, "basic" = FALSE, "single" = FALSE, "null" = FALSE)
e0 <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = c("loglin"), keep_constant = keep_constant, a_n = a_n, modelform = "M", control = control, strat_col = "fac", model_control = model_control)
for (j in c(TRUE, FALSE)) {
for (k in c(TRUE, FALSE)) {
for (l in c(TRUE, FALSE)) {
model_control <- list("strata" = TRUE, "basic" = j, "single" = k, "null" = l)
a_n <- c(0.0)
e1 <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = c("loglin"), keep_constant = keep_constant, a_n = a_n, modelform = "M", control = control, strat_col = "fac", model_control = model_control)
expect_equal(e0$LogLik, e1$LogLik, tolerance = 1e-2)
}
}
}
})
test_that("Coxph strata_basic_single_CR", {
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$lung == 0)
event <- "censor"
names <- c("dose", "fac")
term_n <- c(0, 0)
tform <- c("loglin", "loglin")
keep_constant <- c(1, 0)
a_n <- c(0, 0)
modelform <- "M"
control <- list("ncores" = 2, "lr" = 0.75, "maxiter" = 20, "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
plot_options <- list("name" = paste(tempfile(), "run", sep = ""), "verbose" = FALSE, "studyid" = "studyid", "age_unit" = "years")
dft <- GetCensWeight(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, control, plot_options)
#
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
#
t_ref <- dft$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
j_iterate <- 1
LL_comp <- c(-81.12079, -81.12079, -77.97632, -77.97632, -68.60263, -68.67108, -75.34028, -75.3691, -81.12079, -81.12079, -77.97632, -77.97632, -68.60263, -68.67108, -75.34028, -75.3691, -122.8909, -122.8909, -119.9814, -119.9814, -109.6211, -109.6742, -117.0147, -117.0539, -122.8909, -122.8909, -119.9814, -119.9814, -109.6211, -109.6742, -117.0147, -117.0539)
dev_comp <- c(0, 0, 0, 0, 0.6850986, 0.6804879, 0.7627068, 0.7600753, 0, 0, 0, 0, 0.6850986, 0.6804879, 0.7627068, 0.7600753, 0, 0, 0, 0, 0.6720932, 0.6688576, 0.7401029, 0.7389506, 0, 0, 0, 0, 0.6720932, 0.6688576, 0.7401029, 0.7389506)
for (i in c(TRUE, FALSE)) {
for (j in c(TRUE, FALSE)) {
for (k in c(TRUE, FALSE)) {
for (l in c(TRUE, FALSE)) {
model_control <- list("strata" = i, "basic" = j, "single" = k, "cr" = l)
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)
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 = "rand", model_control = model_control, cens_weight = "weighting")
expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-3)
if (k == FALSE) {
expect_equal(sum(e$Standard_Deviation), dev_comp[j_iterate], tolerance = 1e-3)
}
j_iterate <- j_iterate + 1
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" = "efron", "double_step" = 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 = "rand", model_control = model_control, cens_weight = "weighting")
expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-3)
if (k == FALSE) {
expect_equal(sum(e$Standard_Deviation), dev_comp[j_iterate], tolerance = 1e-3)
}
j_iterate <- j_iterate + 1
}
}
}
}
})
test_that("Pois strata_single", {
fname <- "ll_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)
time1 <- "t0"
df$pyr <- df$t1 - df$t0
pyr <- "pyr"
event <- "lung"
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
names <- c("dose", "rand", "rand")
term_n <- c(2, 1, 0)
tform <- c("loglin", "lin", "plin")
keep_constant <- c(0, 0, 0)
a_n <- c(0.01, 0.1, 0.1)
modelform <- "PAE"
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" = 0)
strat_col <- "fac"
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
verbose <- FALSE
j_iterate <- 1
LL_comp <- c(-468.7465, -464.9279, -461.2769, -462.1182, -3033.332, -2734.64, -992.622, -1334.36)
for (i in c(TRUE, FALSE)) {
for (j in c(TRUE, FALSE)) {
model_control <- list("strata" = i, "single" = j)
a_n <- c(0.01, 0.1, 0.1)
modelform <- "PAE"
e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control)
expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
j_iterate <- j_iterate + 1
a_n <- c(0.01, 0.1, 0.1)
modelform <- "A"
e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control)
expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
j_iterate <- j_iterate + 1
}
}
})
test_that("Pois comb_forms", {
fname <- "ll_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)
time1 <- "t0"
df$pyr <- df$t1 - df$t0
pyr <- "pyr"
event <- "lung"
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 1, max = 5))
names <- c("dose", "rand", "rand", "dose", "dose")
term_n <- c(1, 0, 0, 0, 0)
tform <- c("loglin", "lin", "plin", "loglin_slope", "loglin_top")
keep_constant <- c(0, 0, 0, 0, 0)
a_n <- c(0.01, 0.1, 0.1, 1.0, 0.1)
modelform <- "PAE"
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" = 0)
strat_col <- "fac"
verbose <- FALSE
modelforms <- c("A", "PAE", "M", "PA")
j_iterate <- 1
LL_comp <- c(-790.3591, -471.0312, -471.0312, -463.1375, -707.56, -678.2228, -678.2228, -471.4805)
for (modelform in modelforms) {
model_control <- list("strata" = FALSE, "single" = FALSE)
a_n <- c(0.01, 0.1, 0.1, 1.0, 0.1)
e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control)
expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
j_iterate <- j_iterate + 1
}
term_n <- c(1, 1, 1, 0, 0)
for (modelform in modelforms) {
model_control <- list("strata" = FALSE, "single" = FALSE)
a_n <- c(0.01, 0.1, 0.1, 1.0, 0.1)
e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control)
expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
j_iterate <- j_iterate + 1
}
})
test_that("Pois strata_single expanded", {
fname <- "ll_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)
time1 <- "t0"
df$pyr <- df$t1 - df$t0
pyr <- "pyr"
event <- "lung"
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
names <- c("dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand")
term_n <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
tform <- c("loglin_slope", "loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope", "loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope")
keep_constant <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
a_n <- c(1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1)
modelform <- "PAE"
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" = 0)
strat_col <- "fac"
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
verbose <- FALSE
j_iterate <- 1
LL_comp <- c(-496.736611833105, -475.421339181129, -466.96134309201, -463.218078300204, -4497.1782941901, -3577.95315136081, -2557.93228186619, -2350.19459732101)
for (i in c(TRUE, FALSE)) {
for (j in c(TRUE, FALSE)) {
model_control <- list("strata" = i, "single" = j)
a_n <- c(1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1)
modelform <- "PAE"
e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control)
expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
j_iterate <- j_iterate + 1
a_n <- c(1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1)
modelform <- "A"
e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control)
expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
j_iterate <- j_iterate + 1
}
}
})
