### ### Tests for calibration curves produced using pseudo-values (calib.type = 'pv') ### ### Run tests for when curve.type = "loess" and CI.type = "bootstrap". test_that("check calib_pv output, (j = 1, s = 0), curve.type = loess, CI.type = bootstrap", { 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.pv.1 <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "loess", tp.pred.plot = NULL, transitions.out = NULL) expect_equal(class(dat.calib.pv.1), c("calib_pv", "calib_msm")) expect_equal(dat.calib.pv.1[["metadata"]][["curve.type"]], "loess") expect_equal(ncol(dat.calib.pv.1[["plotdata"]][[1]]), 4) expect_no_error(summary(dat.calib.pv.1)) ## Check same results when just calculating pseudo-values for first three individuals dat.calib.pv.ids.1 <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', pv.ids = 1:3, tp.pred.plot = NULL, transitions.out = NULL) expect_equal(dat.calib.pv.1[["plotdata"]][[1]][1:3, "pv"], dat.calib.pv.ids.1[[1]][,2]) expect_equal(dat.calib.pv.1[["plotdata"]][[6]][1:3, "pv"], dat.calib.pv.ids.1[[1]][,7]) ## Calculate observed event probabilities with a confidence interval using bootstrapping and transitions.out = NULL expect_warning(calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "loess", CI = 95, CI.type = "bootstrap", CI.R.boot = 3, tp.pred.plot = NULL, transitions.out = c(1))) dat.calib.pv.4 <- suppressWarnings(calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "loess", CI = 95, CI.type = "bootstrap", CI.R.boot = 3, tp.pred.plot = NULL, transitions.out = c(1,2))) expect_equal(class(dat.calib.pv.4), c("calib_pv", "calib_msm")) expect_equal(ncol(dat.calib.pv.4[["plotdata"]][[1]]), 5) expect_equal(dat.calib.pv.1[["plotdata"]][[1]]$obs, dat.calib.pv.4[["plotdata"]][[1]]$obs) expect_equal(dat.calib.pv.1[["plotdata"]][[1]]$pred, dat.calib.pv.4[["plotdata"]][[1]]$pred) expect_equal(dat.calib.pv.1[["plotdata"]][[2]]$obs, dat.calib.pv.4[["plotdata"]][[2]]$obs) expect_equal(dat.calib.pv.1[["plotdata"]][[2]]$pred, dat.calib.pv.4[["plotdata"]][[2]]$pred) expect_no_error(summary(dat.calib.pv.4)) ## Calculate observed event probabilities with a confidence interval using bootstrapping, transitions.out = NULL and defining tp.pred.plot manually ### Create landmark ids and extract tp.pred.plot correct id.lmk <- 1:50 tp.pred.plot <- tps0 |> dplyr::filter(id %in% id.lmk) |> dplyr::filter(j == 1) |> dplyr::select(any_of(paste("pstate", 1:6, sep = ""))) ## No confidence interval dat.calib.pv.9 <- suppressWarnings(calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "loess", tp.pred.plot = tp.pred.plot, transitions.out = NULL)) ## Should be one less column in plotdata (no patient ids) expect_equal(class(dat.calib.pv.9), c("calib_pv", "calib_msm")) expect_equal(ncol(dat.calib.pv.9[["plotdata"]][[1]]), 3) expect_equal(nrow(dat.calib.pv.9[["plotdata"]][[1]]), 50) expect_equal(dat.calib.pv.1[["plotdata"]][[1]]$obs, dat.calib.pv.9[["plotdata"]][[1]]$obs) expect_equal(dat.calib.pv.1[["plotdata"]][[1]]$pred, dat.calib.pv.9[["plotdata"]][[1]]$pred) expect_no_error(summary(dat.calib.pv.9)) ## With confidence interval dat.calib.pv.10 <- suppressWarnings(calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "loess", CI = 95, CI.type = "bootstrap", CI.R.boot = 3, tp.pred.plot = tp.pred.plot, transitions.out = NULL)) expect_equal(class(dat.calib.pv.10), c("calib_pv", "calib_msm")) expect_equal(ncol(dat.calib.pv.10[["plotdata"]][[1]]), 