### ### Tests for calibration curves produced using pseudo-values (calib.type = 'AJ') ### ### Run tests for pv_n_pctls = NULL and pv_group_vars = NULL test_that("check calib_aj, pv_n_pctls = NULL and pv_group_vars = NULL", { ## Reduce to 50 individuals # Extract the predicted transition probabilities out of state j = 1 for first 50 individuals tp_pred <- tps0 |> dplyr::filter(id %in% 1:50) |> dplyr::filter(j == 1) |> dplyr::select(any_of(paste("pstate", 1:6, sep = ""))) # Reduce ebmtcal to first 50 individuals ebmtcal <- ebmtcal |> dplyr::filter(id %in% 1:50) # Reduce msebmtcal_cmprsk to first 100 individuals msebmtcal <- msebmtcal |> dplyr::filter(id %in% 1:50) ## Calculate observed event probabilities using transitions_out = NULL dat_calib_aj_1 <- suppressWarnings(calib_msm(data_ms = msebmtcal, data_raw = ebmtcal, j = 1, s = 0, t = 1826, tp_pred = tp_pred, calib_type = 'aj', tp_pred_plot = NULL, transitions_out = NULL)) expect_equal(class(dat_calib_aj_1), c("calib_aj", "calib_msm")) expect_equal(length(dat_calib_aj_1[["mean"]]), 6) ## Calculate observed event probabilities using transitions_out = NULL dat_calib_aj_CI_1 <- suppressWarnings(calib_msm(data_ms = msebmtcal, data_raw = ebmtcal, j = 1, s = 0, t = 1826, tp_pred = tp_pred, calib_type = 'aj', CI = 95, CI_R_boot = 10, tp_pred_plot = NULL, transitions_out = NULL)) expect_equal(class(dat_calib_aj_CI_1), c("calib_aj", "calib_msm")) expect_equal(length(dat_calib_aj_CI_1[["mean"]]), 6) expect_equal(length(dat_calib_aj_CI_1[["mean"]][[1]]), 3) expect_equal(as.numeric(dat_calib_aj_1[["mean"]][1]), as.numeric(dat_calib_aj_CI_1[["mean"]][[1]][1])) expect_equal(as.numeric(dat_calib_aj_1[["mean"]][6]), as.numeric(dat_calib_aj_CI_1[["mean"]][[6]][1])) }) ### Run tets pv_n_pctls specified test_that("check calib_pv output, pv_n_pctls specified", { ## Reduce to 50 individuals # Extract the predicted transition probabilities out of state j = 1 for first 50 individuals tp_pred <- tps0 |> dplyr::filter(id %in% 1:50) |> dplyr::filter(j == 1) |> dplyr::select(any_of(paste("pstate", 1:6, sep = ""))) # Reduce ebmtcal to first 50 individuals ebmtcal <- ebmtcal |> dplyr::filter(id %in% 1:50) # Reduce msebmtcal_cmprsk to first 100 individuals msebmtcal <- msebmtcal |> dplyr::filter(id %in% 1:50) ## Calculate observed event probabilities using transitions_out = NULL dat_calib_aj_1 <- suppressWarnings(calib_msm(data_ms = msebmtcal, data_raw = ebmtcal, j = 1, s = 0, t = 1826, tp_pred = tp_pred, calib_type = 'aj', pv_n_pctls = 2, tp_pred_plot = NULL, transitions_out = NULL)) expect_equal(class(dat_calib_aj_1), c("calib_aj", "calib_msm")) expect_equal(length(dat_calib_aj_1[["mean"]]), 6) ## Calculate observed event probabilities using transitions_out = NULL dat_calib_aj_CI_1 <- suppressWarnings(calib_msm(data_ms = msebmtcal, data_raw = ebmtcal, j = 1, s = 0, t = 1826, tp_pred = tp_pred, calib_type = 'aj', pv_n_pctls = 2, CI = 95, CI_R_boot = 10, tp_pred_plot = NULL, transitions_out = NULL)) expect_equal(class(dat_calib_aj_CI_1), c("calib_aj", "calib_msm")) expect_equal(length(dat_calib_aj_CI_1[["mean"]]), 6) expect_equal(length(dat_calib_aj_CI_1[["mean"]][[1]]), 3) expect_equal(as.numeric(dat_calib_aj_1[["mean"]][1]), as.numeric(dat_calib_aj_CI_1[["mean"]][[1]][1])) expect_equal(as.numeric(dat_calib_aj_1[["mean"]][6]), as.numeric(dat_calib_aj_CI_1[["mean"]][[6]][1])) }) ### Run tests pv_group_vars specified