test_that("risk check omnibus plain", {
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$lung == 0)
#
event <- "lung"
names <- c("dose", "fac", "dose", "fac", "rand")
term_n <- c(0, 0, 1, 1, 1)
tform <- c("loglin", "lin", "lin", "plin", "loglin")
keep_constant <- c(0, 0, 0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2, 0.3, -0.5)
modelform <- "M"
cens_weight <- c(0)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
verbose <- FALSE
df_order <- data.table("term_n" = term_n, "tform" = tform, "keep_constant" = keep_constant, "a_n" = a_n, "names" = names, "order" = 1:5)
model_list <- c("M", "A", "PA", "PAE")
means <- c(0.053270537, 0.053270537, 0.053270537, 0.053270537, 0.053270537, 0.223545566, 0.223545566, 0.223545566, 0.223545566, 0.223545566, 0.007932817, 0.007932817, 0.007932817, 0.007932817, 0.007932817, 0.053270537, 0.053270537, 0.053270537, 0.053270537, 0.053270537)
medians <- c(0.08229779, 0.08229779, 0.08229779, 0.08229779, 0.08229779, 0.15570474, 0.15570474, 0.15570474, 0.15570474, 0.15570474, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.08229779, 0.08229779, 0.08229779, 0.08229779, 0.08229779)
sums <- c(53.270537, 53.270537, 53.270537, 53.270537, 53.270537, 223.545566, 223.545566, 223.545566, 223.545566, 223.545566, 7.932817, 7.932817, 7.932817, 7.932817, 7.932817, 53.270537, 53.270537, 53.270537, 53.270537, 53.270537)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
for (model_i in 1:4) {
modelform <- model_list[model_i]
for (i in 1:5) {
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
#
df_order$order <- sample(df_order$order)
setorderv(df_order, c("order"))
term_n <- df_order$term_n
tform <- df_order$tform
keep_constant <- df_order$keep_constant
a_n <- df_order$a_n
names <- df_order$names
#
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)
e <- Cox_Relative_Risk(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, model_control = model_control)$Risk
a_i <- (model_i - 1) * 5 + i
expect_equal(mean(e), means[a_i], tolerance = 1e-2)
expect_equal(median(e), medians[a_i], tolerance = 1e-2)
expect_equal(sum(e), sums[a_i], tolerance = 1e-2)
}
}
})
test_that("risk check omnibus gmix", {
fname <- "ll_comp_0.csv"
colTypes <- c("double", "double", "double", "integer", "integer")
df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
time1 <- "t0"
time2 <- "t1"
df$censor <- (df$lung == 0)
#
event <- "lung"
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
model_list <- c("GMIX-R", "GMIX-E", "GMIX")
names <- c("dose", "fac", "dose", "fac", "rand")
term_n <- c(0, 0, 1, 1, 2)
tform <- c("loglin", "loglin", "plin", "plin", "loglin")
keep_constant <- c(0, 0, 0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2, 0.3, -0.5)
df_order <- data.table("term_n" = term_n, "tform" = tform, "keep_constant" = keep_constant, "a_n" = a_n, "names" = names, "order" = 1:5)
means <- c(0.6918334, 2.9080098, 0.6918334, 1.2728663, 1.8214142, 2.9080098)
medians <- c(0.5871897, 2.8144283, 0.5871897, 1.0226682, 1.7472830, 2.8144283)
sums <- c(691.8334, 2908.0098, 691.8334, 1272.8663, 1821.4142, 2908.0098)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
count <- 0
for (model_i in 1:3) {
modelform <- model_list[model_i]
if (modelform == "GMIX") {
for (term_i in 0:3) {
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
model_control$gmix_term <- c(0, term_i %% 2, floor(term_i / 2))
#
df_order$order <- sample(df_order$order)
setorderv(df_order, c("order"))
term_n <- df_order$term_n
tform <- df_order$tform
keep_constant <- df_order$keep_constant
a_n <- df_order$a_n
names <- df_order$names
#
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)
e <- Cox_Relative_Risk(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, model_control = model_control)$Risk
count <- count + 1
expect_equal(mean(e), means[count], tolerance = 1e-2)
expect_equal(median(e), medians[count], tolerance = 1e-2)
expect_equal(sum(e), sums[count], tolerance = 1e-2)
}
} else {
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
#
df_order$order <- sample(df_order$order)
setorderv(df_order, c("order"))
term_n <- df_order$term_n
tform <- df_order$tform
keep_constant <- df_order$keep_constant
a_n <- df_order$a_n
names <- df_order$names
#
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)
e <- Cox_Relative_Risk(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, model_control = model_control)$Risk
count <- count + 1
expect_equal(mean(e), means[count], tolerance = 1e-2)
expect_equal(median(e), medians[count], tolerance = 1e-2)
expect_equal(sum(e), sums[count], tolerance = 1e-2)
}
}
})
test_that("gmix omnibus use", {
fname <- "ll_comp_0.csv"
colTypes <- c("double", "double", "double", "integer", "integer")
df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
time1 <- "t0"
time2 <- "t1"
df$censor <- (df$lung == 0)
#
event <- "lung"
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
model_list <- c("GMIX-R", "GMIX-E", "GMIX")
names <- c("dose", "fac", "dose", "fac", "rand")
term_n <- c(0, 0, 1, 1, 2)
tform <- c("loglin", "loglin", "plin", "plin", "loglin")
keep_constant <- c(0, 0, 0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2, 0.3, -0.5)
df_order <- data.table("term_n" = term_n, "tform" = tform, "keep_constant" = keep_constant, "a_n" = a_n, "names" = names, "order" = 1:5)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
options(warn = -1)
count <- 0
cens_weight <- c(0)
for (model_i in 1:3) {
modelform <- model_list[model_i]
if (modelform == "GMIX") {
for (term_i in 0:3) {
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
model_control$gmix_term <- c(0, term_i %% 2, floor(term_i / 2))
#
df_order$order <- sample(df_order$order)
setorderv(df_order, c("order"))
term_n <- df_order$term_n
tform <- df_order$tform
keep_constant <- df_order$keep_constant
a_n <- df_order$a_n
names <- df_order$names
#
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(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 = "null"))
}
} else {
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
#
df_order$order <- sample(df_order$order)
setorderv(df_order, c("order"))
term_n <- df_order$term_n
tform <- df_order$tform
keep_constant <- df_order$keep_constant
a_n <- df_order$a_n
names <- df_order$names
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control, cens_weight = "null"))
}
}
})
test_that("risk check omnibus dose", {
fname <- "ll_comp_0.csv"
colTypes <- c("double", "double", "double", "integer", "integer")
df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
time1 <- "t0"
time2 <- "t1"
df$censor <- (df$lung == 0)
#
event <- "lung"
names <- c("dose", "fac", "dose", "fac", "rand")
term_n <- c(0, 0, 1, 1, 1)
tform <- c("loglin", "lin", "lin", "plin", "loglin")
keep_constant <- c(0, 0, 0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2, 0.3, -0.5)
modelform <- "M"
cens_weight <- c(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
df_order <- data.table("term_n" = term_n, "tform" = tform, "keep_constant" = keep_constant, "a_n" = a_n, "names" = names, "order" = 1:5)