4) expect_equal(nrow(dat.calib.pv.10[["plotdata"]][[1]]), 50) expect_equal(dat.calib.pv.1[["plotdata"]][[1]]$obs, dat.calib.pv.10[["plotdata"]][[1]]$obs) expect_equal(dat.calib.pv.1[["plotdata"]][[1]]$pred, dat.calib.pv.10[["plotdata"]][[1]]$pred) expect_no_error(summary(dat.calib.pv.10)) }) ### Run tests for when curve.type = "loess" and CI.type = "bootstrap". test_that("check calib_pv output, (j = 1, s = 0), curve.type = loess, CI.type = parametric", { ## 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.pv.1 <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "loess", tp.pred.plot = NULL, transitions.out = NULL) expect_equal(dat.calib.pv.1[["metadata"]][["curve.type"]], "loess") expect_equal(ncol(dat.calib.pv.1[["plotdata"]][[1]]), 4) expect_no_error(summary(dat.calib.pv.1)) ## Calculate observed event probabilities with a confidence interval using parametric approach dat.calib.pv.5 <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "loess", CI = 95, CI.type = "parametric", tp.pred.plot = NULL, transitions.out = c(1,2)) expect_equal(ncol(dat.calib.pv.5[["plotdata"]][[1]]), 6) expect_equal(dat.calib.pv.1[["plotdata"]][[1]]$obs, dat.calib.pv.5[["plotdata"]][[1]]$obs) expect_equal(dat.calib.pv.1[["plotdata"]][[1]]$pred, dat.calib.pv.5[["plotdata"]][[1]]$pred) expect_equal(dat.calib.pv.1[["plotdata"]][[2]]$obs, dat.calib.pv.5[["plotdata"]][[2]]$obs) expect_equal(dat.calib.pv.1[["plotdata"]][[2]]$pred, dat.calib.pv.5[["plotdata"]][[2]]$pred) expect_no_error(summary(dat.calib.pv.5)) ## Calculate observed event probabilities with a confidence interval using bootstrapping, transitions.out = NULL and defining tp.pred.plot manually ### Create landmark ids and extract tp.pred.plot correct id.lmk <- 1:50 tp.pred.plot <- tps0 |> dplyr::filter(id %in% id.lmk) |> dplyr::filter(j == 1) |> dplyr::select(any_of(paste("pstate", 1:6, sep = ""))) ## With confidence interval dat.calib.pv.10 <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "loess", CI = 95, CI.type = "parametric", tp.pred.plot = tp.pred.plot, transitions.out = NULL) str(dat.calib.pv.10) expect_equal(ncol(dat.calib.pv.10[["plotdata"]][[1]]), 5) expect_equal(dat.calib.pv.1[["plotdata"]][[1]]$obs, dat.calib.pv.10[["plotdata"]][[1]]$obs) expect_equal(dat.calib.pv.1[["plotdata"]][[1]]$pred, dat.calib.pv.10[["plotdata"]][[1]]$pred) expect_no_error(summary(dat.calib.pv.10)) }) ### Run tests for when curve.type = "rcs" and CI.type = "bootstrap" (not rerunning all of them for curve.type = rcs) test_that("check calib_pv output, (j = 1, s = 0), curve.type = rcs, CI.type = bootstrap.", { skip_on_cran() ## Reduce to 150 individuals # Extract the predicted transition probabilities out of state j = 1 for first 150 individuals tp.pred <- tps0 |> dplyr::filter(id %in% 1:150) |> dplyr::filter(j == 1) |> dplyr::select(any_of(paste("pstate", 1:6, sep = ""))) # Reduce ebmtcal to first 150 individuals ebmtcal <- ebmtcal |> dplyr::filter(id %in% 1:150) # Reduce msebmtcal.cmprsk to first 150 individuals msebmtcal <- msebmtcal |> dplyr::filter(id %in% 1:150) ## Calculate observed event probabilities using transitions.out = NULL dat.calib.pv.1 <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "rcs", tp.pred.plot = NULL, transitions.out = c(1)) expect_equal(dat.calib.pv.1[["metadata"]][["curve.type"]], "rcs") expect_equal(ncol(dat.calib.pv.1[["plotdata"]][[1]]), 