Point-wise prediction hazard 2 time scale

Point-wise prediction hazard 2 time scale

Usage

predict_haz2ts_pointwise(fitted_model, u, s, ds = NULL)

Arguments

fitted_model

An object of class 'haz2ts' fitted via fit2ts().

u

The value(s) of u where prediction is required

s

The value(s) of s where prediction is required

ds

(optional) The distance between two consecutive points on the s axis. If not provided, an optimal minimum value will be chosen automatically and a warning is returned.

Value

A data.frame with one row and 6 variable: the values of u and s for which predictions of hazard, se_hazard, the cumulative hazard cumhaz and the survival probability are obtained

Examples

id <- 1:20
u <- c(
  5.43, 3.25, 8.15, 5.53, 7.28, 6.61, 5.91, 4.94, 4.25, 3.86, 4.05, 6.86,
  4.94, 4.46, 2.14, 7.56, 5.55, 7.60, 6.46, 4.96
)
s <- c(
  0.44, 4.89, 0.92, 1.81, 2.02, 1.55, 3.16, 6.36, 0.66, 2.02, 1.22, 3.96,
  7.07, 2.91, 3.38, 2.36, 1.74, 0.06, 5.76, 3.00
)
ev <- c(1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1)
fakedata <- as.data.frame(cbind(id, u, s, ev))
fakedata2ts <- prepare_data(
  u = fakedata$u,
  s_out = fakedata$s,
  ev = fakedata$ev,
  ds = .5
)
#> `s_in = NULL`. I will use `s_in = 0` for all observations.
#> `s_in = NULL`. I will use `s_in = 0` for all observations.
# Fit a fake model - not optimal smoothing
fakemod <- fit2ts(fakedata2ts,
  optim_method = "grid_search",
  lrho = list(
    seq(1, 1.5, .5),
    seq(1, 1.5, .5)
  )
)
predict_haz2ts_pointwise(fakemod, u = 5, s = 4.44)
#> chosen interval: ds = 0.4
#>     u   s    hazard se_hazard    cumhaz  survival
#> 161 5 4.6 0.1632987 0.1021754 0.7466594 0.4739472