model_list <- c("M", "A", "PA", "PAE")
names <- c("dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "fac", "fac", "fac", "fac", "fac", "fac", "fac", "fac", "fac", "fac", "fac")
term_n <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
tform <- c("loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope", "loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope")
keep_constant <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
a_n <- c(-0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1)
df_order <- data.table("term_n" = term_n, "tform" = tform, "keep_constant" = keep_constant, "a_n" = a_n, "names" = names, "order" = 1:22)
means <- c(3.268830, 2.672975, 1.753389, 3.268830)
medians <- c(2.898413, 2.558679, 1.636409, 2.898413)
sums <- c(3268.830, 2672.975, 1753.389, 3268.830)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
for (model_i in 1:4) {
modelform <- model_list[model_i]
for (i in 1:5) {
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
#
df_order$order <- sample(df_order$order)
setorderv(df_order, c("order"))
term_n <- df_order$term_n
tform <- df_order$tform
keep_constant <- df_order$keep_constant
a_n <- df_order$a_n
names <- df_order$names
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
e <- Cox_Relative_Risk(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, model_control = model_control)$Risk
expect_equal(mean(e), means[model_i], tolerance = 1e-2)
expect_equal(median(e), medians[model_i], tolerance = 1e-2)
expect_equal(sum(e), sums[model_i], tolerance = 1e-2)
}
}
})
test_that("Coxph relative risk combinations", {
a <- c(0, 1, 2, 3, 4, 5, 6)
b <- c(1, 2, 3, 4, 5, 6, 7)
c <- c(0, 1, 0, 0, 0, 1, 0)
d <- c(3, 4, 5, 6, 7, 8, 9)
df <- data.table("a" = a, "b" = b, "c" = c, "d" = d)
time1 <- "a"
time2 <- "b"
event <- "c"
names <- c("d")
term_n <- c(0)
tform <- c("loglin")
keep_constant <- c(0)
a_n <- c(-0.1)
modelform <- "M"
control <- list("ncores" = 2, "lr" = 0.75, "maxiter" = -1, "halfmax" = 5, "epsilon" = 1e-9, "deriv_epsilon" = 1e-9, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
keep_constant <- c(1)
expect_error(Cox_Relative_Risk(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, control))
keep_constant <- c(0)
names <- c("d", "CONST")
term_n <- c(0, 0)
tform <- c("loglin", "loglin")
keep_constant <- c(0, 0)
a_n <- c(-0.1, 0.1)
expect_no_error(Cox_Relative_Risk(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, control))
})
test_that("check deviation calc, expected cox", {
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$rand <- floor(runif(nrow(df), min = 0, max = 5))
time1 <- "t0"
time2 <- "t1"
event <- "lung"
names <- c("dose", "fac", "rand")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "loglin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2)
modelform <- "M"
cens_weight <- c(0)
verbose <- FALSE
devs <- c()
modelform <- "M"
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
for (i in 1:3) {
a_n <- c(0.6465390, 0.4260961, 0.1572781)
keep_constant <- c(0, 0, 0)
keep_constant[i] <- 1
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "fac", model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
}
a_n <- c(0.6465390, 0.4260961, 0.1572781)
keep_constant <- c(0, 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)
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 = "fac", model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
time1 <- "t0"
time2 <- "t1"
event <- "lung"
names <- c("dose", "fac", "rand")
term_n <- c(0, 0, 0)
tform <- c("loglin", "loglin", "plin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2)
for (i in 1:3) {
a_n <- c(0.6428582, 0.4240752, 0.1507817)
keep_constant <- c(0, 0, 0)
keep_constant[i] <- 1
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "fac", model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
}
a_n <- c(0.6428582, 0.4240752, 0.1507817)
keep_constant <- c(0, 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)
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 = "fac", model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
expect_equal(devs, c(0.61445, 0.54101, 0.73858, 0.95015, 0.63646, 0.56292, 0.73815, 0.97195), tolerance = 1e-4)
})
test_that("check deviation calc, Observed cox", {
fname <- "ll_comp_0.csv"
colTypes <- c("double", "double", "double", "integer", "integer")
df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
time1 <- "t0"
time2 <- "t1"
event <- "lung"
names <- c("dose", "fac", "rand")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "loglin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2)
modelform <- "M"
cens_weight <- c(0)
verbose <- FALSE
devs <- c()
modelform <- "M"
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE, "oberved_info" = TRUE)
for (i in 1:3) {
a_n <- c(0.6465390, 0.4260961, 0.1572781)
keep_constant <- c(0, 0, 0)
keep_constant[i] <- 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)
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 = "fac", model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
}
a_n <- c(0.6465390, 0.4260961, 0.1572781)
keep_constant <- c(0, 0, 0)
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "fac", model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
time1 <- "t0"
time2 <- "t1"
event <- "lung"
names <- c("dose", "fac", "rand")
term_n <- c(0, 0, 0)
tform <- c("loglin", "loglin", "plin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2)
for (i in 1:3) {
a_n <- c(0.6428582, 0.4240752, 0.1507817)
keep_constant <- c(0, 0, 0)
keep_constant[i] <- 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)
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 = "fac", model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
}
a_n <- c(0.6428582, 0.4240752, 0.1507817)
keep_constant <- c(0, 0, 0)
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "fac", model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
expect_equal(devs, c(0.6091269, 0.5356671, 0.7385757, 0.9448081, 0.7051473, 0.5838560, 0.7381538, 0.9897501), tolerance = 1e-4)
})
test_that("check Linear Constraints", {
fname <- "l_pl_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)
df$pyr <- df$t1 - df$t0
time1 <- "t0"
time2 <- "t1"
pyr <- "pyr"
event <- "lung"
names <- c("dose", "fac")
term_n <- c(0, 0)
tform <- c("loglin", "plin")
keep_constant <- c(0, 0)
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "null" = FALSE, "constraint" = TRUE)
Constraint_Matrix <- matrix(c(1, -1), nrow = 1)
Constraint_const <- c(0.0)
set.seed(3742)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
for (i in 1:5) {
a_n <- 2 * runif(2) - 1
del <- abs(a_n[1] - a_n[2])
a_n0 <- rep(sum(a_n) / 2, 2)
a_n <- a_n0 - c(-del / 2, del / 2)
modelform <- "M"
control <- list("ncores" = 2, "lr" = 0.75, "maxiter" = 20, "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col = "fac", model_control = model_control, cons_mat = Constraint_Matrix, cons_vec = Constraint_const)
expect_equal(e$beta_0, c(0.357333, 0.357333), tolerance = 1e-2)
}
for (i in 1:5) {
a_n <- 2 * runif(2) - 1
del <- abs(a_n[1] - a_n[2])