4) expect_no_error(summary(dat.calib.pv.1)) ## Calculate observed event probabilities with a confidence interval using bootstrapping dat.calib.pv.4 <- suppressWarnings(calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "rcs", CI = 95, CI.type = "bootstrap", CI.R.boot = 3, tp.pred.plot = NULL, transitions.out = c(1))) expect_equal(ncol(dat.calib.pv.4[["plotdata"]][[1]]), 5) expect_equal(dat.calib.pv.1[["plotdata"]][[1]]$obs, dat.calib.pv.4[["plotdata"]][[1]]$obs) expect_equal(dat.calib.pv.1[["plotdata"]][[1]]$pred, dat.calib.pv.4[["plotdata"]][[1]]$pred) expect_no_error(summary(dat.calib.pv.4)) }) ### Run tests for when curve.type = "rcs" and CI.type = "parametric" (not rerunning all of them for curve.type = rcs) test_that("check calib_pv output, (j = 1, s = 0), curve.type = rcs, CI.type = bootstrap.", { skip_on_cran() ## Reduce to 150 individuals # Extract the predicted transition probabilities out of state j = 1 for first 150 individuals tp.pred <- tps0 |> dplyr::filter(id %in% 1:150) |> dplyr::filter(j == 1) |> dplyr::select(any_of(paste("pstate", 1:6, sep = ""))) # Reduce ebmtcal to first 150 individuals ebmtcal <- ebmtcal |> dplyr::filter(id %in% 1:150) # Reduce msebmtcal.cmprsk to first 150 individuals msebmtcal <- msebmtcal |> dplyr::filter(id %in% 1:150) ## Calculate observed event probabilities using transitions.out = NULL dat.calib.pv.1 <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "rcs", tp.pred.plot = NULL, transitions.out = c(1)) expect_equal(dat.calib.pv.1[["metadata"]][["curve.type"]], "rcs") expect_equal(ncol(dat.calib.pv.1[["plotdata"]][[1]]), 4) expect_no_error(summary(dat.calib.pv.1)) ## Calculate observed event probabilities with a confidence interval using parametric approach dat.calib.pv.4 <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "rcs", CI = 95, CI.type = "parametric", tp.pred.plot = NULL, transitions.out = c(1)) expect_equal(ncol(dat.calib.pv.4[["plotdata"]][[1]]), 6) expect_equal(dat.calib.pv.1[["plotdata"]][[1]]$obs, dat.calib.pv.4[["plotdata"]][[1]]$obs) expect_equal(dat.calib.pv.1[["plotdata"]][[1]]$pred, dat.calib.pv.4[["plotdata"]][[1]]$pred) expect_no_error(summary(dat.calib.pv.4)) }) ### Add some tests for when each of group.vars and pv.n.pctls are specified test_that("check calib_pv output, (j = 1, s = 0), groups.vars and pv.n.pctls specified", { skip_on_cran() ## Reduce to 50 individuals # Extract the predicted transition probabilities out of state j = 1 for first 100 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 when both pv.group.vars and pv.n.pctls are specified dat.calib.pv.1 <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "loess", loess.span = 1, loess.degree = 1, pv.group.vars = c("year"), pv.n.pctls = 2, tp.pred.plot = NULL, transitions.out = NULL) expect_equal(ncol(dat.calib.pv.1[["plotdata"]][[1]]), 4) expect_equal(length(dat.calib.pv.1[["plotdata"]]), 6) ## Check same results when just calculating pseudo-values for first three individuals dat.calib.pv.ids.1 <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', pv.group.vars = c("year"), pv.n.pctls = 2, pv.ids = 1:3, tp.pred.plot = NULL, transitions.out = NULL) expect_equal(dat.calib.pv.1[["plotdata"]][[1]][1:3, "pv"], dat.calib.pv.ids.1[[1]][,2]) expect_equal(dat.calib.pv.1[["plotdata"]][[6]][1:3, "pv"], dat.calib.pv.ids.1[[1]][,7]) ## Check same results when just