test_that("Coxph strata_basic_single_CR_null log_bound", { fname <- "ll_comp_0.csv" colTypes <- c("double", "double", "double", "integer", "integer") df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE) set.seed(3742) df$rand <- floor(runif(nrow(df), min = 0, max = 5)) time1 <- "t0" time2 <- "t1" df$censor <- (df$lung == 0) event <- "censor" names <- c("dose", "fac") term_n <- c(0, 0) tform <- c("loglin", "loglin") keep_constant <- c(1, 0) a_n <- c(0, 0) modelform <- "M" fir <- 0 der_iden <- 0 control <- list("ncores" = 2, "lr" = 0.75, "maxiter" = 20, "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1) plot_options <- list("name" = paste(tempfile(), "run", sep = ""), "verbose" = FALSE, "studyid" = "studyid", "age_unit" = "years") dft <- GetCensWeight(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, control, plot_options) # # t_ref <- dft$t surv_ref <- dft$surv t_c <- df$t1 cens_weight <- approx(t_ref, surv_ref, t_c, rule = 2)$y df$weighting <- cens_weight # event <- "lung" a_n <- c(-0.1, -0.1) keep_constant <- c(0, 0) control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(-1, -1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1) verbose <- FALSE if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN","false")))){skip("Cran Skip")} for (i in c(TRUE, FALSE)) { for (j in c(TRUE, FALSE)) { for (k in c(FALSE, FALSE)) { for (l in c(TRUE, FALSE)) { for (m in c(TRUE, FALSE)) { model_control <- list("strata" = i, "basic" = j, "single" = k, "cr" = l, "log_bound" = TRUE, "manual" = m) if (verbose) { print(model_control) } a_n <- c(-0.1, -0.1) control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1) expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting")) a_n <- c(-0.1, -0.1) control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 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) 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, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting")) if (verbose) { print("---------------") } } } } } } for (m in c(TRUE, FALSE)) { model_control <- list("null" = T, "log_bound" = TRUE, "manual" = m) if (verbose) { print(model_control) } a_n <- c(-0.1, -0.1) control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1) expect_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting")) model_control <- list("single" = T, "log_bound" = TRUE, "manual" = m) expect_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control, cens_weight = "weighting")) } }) test_that("Poisson strata_single log_bound", { fname <- "ll_comp_0.csv" colTypes <- c("double", "double", "double", "integer", "integer") df <- fread(fname, nThread = min(c(detectCores(), 2)), data.table = TRUE, header = TRUE, colClasses = colTypes, verbose = FALSE, fill = TRUE) set.seed(3742) df$rand <- floor(runif(nrow(df), min = 0, max = 5)) df$pyr <- df$t1 - df$t0 time1 <- "t0" time2 <- "t1" pyr <- "pyr" df$censor <- (df$lung == 0) event <- "censor" names <- c("dose", "fac") term_n <- c(0, 0) tform <- c("loglin", "loglin") keep_constant <- c(1, 0) a_n <- c(0, 0) modelform <- "M" fir <- 0 der_iden <- 0 control <- list("ncores" = 2, "lr" = 0.75, "maxiter" = 20, "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1) # event <- "lung" a_n <- c(-0.1, -0.1) keep_constant <- c(0, 0) control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(-1, -1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1) verbose <- FALSE if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN","false")))){skip("Cran Skip")} for (i in c(TRUE, FALSE)) { for (k in c(FALSE, FALSE)) { for (m in c(TRUE, FALSE)) { model_control <- list("strata" = i, "single" = k, "log_bound" = TRUE, "manual" = m) if (verbose) { print(model_control) } a_n <- c(-0.1, -0.1) control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1) expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control)) } } } for (m in c(TRUE, FALSE)) { for (k in c(TRUE)) { model_control <- list("strata" = F, "single" = k, "log_bound" = TRUE, "manual" = m) if (verbose) { print(model_control) } a_n <- c(-0.1, -0.1) control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1) expect_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control)) } } }) test_that("Coxph EPICURE validated answers, loglin", { fname <- "base_example.csv" df <- fread(fname) time1 <- "entry" time2 <- "exit" event <- "event" names <- c("dose0", "dose1") term_n <- c(0, 0) tform <- c("loglin", "loglin") keep_constant <- c(0, 0) a_n <- c(0, 0) modelform <- "M" fir <- 0 der_iden <- 0 # a_n <- c(-0.6067, 5.019) model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = 0.1) control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1) if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN","false")))){skip("Cran Skip")} v_lower <- c(-0.6305960, -0.6572672, -0.6817293, -0.6929630, -0.7300938, -0.7537744, -0.7749381, -0.8001031, -0.8175117) v_upper <- c(-0.5828725, -0.5562505, -0.5318645, -0.5206756, -0.4837373, -0.4602148, -0.4392159, -0.4142752, -0.3970399) alphas <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005) for (alpha_i in seq_along(alphas)) { a_n <- c(-0.6067, 5.019) model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = alphas[alpha_i], "para_number" = 0) e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control) a <- e$Parameter_Limits expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4) expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4) } v_lower <- c(4.981497, 4.939337, 4.900838, 4.883211, 4.825191, 4.788380, 4.755608, 4.716794, 4.690041) v_upper <- c(5.057414, 5.100032, 5.139239, 5.157283, 5.217094, 5.255376, 5.289680, 5.330581, 5.358945) for (alpha_i in seq_along(alphas)) { a_n <- c(-0.6067, 5.019) model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = alphas[alpha_i], "para_number" = 1) e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control) a <- e$Parameter_Limits expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4) expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4) } }) test_that("Coxph EPICURE validated answers, loglin manual", { fname <- "base_example.csv" df <- fread(fname) time1 <- "entry" time2 <- "exit" event <- "event" names <- c("dose0", "dose1") term_n <- c(0, 0) tform <- c("loglin", "loglin") keep_constant <- c(0, 0) a_n <- c(0, 0) modelform <- "M" fir <- 0 der_iden <- 0 # a_n <- c(-0.6067, 5.019) model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = 0.1) control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1) v_lower <- c(-0.6305960, -0.6572672, -0.6817293, -0.6929630, -0.7300938, -0.7537744, -0.7749381, -0.8001031, -0.8175117) v_upper <- c(-0.5828725, -0.5562505, -0.5318645, -0.5206756, -0.4837373, -0.4602148, -0.4392159, -0.4142752, -0.3970399) if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN","false")))){skip("Cran Skip")} alphas <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005) for (alpha_i in seq_along(alphas)) { a_n <- c(-0.6067, 5.019) model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = alphas[alpha_i], "para_number" = 0, "manual" = TRUE) e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control) a <- e$Parameter_Limits expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4) expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4) } v_lower <- c(4.981497, 4.939337, 4.900838, 4.883211, 4.825191, 4.788380, 4.755608, 4.716794, 4.690041) v_upper <- c(5.057414, 5.100032, 5.139239, 5.157283, 5.217094, 5.255376, 5.289680, 5.330581, 5.358945) for (alpha_i in seq_along(alphas)) { a_n <- c(-0.6067, 5.019) model_control <- list("basic" = TRUE, "log_bound" = TRUE, "alpha" = alphas[alpha_i], "para_number" = 1, "manual" = TRUE) e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control) a <- e$Parameter_Limits expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4) expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4) } }) test_that("Coxph, lin both", { fname <- "base_example.csv" df <- fread(fname) time1 <- "entry" time2 <- "exit" event <- "event" names <- c("dose0", "dose1", "dose0") term_n <- c(0, 0, 1) tform <- c("loglin", "loglin", "lin") keep_constant <- c(0, 0, 0) # a_n <- c(0.2462, 5.020, -0.5909) a_n <- c(0.2462, 5.020, -0.7) modelform <- "M" fir <- 0 der_iden <- 0 # model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = FALSE, "alpha" = 0.1) control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10) alpha <- 0.005 a_n <- c(0.2462, 5.020, -0.599) model_control <- list("basic" = FALSE, "maxstep" = 5, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = FALSE) expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control)) a_n <- c(0.2462, 5.020, -0.599) model_control <- list("basic" = FALSE, "maxstep" = 5, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = TRUE) expect_no_error(RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control)) if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN","false")))){skip("Cran Skip")} alpha_list <- c(0.75, 0.5, 1 - 0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005) control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(1, 1), "halfmax" = 2, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10) control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 5, "epsilon" = 1e-4, "deriv_epsilon" = 1e-3, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10) v_lower <- c(4.97252283668956, 4.9349945105648, 4.89804715665926, 4.88084912208962, 4.82369762341988, 4.78721237571926, 4.7546530342797, 4.71603055250556, 4.68938287303871) v_upper <- c(5.06762896572498, 5.10561529697034, 5.14327069556976, 5.16088604918614, 5.21982880792394, 5.2577860471215, 5.29187760184654, 5.33258757872226, 5.36084782852899) for (alpha_i in 1:length(alpha_list)) { alpha <- alpha_list[alpha_i] a_n <- c(0.2462, 