a_n0 <- rep(sum(a_n) / 2, 2)
a_n <- a_n0 + c(-del / 2, del / 2)
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)
e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col = "fac", model_control = model_control, cons_mat = Constraint_Matrix, cons_vec = Constraint_const)
expect_equal(e$beta_0, c(-0.472812, -0.472812), tolerance = 1e-2)
}
})
test_that("check deviation calc, poisson", {
fname <- "ll_comp_0.csv"
colTypes <- c("double", "double", "double", "integer", "integer")
df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
df$pyr <- df$t1 - df$t0
pyr <- "pyr"
event <- "lung"
names <- c("dose", "fac", "rand")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "loglin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2)
modelform <- "M"
devs <- c()
modelform <- "M"
for (inma_type in c(T, F)) {
model_control <- list("oberved_info" = inma_type)
for (i in 1:3) {
a_n <- c(0.6465390, 0.4260961, 0.1572781)
keep_constant <- c(0, 0, 0)
keep_constant[i] <- 1
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
}
event <- "lung"
names <- c("dose", "fac", "rand")
term_n <- c(0, 0, 0)
tform <- c("loglin", "loglin", "plin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2)
for (i in 1:3) {
a_n <- c(0.6428582, 0.4240752, 0.1507817)
keep_constant <- c(0, 0, 0)
keep_constant[i] <- 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)
e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
}
}
expect_equal(devs, c(0.029317931, 0.014226835, 0.030171059, 0.026452308, 0.008968795, 0.040982844, 0.026119220, 0.008023552, 0.040535859, 0.026082652, 0.007801193, 0.040982844), tolerance = 1e-4)
})
test_that("Various CoxRegressionOmnibus options", {
fname <- "ll_comp_0.csv"
colTypes <- c("double", "double", "double", "integer", "integer")
df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
time1 <- "t0"
time2 <- "t1"
event <- "lung"
names <- c("rand", "fac", "dose")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "loglin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2)
modelform <- "M"
cens_weight <- c(0)
verbose <- FALSE
devs <- c()
options(warn = -1)
modelform <- "M"
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
a_n <- c(0.6465390, 0.4260961, 0.1572781)
keep_constant <- c(0, 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)
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 = "fac", model_control = model_control))
keep_constant <- c(1, 1, 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 = "fac", model_control = model_control))
lung_temp <- df$lung
df$lung <- rep(0, length(lung_temp))
keep_constant <- c(0, 0, 0)
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 = "fac", model_control = model_control))
df$lung <- lung_temp
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1, 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(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 = "fac", model_control = model_control))
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)
a_n <- list(c(0.6465390, 0.4260961, 0.1572781), c(0.6465390, 0.4260961, 0.1572781), c(0.6465390, 0.4260961, 0.1572781))
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 = "fac", model_control = model_control))
a_n <- c(0.6465390, 0.4260961, 0.1572781)
#
control <- list("ncores" = 2, "lr" = 0.75, "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" = 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 = "fac", model_control = model_control))
control <- list("ncores" = 2, "lr" = 0.75, "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)
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 = "fac", model_control = model_control))
#
names <- c("rand", "fac", "dose")
term_n <- c(0, 0, 1)
tform <- c("lin", "lin", "lin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, -0.1, 0.2)
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 = "fac", model_control = model_control))
})
test_that("Various RunPoissonRegression_Omnibus options", {
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$pyr <- df$t1 - df$t0
pyr <- "pyr"
#
event <- "lung"
names <- c("rand", "fac", "dose")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "loglin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2)
modelform <- "M"
cens_weight <- c(0)
verbose <- FALSE
devs <- c()
modelform <- "M"
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
a_n <- c(0.6465390, 0.4260961, 0.1572781)
keep_constant <- c(0, 0, 0)
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
strat_col <- "fac"
expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
keep_constant <- c(1, 1, 1)
expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
lung_temp <- df$lung
df$lung <- rep(0, length(lung_temp))
keep_constant <- c(0, 0, 0)
expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
df$lung <- lung_temp
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1, 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, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
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)
a_n <- list(c(0.6465390, 0.4260961, 0.1572781), c(0.6465390, 0.4260961, 0.1572781), c(0.6465390, 0.4260961, 0.1572781))
expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
a_n <- c(0.6465390, 0.4260961, 0.1572781)
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" = 50)
expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
#
control <- list("ncores" = 2, "lr" = 0.75, "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" = 1)
expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
control <- list("ncores" = 2, "lr" = 0.75, "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)
expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
#
names <- c("rand", "fac", "dose")
term_n <- c(0, 0, 1)
tform <- c("lin", "lin", "lin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, -0.1, 0.2)
expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
})
test_that("Pois various_fixes", {
fname <- "ll_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)
time1 <- "t0"
df$pyr <- df$t1 - df$t0
pyr <- "pyr"
event <- "lung"
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
names <- c("dose", "rand", "rand")
term_n <- c(2, 1, 0)
tform <- c("loglin", "loglin", "loglin")
keep_constant <- c(0, 0, 0)
a_n <- c(0.01, 0.1, 0.1)
modelform <- "PAE"
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" = 0)
strat_col <- "fac"
verbose <- FALSE
model_control <- list("strata" = FALSE, "single" = FALSE)
a_n <- c(0.01, 0.1, 0.1)
modelform <- "PAE"
expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
keep_constant <- c(1, 1, 1)
expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
keep_constant <- c(0, 0, 0)
ev0 <- df$lung
df$lung <- rep(0, length(ev0))
expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
names <- c("dose", "rand", "CONST")
df$lung <- ev0
expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
control$guesses <- 100
expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
})
Try the Colossus package in your browser
Any scripts or data that you put into this service are public.
