Computes Marginal Measures Using Two Observations
Computes bivariate figures such as conditional CIFs, survival probabilities, and hazards or bivariate CIFs, densities, and survival probabilities.
mmcif_pd_cond( par, object, newdata, cause, time, left_trunc = NULL, ghq_data = object$ghq_data, strata = NULL, which_cond, type_cond = "derivative", type_obs = "cumulative" ) mmcif_pd_bivariate( par, object, newdata, cause, time, left_trunc = NULL, ghq_data = object$ghq_data, strata = NULL, use_log = FALSE, type = c("cumulative", "cumulative") )
par: numeric vector with the model parameters.
object: an object from mmcif_data.
newdata: a data.frame with data for the observations. It needs to have two rows.
cause: an integer vector with the cause of each outcome. If there are n_causes of outcome, then the vector should have values in 1:(n_causes + 1) with n_causes + 1 indicating censoring.
time: a numeric vector with the observed times.
left_trunc: numeric vector with left-truncation times. NULL
implies that there are not any individuals with left-truncation.
ghq_data: the Gauss-Hermite quadrature nodes and weights to use. It should be a list with two elements called "node"
and "weight". A default is provided if NULL is passed.
strata: an integer vector or a factor vector with the strata of each individual. NULL implies that there are no strata.
which_cond: an integer with value one or two for the index of the individual that is being conditioned on.
type_cond: a character for the type of outcome that is being conditioned on. "derivative" for the derivative of a CIF or "cumulative" for a CIF or the survival probability.
type_obs: a character the type of conditional measure. It can be "derivative" for the derivative of a CIF, "cumulative" for a CIF or the survival probability, and "hazard" for the hazard.
use_log: a logical for whether the returned output should be on the log scale.
type: a 2D character vector for the type of measures for each observation. The elements can be "derivative" for the derivative of a CIF or "cumulative" for a CIF or the survival probability.
A numeric scalar with the requested quantity.
if(require(mets)){ data(prt) # truncate the time max_time <- 90 prt <- within(prt, { status[time >= max_time] <- 0 time <- pmin(time, max_time) }) # select the DZ twins and re-code the status prt_use <- subset(prt, zyg == "DZ") |> transform(status = ifelse(status == 0, 3L, status)) # Gauss Hermite quadrature nodes and weights from fastGHQuad::gaussHermiteData ghq_data <- list( node = c(-3.43615911883774, -2.53273167423279, -1.75668364929988, -1.03661082978951, -0.342901327223705, 0.342901327223705, 1.03661082978951, 1.75668364929988, 2.53273167423279, 3.43615911883774), weight = c(7.6404328552326e-06, 0.00134364574678124, 0.0338743944554811, 0.240138611082314, 0.610862633735326,0.610862633735326, 0.240138611082315, 0.033874394455481, 0.00134364574678124, 7.64043285523265e-06)) # setup the object for the computation mmcif_obj <- mmcif_data( ~ country - 1, prt_use, status, time, id, max_time, 2L, strata = country, ghq_data = ghq_data) # previous estimates par <- c(0.727279974859164, 0.640534073288067, 0.429437766165371, 0.434367104339573, -2.4737847536253, -1.49576564624673, -1.89966050143904, -1.58881346649412, -5.5431198001029, -3.5328359024178, -5.82305147022587, -3.4531896212114, -5.29132887832377, -3.36106297109548, -6.03690322125729, -3.49516746825624, 2.55000711185704, 2.71995985605891, 2.61971498736444, 3.05976391058032, -5.97173564860957, -3.37912051983482, -5.14324860374941, -3.36396780694965, -6.02337246348561, -3.03754644968859, -5.51267338700737, -3.01148582224673, 2.69665543753264, 2.59359057553995, 2.7938341786374, 2.70689750644755, -0.362056555418564, 0.24088005091276, 0.124070380635372, -0.246152029808377, -0.0445628476462479, -0.911485513197845, -0.27911988106887, -0.359648419277058, -0.242711959678559, -6.84897302527358) # the test data we will use test_dat <- data.frame( country = factor(c("Norway", "Norway"), levels(prt_use$country)), status = c(1L, 2L), time = c(60, 75)) # probability that both experience the event prior to the two times mmcif_pd_bivariate( par = par, object = mmcif_obj, newdata = test_dat, cause = status, strata = country, ghq_data = ghq_data, time = time, type = c("cumulative", "cumulative")) |> print() # density that one experiences an event at the point and the other # experiences an event prior to the point mmcif_pd_bivariate( par = par, object = mmcif_obj, newdata = test_dat, cause = status, strata = country, ghq_data = ghq_data, time = time, type = c("derivative", "cumulative")) |> print() # probability that both survive up to the passed points mmcif_pd_bivariate( par = par, object = mmcif_obj, newdata = test_dat, cause = c(3L, 3L), strata = country, ghq_data = ghq_data, time = time, type = c("cumulative", "cumulative")) |> print() # conditional hazard given that the other experiences an event prior to time mmcif_pd_cond( par = par, object = mmcif_obj, newdata = test_dat, cause = status, strata = country, ghq_data = ghq_data, time = time, which_cond = 1L, type_cond = "cumulative", type_obs = "hazard") |> print() # conditional CIF given that the other experiences an event prior to the # time mmcif_pd_cond( par = par, object = mmcif_obj, newdata = test_dat, cause = c(2L, 2L), strata = country, ghq_data = ghq_data, time = time, which_cond = 1L, type_cond = "cumulative", type_obs = "cumulative") |> print() # same but given that the other experiences the event at the point mmcif_pd_cond( par = par, object = mmcif_obj, newdata = test_dat, cause = c(2L, 2L), strata = country, ghq_data = ghq_data, time = time, which_cond = 1L, type_cond = "derivative", type_obs = "cumulative") |> print() }
mmcif_pd_univariate and mmcif_fit.