5.020, -0.599) model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = TRUE) e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control) a <- e$Parameter_Limits expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4) expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4) } v_lower <- c(-0.643365949558998, -0.677336655540846, -0.706075250211414, -0.718165409196492, -0.753647332793819, -0.773208334303991, -0.789018704115451, -0.806061085000755, -0.816875114954096) v_upper <- c(-0.521472203247917, -0.444964438813732, -0.327862977142017, -0.235044092073815, 2.91573713669059, 3.21014641617297, 3.48490803194128, 3.82648584413642, 4.07272009904963) for (alpha_i in 1:length(alpha_list)) { alpha <- alpha_list[alpha_i] a_n <- c(0.2462, 5.020, -0.599) model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 2, "manual" = TRUE) e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "nan", model_control = model_control) a <- e$Parameter_Limits expect_equal(a[1], v_lower[alpha_i], tolerance = 1e-4) expect_equal(a[2], v_upper[alpha_i], tolerance = 1e-4) } }) test_that("Poisson, lin both", { fname <- "base_example.csv" df <- fread(fname) pyr <- "exit" event <- "event" names <- c("dose0", "dose1") term_n <- c(0, 1) tform <- c("loglin", "lin") keep_constant <- c(0, 0) a_n <- c(-2.917, 0.06526) modelform <- "M" fir <- 0 der_iden <- 0 # model_control <- list("basic" = FALSE, "maxstep" = 100, "log_bound" = FALSE, "alpha" = 0.1) control <- list("ncores" = 2, "lr" = 0.75, "maxiters" = c(10, 10), "halfmax" = 5, "epsilon" = 1e-6, "deriv_epsilon" = 1e-6, "abs_max" = 1.0, "change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = 0, "ties" = "breslow", "double_step" = 1, "guesses" = 10) alpha <- 0.005 a_n <- c(-2.917, 0.06526) model_control <- list("basic" = FALSE, "maxstep" = 5, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = FALSE) expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control)) a_n <- c(-2.917, 0.06526) model_control <- list("basic" = FALSE, "maxstep" = 5, "log_bound" = TRUE, "alpha" = alpha, "para_number" = 1, "manual" = TRUE) expect_no_error(RunPoissonRegression_Omnibus(df, pyr, event, names, term_n = term_n, tform = tform, keep_constant = keep_constant, a_n = a_n, modelform = modelform, fir = fir, der_iden = der_iden, control = control, strat_col = "rand", model_control = model_control)) # if (!isTRUE(as.logical(Sys.getenv("NOT_CRAN","false")))){skip("Cran Skip")} # alpha_list <- c(0.75, 0.5, 1-0.683, 0.25, 0.1, 0.05, 0.025, 0.01, 0.005) # control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(1,1), 'halfmax' = 2, 'epsilon' = 1e-6, 'deriv_epsilon' = 1e-6, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=0, 'ties'='breslow', 'double_step'=1, 'guesses'=10) # control <- list( "ncores"=2, 'lr' = 0.75, 'maxiters' = c(10,10), 'halfmax' = 5, 'epsilon' = 1e-4, 'deriv_epsilon' = 1e-3, 'abs_max'=1.0, 'change_all'=TRUE, 'dose_abs_max'=100.0, 'verbose'=0, 'ties'='breslow', 'double_step'=1, 'guesses'=10) # v_lower <- c(4.97252283668956, 4.9349945105648, 4.89804715665926, 4.88084912208962, 4.82369762341988, 4.78721237571926, 4.7546530342797, 4.71603055250556, 4.68938287303871) # v_upper <- c(5.06762896572498, 5.10561529697034, 5.14327069556976, 5.16088604918614, 5.21982880792394, 5.2577860471215, 5.29187760184654, 5.33258757872226, 5.36084782852899) # for (alpha_i in 1:length(alpha_list)){ # alpha <- alpha_list[alpha_i] # a_n <- c(0.2462, 5.020,-0.599) # model_control <- list( 'basic'=FALSE, 'maxstep'=100, 'log_bound'=TRUE, 'alpha'=alpha, 'para_number'=1, 'manual'=TRUE) # e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n=term_n, tform=tform, keep_constant=keep_constant, a_n=a_n, modelform=modelform, fir=fir, der_iden=der_iden, control=control,strat_col="nan", model_control=model_control) # a <- e$Parameter_Limits # expect_equal(a[1], v_lower[alpha_i],tolerance=1e-4) # expect_equal(a[2], v_upper[alpha_i],tolerance=1e-4) # } # v_lower <- c(-0.643365949558998, -0.677336655540846, -0.706075250211414, -0.718165409196492, -0.753647332793819, -0.773208334303991, -0.789018704115451, -0.806061085000755, -0.816875114954096) # v_upper <- c(-0.521472203247917, -0.444964438813732, -0.327862977142017, -0.235044092073815, 2.91573713669059, 3.21014641617297, 3.48490803194128, 3.82648584413642, 4.07272009904963) # for (alpha_i in 1:length(alpha_list)){ # alpha <- alpha_list[alpha_i] # a_n <- c(0.2462, 5.020,-0.599) # model_control <- list( 'basic'=FALSE, 'maxstep'=100, 'log_bound'=TRUE, 'alpha'=alpha, 'para_number'=2, 'manual'=TRUE) # e <- RunCoxRegression_Omnibus(df, time1, time2, event, names, term_n=term_n, tform=tform, keep_constant=keep_constant, a_n=a_n, modelform=modelform, fir=fir, der_iden=der_iden, control=control,strat_col="nan", model_control=model_control) # a <- e$Parameter_Limits # expect_equal(a[1], v_lower[alpha_i],tolerance=1e-4) # expect_equal(a[2], v_upper[alpha_i],tolerance=1e-4) # } })