Colossus documentation built on June 8, 2025, 1:10 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
test_that("Coxph basic_single_null match", {
fname <- "ll_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)
time1 <- "t0"
time2 <- "t1"
event <- "lung"
names <- c("dose")
term_n <- c(0)
tform <- c("loglin")
keep_constant <- c(0)
a_n <- c(0.0)
modelform <- "M"
verbose <- FALSE
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)
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "null" = FALSE)
e0 <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = c("loglin"), keep_constant = keep_constant, a_n = a_n, modelform = "M", control = control, strat_col = "fac", model_control = model_control)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
for (j in c(TRUE, FALSE)) {
for (k in c(TRUE, FALSE)) {
for (l in c(TRUE, FALSE)) {
model_control <- list("strata" = FALSE, "basic" = j, "single" = k, "null" = l)
a_n <- c(0.0)
e1 <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = c("loglin"), keep_constant = keep_constant, a_n = a_n, modelform = "M", control = control, strat_col = "fac", model_control = model_control)
expect_equal(e0$LogLik, e1$LogLik, tolerance = 1e-2)
}
}
}
model_control <- list("strata" = TRUE, "basic" = FALSE, "single" = FALSE, "null" = FALSE)
e0 <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = c("loglin"), keep_constant = keep_constant, a_n = a_n, modelform = "M", control = control, strat_col = "fac", model_control = model_control)
for (j in c(TRUE, FALSE)) {
for (k in c(TRUE, FALSE)) {
for (l in c(TRUE, FALSE)) {
model_control <- list("strata" = TRUE, "basic" = j, "single" = k, "null" = l)
a_n <- c(0.0)
e1 <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = c("loglin"), keep_constant = keep_constant, a_n = a_n, modelform = "M", control = control, strat_col = "fac", model_control = model_control)
expect_equal(e0$LogLik, e1$LogLik, tolerance = 1e-2)
}
}
}
})
test_that("Coxph strata_basic_single_CR", {
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$lung == 0)
event <- "censor"
names <- c("dose", "fac")
term_n <- c(0, 0)
tform <- c("loglin", "loglin")
keep_constant <- c(1, 0)
a_n <- c(0, 0)
modelform <- "M"
control <- list("ncores" = 2, "lr" = 0.75, "maxiter" = 20, "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
plot_options <- list("name" = paste(tempfile(), "run", sep = ""), "verbose" = FALSE, "studyid" = "studyid", "age_unit" = "years")
dft <- GetCensWeight(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, control, plot_options)
#
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
#
t_ref <- dft$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
j_iterate <- 1
LL_comp <- c(-81.12079, -81.12079, -77.97632, -77.97632, -68.60263, -68.67108, -75.34028, -75.3691, -81.12079, -81.12079, -77.97632, -77.97632, -68.60263, -68.67108, -75.34028, -75.3691, -122.8909, -122.8909, -119.9814, -119.9814, -109.6211, -109.6742, -117.0147, -117.0539, -122.8909, -122.8909, -119.9814, -119.9814, -109.6211, -109.6742, -117.0147, -117.0539)
dev_comp <- c(0, 0, 0, 0, 0.6850986, 0.6804879, 0.7627068, 0.7600753, 0, 0, 0, 0, 0.6850986, 0.6804879, 0.7627068, 0.7600753, 0, 0, 0, 0, 0.6720932, 0.6688576, 0.7401029, 0.7389506, 0, 0, 0, 0, 0.6720932, 0.6688576, 0.7401029, 0.7389506)
for (i in c(TRUE, FALSE)) {
for (j in c(TRUE, FALSE)) {
for (k in c(TRUE, FALSE)) {
for (l in c(TRUE, FALSE)) {
model_control <- list("strata" = i, "basic" = j, "single" = k, "cr" = l)
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)
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 = "rand", model_control = model_control, cens_weight = "weighting")
expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-3)
if (k == FALSE) {
expect_equal(sum(e$Standard_Deviation), dev_comp[j_iterate], tolerance = 1e-3)
}
j_iterate <- j_iterate + 1
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" = "efron", "double_step" = 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 = "rand", model_control = model_control, cens_weight = "weighting")
expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-3)
if (k == FALSE) {
expect_equal(sum(e$Standard_Deviation), dev_comp[j_iterate], tolerance = 1e-3)
}
j_iterate <- j_iterate + 1
}
}
}
}
})
test_that("Pois strata_single", {
fname <- "ll_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)
time1 <- "t0"
df$pyr <- df$t1 - df$t0
pyr <- "pyr"
event <- "lung"
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
names <- c("dose", "rand", "rand")
term_n <- c(2, 1, 0)
tform <- c("loglin", "lin", "plin")
keep_constant <- c(0, 0, 0)
a_n <- c(0.01, 0.1, 0.1)
modelform <- "PAE"
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" = 0)
strat_col <- "fac"
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
verbose <- FALSE
j_iterate <- 1
LL_comp <- c(-468.7465, -464.9279, -461.2769, -462.1182, -3033.332, -2734.64, -992.622, -1334.36)
for (i in c(TRUE, FALSE)) {
for (j in c(TRUE, FALSE)) {
model_control <- list("strata" = i, "single" = j)
a_n <- c(0.01, 0.1, 0.1)
modelform <- "PAE"
e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control)
expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
j_iterate <- j_iterate + 1
a_n <- c(0.01, 0.1, 0.1)
modelform <- "A"
e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control)
expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
j_iterate <- j_iterate + 1
}
}
})
test_that("Pois comb_forms", {
fname <- "ll_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)
time1 <- "t0"
df$pyr <- df$t1 - df$t0
pyr <- "pyr"
event <- "lung"
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 1, max = 5))
names <- c("dose", "rand", "rand", "dose", "dose")
term_n <- c(1, 0, 0, 0, 0)
tform <- c("loglin", "lin", "plin", "loglin_slope", "loglin_top")
keep_constant <- c(0, 0, 0, 0, 0)
a_n <- c(0.01, 0.1, 0.1, 1.0, 0.1)
modelform <- "PAE"
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" = 0)
strat_col <- "fac"
verbose <- FALSE
modelforms <- c("A", "PAE", "M", "PA")
j_iterate <- 1
LL_comp <- c(-790.3591, -471.0312, -471.0312, -463.1375, -707.56, -678.2228, -678.2228, -471.4805)
for (modelform in modelforms) {
model_control <- list("strata" = FALSE, "single" = FALSE)
a_n <- c(0.01, 0.1, 0.1, 1.0, 0.1)
e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control)
expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
j_iterate <- j_iterate + 1
}
term_n <- c(1, 1, 1, 0, 0)
for (modelform in modelforms) {
model_control <- list("strata" = FALSE, "single" = FALSE)
a_n <- c(0.01, 0.1, 0.1, 1.0, 0.1)
e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control)
expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
j_iterate <- j_iterate + 1
}
})
test_that("Pois strata_single expanded", {
fname <- "ll_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)
time1 <- "t0"
df$pyr <- df$t1 - df$t0
pyr <- "pyr"
event <- "lung"
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
names <- c("dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand", "rand")
term_n <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
tform <- c("loglin_slope", "loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope", "loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope")
keep_constant <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
a_n <- c(1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1)
modelform <- "PAE"
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" = 0)
strat_col <- "fac"
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
