Nothing
test_that("check plot.calib_msm output (j = 1, s = 0)", {
## Extract relevant predicted risks from tps0
tp.pred <- dplyr::select(dplyr::filter(tps0, j == 1), any_of(paste("pstate", 1:6, sep = "")))
## Calculate observed event probabilities
dat.calib.blr <-
calib_msm(data.ms = msebmtcal,
data.raw = ebmtcal,
j=1,
s=0,
t = 1826,
tp.pred = tp.pred,
calib.type = "blr",
curve.type = "rcs",
rcs.nk = 3,
w.covs = c("year", "agecl", "proph", "match"))
## Plot calibration plots and run tests
plot.object <- plot(dat.calib.blr, combine = TRUE, nrow = 2, ncol = 3)
expect_equal(class(plot.object), c("gg", "ggplot", "ggarrange"))
plot.object <- plot(dat.calib.blr, combine = FALSE, nrow = 2, ncol = 3)
expect_length(plot.object, 6)
expect_type(plot.object, "list")
## Plot calibration plots and run tests with marginal density plots
plot.object <- plot(dat.calib.blr, combine = TRUE, nrow = 2, ncol = 3, marg.density = TRUE, marg.density.size = 1)
expect_length(plot.object, 6)
expect_equal(class(plot.object), c("gtable", "gTree", "grob", "gDesc"))
## Plot calibration plots and run tests with marginal rug plots
plot.object <- plot(dat.calib.blr, combine = TRUE, nrow = 2, ncol = 3, marg.rug = TRUE)
expect_equal(class(plot.object), c("gg", "ggplot", "ggarrange"))
## Add titles
plot.object <- plot(dat.calib.blr, combine = TRUE, nrow = 2, ncol = 3, marg.rug = TRUE,
titles = paste("eggs", 1:6),
axis.titles.text.x = paste("eggs.x", 1:6),
axis.titles.text.y = paste("eggs.y", 1:6))
expect_equal(class(plot.object), c("gg", "ggplot", "ggarrange"))
})
test_that("check plot.calib_msm output (j = 1, s = 0) with CI", {
## Extract relevant predicted risks from tps0
tp.pred <- dplyr::select(dplyr::filter(tps0, j == 1), any_of(paste("pstate", 1:6, sep = "")))
## Calculate observed event probabilities
dat.calib.blr <-
calib_msm(data.ms = msebmtcal,
data.raw = ebmtcal,
j=1,
s=0,
t = 1826,
tp.pred = tp.pred,
calib.type = "blr",
curve.type = "rcs",
rcs.nk = 3,
w.covs = c("year", "agecl", "proph", "match"),
CI = 95,
CI.R.boot = 5)
## Plot calibration plots and run tests without marginal density plots
plot.object <- plot(dat.calib.blr, combine = TRUE, nrow = 2, ncol = 3)
expect_equal(class(plot.object), c("gg", "ggplot", "ggarrange"))
plot.object <- plot(dat.calib.blr, combine = FALSE, nrow = 2, ncol = 3)
expect_length(plot.object, 6)
expect_type(plot.object, "list")
## Plot calibration plots and run tests with marginal density plots
plot.object <- plot(dat.calib.blr, combine = TRUE, nrow = 2, ncol = 3, marg.density = TRUE, marg.density.size = 1)
expect_equal(class(plot.object), c("gtable", "gTree", "grob", "gDesc"))
## Plot calibration plots and run tests with marginal rug plots
plot.object <- plot(dat.calib.blr, combine = TRUE, nrow = 2, ncol = 3, marg.rug = TRUE)
expect_equal(class(plot.object), c("gg", "ggplot", "ggarrange"))
})
test_that("check plot.calib_msm output (j = 3, s = 100)", {
## Extract relevant predicted risks from tps0
tp.pred <- dplyr::select(dplyr::filter(tps100, j == 3), any_of(paste("pstate", 1:6, sep = "")))
## Calculate observed event probabilities
dat.calib.blr <-
calib_msm(data.ms = msebmtcal,
data.raw = ebmtcal,
j=3,
s=100,
t = 1826,
tp.pred = tp.pred,
calib.type = "blr",
curve.type = "rcs",
rcs.nk = 3,
w.covs = c("year", "agecl", "proph", "match"))
## Plot calibration plots and run tests
plot.object <- plot(dat.calib.blr, combine = TRUE, nrow = 2, ncol = 3)
expect_equal(class(plot.object), c("gg", "ggplot", "ggarrange"))
plot.object <- plot(dat.calib.blr, combine = FALSE, nrow = 2, ncol = 3)
expect_length(plot.object, 4)
expect_type(plot.object, "list")
})
test_that("check plot.calib_pv output (j = 1, s = 0)", {
## 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
dat.calib.pv <-