calculating pseudo-values for first three individuals, but specify transitions 1 and 6 dat.calib.pv.ids.1.tout <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', pv.group.vars = c("year"), pv.n.pctls = 2, pv.ids = 1:3, tp.pred.plot = NULL, transitions.out = c(1,6)) expect_equal(dat.calib.pv.ids.1.tout[[1]][,2], dat.calib.pv.ids.1[[1]][,2]) expect_equal(dat.calib.pv.ids.1.tout[[1]][,3], dat.calib.pv.ids.1[[1]][,7]) expect_equal(ncol(dat.calib.pv.ids.1.tout[["plotdata"]]), 3) ## Calculate observed event probabilities for pv.group.vars dat.calib.pv.2 <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "loess", loess.span = 1, loess.degree = 1, pv.group.vars = c("year"), tp.pred.plot = NULL, transitions.out = NULL) expect_equal(ncol(dat.calib.pv.2[["plotdata"]][[1]]), 4) expect_equal(length(dat.calib.pv.2[["plotdata"]]), 6) ## Check same results when just calculating pseudo-values for first three individuals dat.calib.pv.ids.2 <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', pv.group.vars = c("year"), pv.ids = 1:3, tp.pred.plot = NULL, transitions.out = NULL) expect_equal(dat.calib.pv.2[["plotdata"]][[1]][1:3, "pv"], dat.calib.pv.ids.2[[1]][,2]) expect_equal(dat.calib.pv.2[["plotdata"]][[6]][1:3, "pv"], dat.calib.pv.ids.2[[1]][,7]) ## No need to test for transitions.out when pv.n.pctls not specified, because there are no computational gains and ## pseudo-values are just calculated for all states anyway. ## Calculate observed event probabilities for pv.n.pctls dat.calib.pv.3 <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "loess", loess.span = 1, loess.degree = 1, pv.n.pctls = 2, tp.pred.plot = NULL, transitions.out = NULL) expect_equal(ncol(dat.calib.pv.3[["plotdata"]][[1]]), 4) expect_equal(length(dat.calib.pv.3[["plotdata"]]), 6) ## Check same results when just calculating pseudo-values for first three individuals dat.calib.pv.ids.3 <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', pv.n.pctls = 2, pv.ids = 1:3, tp.pred.plot = NULL, transitions.out = NULL) expect_equal(dat.calib.pv.3[["plotdata"]][[1]][1:3, "pv"], dat.calib.pv.ids.3[[1]][,2]) expect_equal(dat.calib.pv.3[["plotdata"]][[6]][1:3, "pv"], dat.calib.pv.ids.3[[1]][,7]) ## Check same results when just calculating pseudo-values for first three individuals, but specify transitions 1 and 6 dat.calib.pv.ids.3.tout <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', pv.n.pctls = 2, pv.ids = 1:3, tp.pred.plot = NULL, transitions.out = c(1,6)) expect_equal(dat.calib.pv.ids.3.tout[[1]][,2], dat.calib.pv.ids.3[[1]][,2]) expect_equal(dat.calib.pv.ids.3.tout[[1]][,3], dat.calib.pv.ids.3[[1]][,7]) expect_equal(ncol(dat.calib.pv.ids.3.tout[["plotdata"]]), 3) }) ### Add some tests where we expect errors, if requesting things that aren't possible test_that("check calib_pv output, (j = 1, s = 0), cause errors", { skip_on_cran() ## Reduce to 50 individuals # Extract the predicted transition probabilities out of state j = 1 for first 100 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) ## Request bootstrap confidence interval and don't give number of bootstrap replicates (for either rcs or parametric) expect_error(calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "loess", CI = 95, CI.type = "bootstrap", tp.pred.plot = NULL, transitions.out = NULL)) expect_error(calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "rcs", CI = 95, CI.type = "bootstrap", tp.pred.plot = NULL, transitions.out = NULL)) }) test_that("check calib_pv output, (j = 3, s = 100), pv.group.vars defined", { skip_on_cran() ## Extract relevant predicted risks from tps100 tp.pred <- dplyr::select(dplyr::filter(tps100, j == 3), any_of(paste("pstate", 1:6, sep = ""))) ## Calculate observed event probabilities dat.calib.pv <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j=3, s=100, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "rcs", rcs.nk = 3, pv.group.vars = c("year")) expect_type(dat.calib.pv, "list") expect_equal(class(dat.calib.pv), c("calib_pv", "calib_msm")) expect_length(dat.calib.pv[["plotdata"]], 4) expect_length(dat.calib.pv[["plotdata"]][["state3"]]$id, 413) expect_length(dat.calib.pv[["plotdata"]][["state6"]]$id, 413) expect_error(dat.calib.pv[["plotdata"]][[6]]) expect_false(dat.calib.pv[["metadata"]]$CI) }) test_that("check calib_pv output, (j = 3, s = 100), pv.n.pctls defined", { skip_on_cran() ## Extract relevant predicted risks from tps100 tp.pred <- dplyr::select(dplyr::filter(tps100, j == 3), any_of(paste("pstate", 1:6, sep = ""))) ## Calculate observed event probabilities dat.calib.pv <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j=3, s=100, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "rcs", rcs.nk = 3, pv.n.pctls = 2) expect_type(dat.calib.pv, "list") expect_equal(class(dat.calib.pv), c("calib_pv", "calib_msm")) expect_length(dat.calib.pv[["plotdata"]], 4) expect_length(dat.calib.pv[["plotdata"]][["state3"]]$id, 413) expect_length(dat.calib.pv[["plotdata"]][["state6"]]$id, 413) expect_error(dat.calib.pv[["plotdata"]][[6]]) expect_false(dat.calib.pv[["metadata"]]$CI) }) test_that("check calib_pv output, (j = 3, s = 100), pv.group.vars and pv.n.pctls defined", { skip_on_cran() ## Extract relevant predicted risks from tps100 tp.pred <- dplyr::select(dplyr::filter(tps100, j == 3), any_of(paste("pstate", 1:6, sep = ""))) ## Calculate observed event probabilities dat.calib.pv <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j=3, s=100, t = 1826, tp.pred = tp.pred, calib.type = 'pv', curve.type = "rcs", rcs.nk = 3, pv.group.vars = c("year"), pv.n.pctls = 2) expect_type(dat.calib.pv, "list") expect_equal(class(dat.calib.pv), c("calib_pv", "calib_msm")) expect_length(dat.calib.pv[["plotdata"]], 4) expect_length(dat.calib.pv[["plotdata"]][["state3"]]$id, 413) expect_length(dat.calib.pv[["plotdata"]][["state6"]]$id, 413) expect_error(dat.calib.pv[["plotdata"]][[6]]) expect_false(dat.calib.pv[["metadata"]]$CI) }) test_that("check calib_pv output, (j = 1, s = 0), pv.precalc", { skip_on_cran() ## Extract relevant predicted risks from tps100 tp.pred <- dplyr::select(dplyr::filter(tps0, j == 1), any_of(paste("pstate", 1:6, sep = ""))) ## Define pv.precalc to be the estimated predicted probabilities pv.precalc <- tp.pred ## Calculate observed event probabilities dat.calib.pv <- calib_msm(data.ms = msebmtcal, data.raw = ebmtcal, j = 1, s = 0, t = 1826, tp.pred = tp.pred, calib.type = 'pv', pv.precalc = tp.pred, curve.type = "rcs", rcs.nk = 3) expect_type(dat.calib.pv, "list") expect_equal(class(dat.calib.pv), c("calib_pv", "calib_msm")) expect_length(dat.calib.pv[["plotdata"]], 6) expect_length(dat.calib.pv[["plotdata"]][["state3"]]$id, 2279) expect_length(dat.calib.pv[["plotdata"]][["state6"]]$id, 2279) expect_false(dat.calib.pv[["metadata"]]$CI) })