verbose <- FALSE
j_iterate <- 1
LL_comp <- c(-496.736611833105, -475.421339181129, -466.96134309201, -463.218078300204, -4497.1782941901, -3577.95315136081, -2557.93228186619, -2350.19459732101)
for (i in c(TRUE, FALSE)) {
for (j in c(TRUE, FALSE)) {
model_control <- list("strata" = i, "single" = j)
a_n <- c(1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1)
modelform <- "PAE"
e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control)
expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
j_iterate <- j_iterate + 1
a_n <- c(1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1)
modelform <- "A"
e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control)
expect_equal(e$LogLik, LL_comp[j_iterate], tolerance = 1e-2)
j_iterate <- j_iterate + 1
}
}
})
test_that("risk check omnibus plain", {
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$lung == 0)
#
event <- "lung"
names <- c("dose", "fac", "dose", "fac", "rand")
term_n <- c(0, 0, 1, 1, 1)
tform <- c("loglin", "lin", "lin", "plin", "loglin")
keep_constant <- c(0, 0, 0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2, 0.3, -0.5)
modelform <- "M"
cens_weight <- c(0)
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
verbose <- FALSE
df_order <- data.table("term_n" = term_n, "tform" = tform, "keep_constant" = keep_constant, "a_n" = a_n, "names" = names, "order" = 1:5)
model_list <- c("M", "A", "PA", "PAE")
means <- c(0.053270537, 0.053270537, 0.053270537, 0.053270537, 0.053270537, 0.223545566, 0.223545566, 0.223545566, 0.223545566, 0.223545566, 0.007932817, 0.007932817, 0.007932817, 0.007932817, 0.007932817, 0.053270537, 0.053270537, 0.053270537, 0.053270537, 0.053270537)
medians <- c(0.08229779, 0.08229779, 0.08229779, 0.08229779, 0.08229779, 0.15570474, 0.15570474, 0.15570474, 0.15570474, 0.15570474, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.08229779, 0.08229779, 0.08229779, 0.08229779, 0.08229779)
sums <- c(53.270537, 53.270537, 53.270537, 53.270537, 53.270537, 223.545566, 223.545566, 223.545566, 223.545566, 223.545566, 7.932817, 7.932817, 7.932817, 7.932817, 7.932817, 53.270537, 53.270537, 53.270537, 53.270537, 53.270537)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
for (model_i in 1:4) {
modelform <- model_list[model_i]
for (i in 1:5) {
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
#
df_order$order <- sample(df_order$order)
setorderv(df_order, c("order"))
term_n <- df_order$term_n
tform <- df_order$tform
keep_constant <- df_order$keep_constant
a_n <- df_order$a_n
names <- df_order$names
#
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)
e <- Cox_Relative_Risk(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, model_control = model_control)$Risk
a_i <- (model_i - 1) * 5 + i
expect_equal(mean(e), means[a_i], tolerance = 1e-2)
expect_equal(median(e), medians[a_i], tolerance = 1e-2)
expect_equal(sum(e), sums[a_i], tolerance = 1e-2)
}
}
})
test_that("risk check omnibus gmix", {
fname <- "ll_comp_0.csv"
colTypes <- c("double", "double", "double", "integer", "integer")
df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
time1 <- "t0"
time2 <- "t1"
df$censor <- (df$lung == 0)
#
event <- "lung"
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
model_list <- c("GMIX-R", "GMIX-E", "GMIX")
names <- c("dose", "fac", "dose", "fac", "rand")
term_n <- c(0, 0, 1, 1, 2)
tform <- c("loglin", "loglin", "plin", "plin", "loglin")
keep_constant <- c(0, 0, 0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2, 0.3, -0.5)
df_order <- data.table("term_n" = term_n, "tform" = tform, "keep_constant" = keep_constant, "a_n" = a_n, "names" = names, "order" = 1:5)
means <- c(0.6918334, 2.9080098, 0.6918334, 1.2728663, 1.8214142, 2.9080098)
medians <- c(0.5871897, 2.8144283, 0.5871897, 1.0226682, 1.7472830, 2.8144283)
sums <- c(691.8334, 2908.0098, 691.8334, 1272.8663, 1821.4142, 2908.0098)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
count <- 0
for (model_i in 1:3) {
modelform <- model_list[model_i]
if (modelform == "GMIX") {
for (term_i in 0:3) {
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
model_control$gmix_term <- c(0, term_i %% 2, floor(term_i / 2))
#
df_order$order <- sample(df_order$order)
setorderv(df_order, c("order"))
term_n <- df_order$term_n
tform <- df_order$tform
keep_constant <- df_order$keep_constant
a_n <- df_order$a_n
names <- df_order$names
#
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)
e <- Cox_Relative_Risk(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, model_control = model_control)$Risk
count <- count + 1
expect_equal(mean(e), means[count], tolerance = 1e-2)
expect_equal(median(e), medians[count], tolerance = 1e-2)
expect_equal(sum(e), sums[count], tolerance = 1e-2)
}
} else {
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
#
df_order$order <- sample(df_order$order)
setorderv(df_order, c("order"))
term_n <- df_order$term_n
tform <- df_order$tform
keep_constant <- df_order$keep_constant
a_n <- df_order$a_n
names <- df_order$names
#
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)
e <- Cox_Relative_Risk(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, model_control = model_control)$Risk
count <- count + 1
expect_equal(mean(e), means[count], tolerance = 1e-2)
expect_equal(median(e), medians[count], tolerance = 1e-2)
expect_equal(sum(e), sums[count], tolerance = 1e-2)
}
}
})
test_that("gmix omnibus use", {
fname <- "ll_comp_0.csv"
colTypes <- c("double", "double", "double", "integer", "integer")
df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
time1 <- "t0"
time2 <- "t1"
df$censor <- (df$lung == 0)
#
event <- "lung"
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
model_list <- c("GMIX-R", "GMIX-E", "GMIX")
names <- c("dose", "fac", "dose", "fac", "rand")
term_n <- c(0, 0, 1, 1, 2)
tform <- c("loglin", "loglin", "plin", "plin", "loglin")
keep_constant <- c(0, 0, 0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2, 0.3, -0.5)
df_order <- data.table("term_n" = term_n, "tform" = tform, "keep_constant" = keep_constant, "a_n" = a_n, "names" = names, "order" = 1:5)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
options(warn = -1)
count <- 0
cens_weight <- c(0)
for (model_i in 1:3) {
modelform <- model_list[model_i]
if (modelform == "GMIX") {
for (term_i in 0:3) {
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
model_control$gmix_term <- c(0, term_i %% 2, floor(term_i / 2))
#
df_order$order <- sample(df_order$order)
setorderv(df_order, c("order"))
term_n <- df_order$term_n
tform <- df_order$tform
keep_constant <- df_order$keep_constant
a_n <- df_order$a_n
names <- df_order$names
#
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(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 = "null"))
}
} else {
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
#
df_order$order <- sample(df_order$order)
setorderv(df_order, c("order"))
term_n <- df_order$term_n
tform <- df_order$tform
keep_constant <- df_order$keep_constant
a_n <- df_order$a_n
names <- df_order$names
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "rand", model_control = model_control, cens_weight = "null"))
}
}
})
test_that("risk check omnibus dose", {
fname <- "ll_comp_0.csv"
colTypes <- c("double", "double", "double", "integer", "integer")
df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
time1 <- "t0"
time2 <- "t1"
df$censor <- (df$lung == 0)
#
event <- "lung"
names <- c("dose", "fac", "dose", "fac", "rand")