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",
rcs.nk = 3))
## Plot calibration plots and run tests
plot.object <- plot(dat.calib.pv, combine = TRUE)
expect_equal(class(plot.object), c("gg", "ggplot", "ggarrange"))
plot.object <- plot(dat.calib.pv, combine = FALSE)
expect_length(plot.object, 6)
expect_type(plot.object, "list")
})
test_that("check plot.calib_pv output (j = 1, s = 0) with CI", {
## 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
dat.calib.pv <-
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",
rcs.nk = 3,
CI = 95,
CI.type = "parametric"))
## Plot calibration plots and run tests
plot.object <- plot(dat.calib.pv, combine = TRUE)
expect_equal(class(plot.object), c("gg", "ggplot", "ggarrange"))
plot.object <- plot(dat.calib.pv, combine = FALSE)
expect_length(plot.object, 6)
expect_type(plot.object, "list")
})
test_that("check plot.calib_pv output (j = 3, s = 100) with CI", {
## Reduce to 500 individuals
# Extract the predicted transition probabilities out of state j = 1 for first 500 individuals
tp.pred <- tps0 |>
dplyr::filter(id %in% 1:500) |>
dplyr::filter(j == 3) |>
dplyr::select(any_of(paste("pstate", 1:6, sep = "")))
# Reduce ebmtcal to first 500 individuals
ebmtcal <- ebmtcal |> dplyr::filter(id %in% 1:500)
# Reduce msebmtcal.cmprsk to first 100 individuals
msebmtcal <- msebmtcal |> dplyr::filter(id %in% 1:500)
## 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,
CI = 95,
CI.type = "parametric")
## Plot calibration plots and run tests
plot.object <- plot(dat.calib.pv, combine = TRUE)
expect_equal(class(plot.object), c("gg", "ggplot", "ggarrange"))
plot.object <- plot(dat.calib.pv, combine = FALSE)
expect_length(plot.object, 4)
expect_type(plot.object, "list")
})
test_that("check plot.calib_mlr output (j = 1, s = 0)", {
## Reduce to 500 individuals
# Extract the predicted transition probabilities out of state j = 1 for first 500 individuals
tp.pred <- tps0 |>
dplyr::filter(id %in% 1:500) |>
dplyr::filter(j == 1) |>
dplyr::select(any_of(paste("pstate", 1:6, sep = "")))
# Reduce ebmtcal to first 500 individuals
ebmtcal <- ebmtcal |> dplyr::filter(id %in% 1:500)
# Reduce msebmtcal.cmprsk to first 100 individuals
msebmtcal <- msebmtcal |> dplyr::filter(id %in% 1:500)
# ## Extract relevant predicted risks from tps0
# tp.pred <- dplyr::select(dplyr::filter(tps100, j == 3), dplyr::any_of(paste("pstate", 1:6, sep = "")))
## Calculate observed event probabilities
suppressWarnings(
dat.calib.mlr <-
calib_msm(data.ms = msebmtcal,
data.raw = ebmtcal,
j=1,
s=0,
t = 1826,
tp.pred = tp.pred,
calib.type = "mlr",
w.covs = c("year", "agecl", "proph", "match"))
)
## Plot calibration plots and run tests
plot.object <- plot(dat.calib.mlr, combine = TRUE, nrow = 2, ncol = 3)
expect_equal(class(plot.object), c("gg", "ggplot", "ggarrange"))
plot.object <- plot(dat.calib.mlr, combine = FALSE, nrow = 2, ncol = 3)
expect_length(plot.object, 6)
expect_type(plot.object, "list")
})
test_that("check plot.calib_mlr output (j = 3, s = 100)", {
## Extract relevant predicted risks from tps0
tp.pred <- dplyr::select(dplyr::filter(tps100, j == 3), dplyr::any_of(paste("pstate", 1:6, sep = "")))
## Calculate observed event probabilities
suppressWarnings(
dat.calib.mlr <-
calib_msm(data.ms = msebmtcal,
data.raw = ebmtcal,
j=3,
s=100,
t = 1826,
tp.pred = tp.pred,
calib.type = "mlr",
w.covs = c("year", "agecl", "proph", "match"))
)
## Plot calibration plots and run tests
plot.object <- plot(dat.calib.mlr, combine = TRUE, nrow = 2, ncol = 3)
expect_equal(class(plot.object), c("gg", "ggplot", "ggarrange"))
plot.object <- plot(dat.calib.mlr, combine = FALSE, nrow = 2, ncol = 3)
expect_length(plot.object, 4)
expect_type(plot.object, "list")
})
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