set.seed(1)
data(data_repeat_outcomes)
data_repeat_outcomes <-
find_LOCF_risk_set(
data_long = data_repeat_outcomes,
individual_id = "id",
event_time = "event_time",
event_status = "event_status",
covariates = c("ethnicity", "smoking", "diabetes", "sbp_stnd", "tchdl_stnd"),
covariates_time = c(rep("response_time_sbp_stnd", 4), "response_time_tchdl_stnd"),
x_L = c(60,61),
x_hor= c(65,66)
)
data_model_landmark_LOCF <-
fit_LOCF_landmark(
data_long = data_repeat_outcomes,
x_L = c(60, 61),
x_hor = c(65, 66),
covariates = c("ethnicity", "smoking", "diabetes", "sbp_stnd", "tchdl_stnd"),
covariates_time = c(rep("response_time_sbp_stnd", 4), "response_time_tchdl_stnd"),
k = 10,
individual_id = "id",
event_time = "event_time",
event_status = "event_status",
survival_submodel = "cause_specific"
)
cross_validation_list <- lapply(data_model_landmark_LOCF, "[[", i = 1)
data_model_landmark_LME <-
fit_LME_landmark(
data_long = data_repeat_outcomes[["60"]],
x_L = c(60),
x_hor = c(65),
cross_validation_df =
cross_validation_list,
predictors_LME = c("ethnicity", "smoking", "diabetes"),
predictors_LME_time =
"response_time_sbp_stnd",
responses_LME = c("sbp_stnd", "tchdl_stnd"),
responses_LME_time = c("response_time_sbp_stnd", "response_time_tchdl_stnd"),
individual_id = "id",
standardise_time = TRUE,
lme_control = nlme::lmeControl(maxIter = 100, msMaxIter = 100),
event_time = "event_time",
event_status = "event_status",
survival_submodel = "cause_specific"
)
save_file <- function(code) {
path <- tempfile(fileext = ".RDS")
saveRDS(code,file = path)
path
}
test_that("fitting landmark (LOCF)", {
expect_snapshot_file(save_file(
data_model_landmark_LOCF), "out_data_model_landmark_LOCF.RDS",cran = FALSE)
})
test_that("fitting landmark (LME)", {
expect_snapshot_file(save_file(
data_model_landmark_LME), "out_data_model_landmark_LME.RDS",cran = FALSE)
})
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