term_n <- c(0, 0, 1, 1, 1)
tform <- c("loglin", "lin", "lin", "plin", "loglin")
keep_constant <- c(0, 0, 0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2, 0.3, -0.5)
modelform <- "M"
cens_weight <- c(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
df_order <- data.table("term_n" = term_n, "tform" = tform, "keep_constant" = keep_constant, "a_n" = a_n, "names" = names, "order" = 1:5)
model_list <- c("M", "A", "PA", "PAE")
names <- c("dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "dose", "fac", "fac", "fac", "fac", "fac", "fac", "fac", "fac", "fac", "fac", "fac")
term_n <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
tform <- c("loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope", "loglin_top", "lin_slope", "lin_int", "quad_slope", "step_slope", "step_int", "lin_quad_slope", "lin_quad_int", "lin_exp_slope", "lin_exp_int", "lin_exp_exp_slope")
keep_constant <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
a_n <- c(-0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1, -0.1, -0.1, 1, -0.1, 1, 2, 0.3, 1.5, 0.2, 0.7, 1)
df_order <- data.table("term_n" = term_n, "tform" = tform, "keep_constant" = keep_constant, "a_n" = a_n, "names" = names, "order" = 1:22)
means <- c(3.268830, 2.672975, 1.753389, 3.268830)
medians <- c(2.898413, 2.558679, 1.636409, 2.898413)
sums <- c(3268.830, 2672.975, 1753.389, 3268.830)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
for (model_i in 1:4) {
modelform <- model_list[model_i]
for (i in 1:5) {
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
#
df_order$order <- sample(df_order$order)
setorderv(df_order, c("order"))
term_n <- df_order$term_n
tform <- df_order$tform
keep_constant <- df_order$keep_constant
a_n <- df_order$a_n
names <- df_order$names
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
e <- Cox_Relative_Risk(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, model_control = model_control)$Risk
expect_equal(mean(e), means[model_i], tolerance = 1e-2)
expect_equal(median(e), medians[model_i], tolerance = 1e-2)
expect_equal(sum(e), sums[model_i], tolerance = 1e-2)
}
}
})
test_that("Coxph relative risk combinations", {
a <- c(0, 1, 2, 3, 4, 5, 6)
b <- c(1, 2, 3, 4, 5, 6, 7)
c <- c(0, 1, 0, 0, 0, 1, 0)
d <- c(3, 4, 5, 6, 7, 8, 9)
df <- data.table("a" = a, "b" = b, "c" = c, "d" = d)
time1 <- "a"
time2 <- "b"
event <- "c"
names <- c("d")
term_n <- c(0)
tform <- c("loglin")
keep_constant <- c(0)
a_n <- c(-0.1)
modelform <- "M"
control <- list("ncores" = 2, "lr" = 0.75, "maxiter" = -1, "halfmax" = 5, "epsilon" = 1e-9, "deriv_epsilon" = 1e-9, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
keep_constant <- c(1)
expect_error(Cox_Relative_Risk(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, control))
keep_constant <- c(0)
names <- c("d", "CONST")
term_n <- c(0, 0)
tform <- c("loglin", "loglin")
keep_constant <- c(0, 0)
a_n <- c(-0.1, 0.1)
expect_no_error(Cox_Relative_Risk(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, control))
})
test_that("check deviation calc, expected cox", {
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$rand <- floor(runif(nrow(df), min = 0, max = 5))
time1 <- "t0"
time2 <- "t1"
event <- "lung"
names <- c("dose", "fac", "rand")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "loglin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2)
modelform <- "M"
cens_weight <- c(0)
verbose <- FALSE
devs <- c()
modelform <- "M"
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
for (i in 1:3) {
a_n <- c(0.6465390, 0.4260961, 0.1572781)
keep_constant <- c(0, 0, 0)
keep_constant[i] <- 1
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "fac", model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
}
a_n <- c(0.6465390, 0.4260961, 0.1572781)
keep_constant <- c(0, 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)
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 = "fac", model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
time1 <- "t0"
time2 <- "t1"
event <- "lung"
names <- c("dose", "fac", "rand")
term_n <- c(0, 0, 0)
tform <- c("loglin", "loglin", "plin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2)
for (i in 1:3) {
a_n <- c(0.6428582, 0.4240752, 0.1507817)
keep_constant <- c(0, 0, 0)
keep_constant[i] <- 1
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "fac", model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
}
a_n <- c(0.6428582, 0.4240752, 0.1507817)
keep_constant <- c(0, 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)
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 = "fac", model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
expect_equal(devs, c(0.61445, 0.54101, 0.73858, 0.95015, 0.63646, 0.56292, 0.73815, 0.97195), tolerance = 1e-4)
})
test_that("check deviation calc, Observed cox", {
fname <- "ll_comp_0.csv"
colTypes <- c("double", "double", "double", "integer", "integer")
df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
time1 <- "t0"
time2 <- "t1"
event <- "lung"
names <- c("dose", "fac", "rand")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "loglin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2)
modelform <- "M"
cens_weight <- c(0)
verbose <- FALSE
devs <- c()
modelform <- "M"
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE, "oberved_info" = TRUE)
for (i in 1:3) {
a_n <- c(0.6465390, 0.4260961, 0.1572781)
keep_constant <- c(0, 0, 0)
keep_constant[i] <- 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)
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 = "fac", model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
}
a_n <- c(0.6465390, 0.4260961, 0.1572781)
keep_constant <- c(0, 0, 0)
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "fac", model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
time1 <- "t0"
time2 <- "t1"
event <- "lung"
names <- c("dose", "fac", "rand")
term_n <- c(0, 0, 0)
tform <- c("loglin", "loglin", "plin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2)
for (i in 1:3) {
a_n <- c(0.6428582, 0.4240752, 0.1507817)
keep_constant <- c(0, 0, 0)
keep_constant[i] <- 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)
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 = "fac", model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
}
a_n <- c(0.6428582, 0.4240752, 0.1507817)
keep_constant <- c(0, 0, 0)
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, strat_col = "fac", model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
expect_equal(devs, c(0.6091269, 0.5356671, 0.7385757, 0.9448081, 0.7051473, 0.5838560, 0.7381538, 0.9897501), tolerance = 1e-4)
})
test_that("check Linear Constraints", {
fname <- "l_pl_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)
df$pyr <- df$t1 - df$t0
time1 <- "t0"
time2 <- "t1"
pyr <- "pyr"
event <- "lung"
names <- c("dose", "fac")
term_n <- c(0, 0)
tform <- c("loglin", "plin")
keep_constant <- c(0, 0)
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "null" = FALSE, "constraint" = TRUE)
Constraint_Matrix <- matrix(c(1, -1), nrow = 1)
Constraint_const <- c(0.0)
set.seed(3742)
if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN", "false")))) {
skip("Cran Skip")
}
for (i in 1:5) {
a_n <- 2 * runif(2) - 1
del <- abs(a_n[1] - a_n[2])
a_n0 <- rep(sum(a_n) / 2, 2)
a_n <- a_n0 - c(-del / 2, del / 2)
modelform <- "M"
control <- list("ncores" = 2, "lr" = 0.75, "maxiter" = 20, "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col = "fac", model_control = model_control, cons_mat = Constraint_Matrix, cons_vec = Constraint_const)