test_that("check calib_pv output, pv_group_vars specified", { skip_on_cran() ## Reduce to 50 individuals # Extract the predicted transition probabilities out of state j = 1 for first 50 individuals tp_pred <- tps0 |> dplyr::filter(id %in% 1:50) |> dplyr::filter(j == 1) |> dplyr::select(any_of(paste("pstate", 1:6, sep = ""))) # Reduce ebmtcal to first 50 individuals ebmtcal <- ebmtcal |> dplyr::filter(id %in% 1:50) # Reduce msebmtcal_cmprsk to first 100 individuals msebmtcal <- msebmtcal |> dplyr::filter(id %in% 1:50) ## Calculate observed event probabilities using transitions_out = NULL dat_calib_aj_1 <- suppressWarnings(calib_msm(data_ms = msebmtcal, data_raw = ebmtcal, j = 1, s = 0, t = 1826, tp_pred = tp_pred, calib_type = 'aj', pv_group_vars = c("year"), tp_pred_plot = NULL, transitions_out = NULL)) expect_equal(class(dat_calib_aj_1), c("calib_aj", "calib_msm")) expect_equal(length(dat_calib_aj_1[["mean"]]), 6) ## Calculate observed event probabilities using transitions_out = NULL dat_calib_aj_CI_1 <- suppressWarnings(calib_msm(data_ms = msebmtcal, data_raw = ebmtcal, j = 1, s = 0, t = 1826, tp_pred = tp_pred, calib_type = 'aj', pv_group_vars = c("year"), CI = 95, CI_R_boot = 10, tp_pred_plot = NULL, transitions_out = NULL)) expect_equal(class(dat_calib_aj_CI_1), c("calib_aj", "calib_msm")) expect_equal(length(dat_calib_aj_CI_1[["mean"]]), 6) expect_equal(length(dat_calib_aj_CI_1[["mean"]][[1]]), 3) expect_equal(as.numeric(dat_calib_aj_1[["mean"]][1]), as.numeric(dat_calib_aj_CI_1[["mean"]][[1]][1])) expect_equal(as.numeric(dat_calib_aj_1[["mean"]][6]), as.numeric(dat_calib_aj_CI_1[["mean"]][[6]][1])) }) ### Run tests pv_group_vars and pv_n_pctls specified test_that("check calib_pv output, pv_group_vars and pv_n_pctls specified ", { skip_on_cran() ## Set seed set.seed(101) ## Reduce to 50 individuals # Extract the predicted transition probabilities out of state j = 1 for first 50 individuals tp_pred <- tps0 |> dplyr::filter(id %in% 1:100) |> dplyr::filter(j == 1) |> dplyr::select(any_of(paste("pstate", 1:6, sep = ""))) # Reduce ebmtcal to first 50 individuals ebmtcal <- ebmtcal |> dplyr::filter(id %in% 1:100) # Reduce msebmtcal_cmprsk to first 100 individuals msebmtcal <- msebmtcal |> dplyr::filter(id %in% 1:100) ## Calculate observed event probabilities using transitions_out = NULL dat_calib_aj_1 <- suppressWarnings(calib_msm(data_ms = msebmtcal, data_raw = ebmtcal, j = 1, s = 0, t = 1826, tp_pred = tp_pred, calib_type = 'aj', pv_n_pctls = 2, pv_group_vars = c("year"), tp_pred_plot = NULL, transitions_out = NULL)) expect_equal(class(dat_calib_aj_1), c("calib_aj", "calib_msm")) expect_equal(length(dat_calib_aj_1[["mean"]]), 6) ## Calculate observed event probabilities using transitions_out = NULL dat_calib_aj_CI_1 <- suppressWarnings(calib_msm(data_ms = msebmtcal, data_raw = ebmtcal, j = 1, s = 0, t = 1826, tp_pred = tp_pred, calib_type = 'aj', pv_n_pctls = 2, pv_group_vars = c("year"), CI = 95, CI_R_boot = 10, tp_pred_plot = NULL, transitions_out = NULL)) expect_equal(class(dat_calib_aj_CI_1), c("calib_aj", "calib_msm")) expect_equal(length(dat_calib_aj_CI_1[["mean"]]), 6) expect_equal(length(dat_calib_aj_CI_1[["mean"]][[1]]), 3) expect_equal(as.numeric(dat_calib_aj_1[["mean"]][1]), as.numeric(dat_calib_aj_CI_1[["mean"]][[1]][1])) expect_equal(as.numeric(dat_calib_aj_1[["mean"]][6]), as.numeric(dat_calib_aj_CI_1[["mean"]][[6]][1])) })