expect_equal(e$beta_0, c(0.357333, 0.357333), tolerance = 1e-2)
}
for (i in 1:5) {
a_n <- 2 * runif(2) - 1
del <- abs(a_n[1] - a_n[2])
a_n0 <- rep(sum(a_n) / 2, 2)
a_n <- a_n0 + c(-del / 2, del / 2)
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)
e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col = "fac", model_control = model_control, cons_mat = Constraint_Matrix, cons_vec = Constraint_const)
expect_equal(e$beta_0, c(-0.472812, -0.472812), tolerance = 1e-2)
}
})
test_that("check deviation calc, poisson", {
fname <- "ll_comp_0.csv"
colTypes <- c("double", "double", "double", "integer", "integer")
df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
df$pyr <- df$t1 - df$t0
pyr <- "pyr"
event <- "lung"
names <- c("dose", "fac", "rand")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "loglin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2)
modelform <- "M"
devs <- c()
modelform <- "M"
for (inma_type in c(T, F)) {
model_control <- list("oberved_info" = inma_type)
for (i in 1:3) {
a_n <- c(0.6465390, 0.4260961, 0.1572781)
keep_constant <- c(0, 0, 0)
keep_constant[i] <- 1
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
}
event <- "lung"
names <- c("dose", "fac", "rand")
term_n <- c(0, 0, 0)
tform <- c("loglin", "loglin", "plin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2)
for (i in 1:3) {
a_n <- c(0.6428582, 0.4240752, 0.1507817)
keep_constant <- c(0, 0, 0)
keep_constant[i] <- 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)
e <- RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, control = control, model_control = model_control)
devs <- c(devs, sum(e$Standard_Deviation))
}
}
expect_equal(devs, c(0.029317931, 0.014226835, 0.030171059, 0.026452308, 0.008968795, 0.040982844, 0.026119220, 0.008023552, 0.040535859, 0.026082652, 0.007801193, 0.040982844), tolerance = 1e-4)
})
test_that("Various CoxRegressionOmnibus options", {
fname <- "ll_comp_0.csv"
colTypes <- c("double", "double", "double", "integer", "integer")
df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE)
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
time1 <- "t0"
time2 <- "t1"
event <- "lung"
names <- c("rand", "fac", "dose")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "loglin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2)
modelform <- "M"
cens_weight <- c(0)
verbose <- FALSE
devs <- c()
options(warn = -1)
modelform <- "M"
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
a_n <- c(0.6465390, 0.4260961, 0.1572781)
keep_constant <- c(0, 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)
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 = "fac", model_control = model_control))
keep_constant <- c(1, 1, 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 = "fac", model_control = model_control))
lung_temp <- df$lung
df$lung <- rep(0, length(lung_temp))
keep_constant <- c(0, 0, 0)
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 = "fac", model_control = model_control))
df$lung <- lung_temp
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1, 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(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 = "fac", model_control = model_control))
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)
a_n <- list(c(0.6465390, 0.4260961, 0.1572781), c(0.6465390, 0.4260961, 0.1572781), c(0.6465390, 0.4260961, 0.1572781))
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 = "fac", model_control = model_control))
a_n <- c(0.6465390, 0.4260961, 0.1572781)
#
control <- list("ncores" = 2, "lr" = 0.75, "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" = 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 = "fac", model_control = model_control))
control <- list("ncores" = 2, "lr" = 0.75, "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)
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 = "fac", model_control = model_control))
#
names <- c("rand", "fac", "dose")
term_n <- c(0, 0, 1)
tform <- c("lin", "lin", "lin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, -0.1, 0.2)
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 = "fac", model_control = model_control))
})
test_that("Various RunPoissonRegression_Omnibus options", {
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$pyr <- df$t1 - df$t0
pyr <- "pyr"
#
event <- "lung"
names <- c("rand", "fac", "dose")
term_n <- c(0, 0, 1)
tform <- c("loglin", "loglin", "loglin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, 0.1, 0.2)
modelform <- "M"
cens_weight <- c(0)
verbose <- FALSE
devs <- c()
modelform <- "M"
model_control <- list("strata" = FALSE, "basic" = FALSE, "single" = FALSE, "cr" = FALSE)
a_n <- c(0.6465390, 0.4260961, 0.1572781)
keep_constant <- c(0, 0, 0)
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1)
strat_col <- "fac"
expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
keep_constant <- c(1, 1, 1)
expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
lung_temp <- df$lung
df$lung <- rep(0, length(lung_temp))
keep_constant <- c(0, 0, 0)
expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
df$lung <- lung_temp
#
control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1, 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, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
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)
a_n <- list(c(0.6465390, 0.4260961, 0.1572781), c(0.6465390, 0.4260961, 0.1572781), c(0.6465390, 0.4260961, 0.1572781))
expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
a_n <- c(0.6465390, 0.4260961, 0.1572781)
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" = 50)
expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
#
control <- list("ncores" = 2, "lr" = 0.75, "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" = 1)
expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
control <- list("ncores" = 2, "lr" = 0.75, "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)
expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
#
names <- c("rand", "fac", "dose")
term_n <- c(0, 0, 1)
tform <- c("lin", "lin", "lin")
keep_constant <- c(0, 0, 0)
a_n <- c(-0.1, -0.1, 0.2)
expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
})
test_that("Pois various_fixes", {
fname <- "ll_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)
time1 <- "t0"
df$pyr <- df$t1 - df$t0
pyr <- "pyr"
event <- "lung"
set.seed(3742)
df$rand <- floor(runif(nrow(df), min = 0, max = 5))
names <- c("dose", "rand", "rand")
term_n <- c(2, 1, 0)
tform <- c("loglin", "loglin", "loglin")
keep_constant <- c(0, 0, 0)
a_n <- c(0.01, 0.1, 0.1)
modelform <- "PAE"
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" = 0)
strat_col <- "fac"
verbose <- FALSE
model_control <- list("strata" = FALSE, "single" = FALSE)
a_n <- c(0.01, 0.1, 0.1)
modelform <- "PAE"
expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
keep_constant <- c(1, 1, 1)
expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
keep_constant <- c(0, 0, 0)
ev0 <- df$lung
df$lung <- rep(0, length(ev0))
expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
names <- c("dose", "rand", "CONST")
df$lung <- ev0
expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
control$guesses <- 100
expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n, tform, keep_constant, a_n, modelform, control, strat_col, model_control))
})
Try the Colossus package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.