Prediction method for objects of class 'haz2ts' — predict

Prediction method for objects of class 'haz2ts'

Usage

predict_haz2ts(
  x,
  newdata = NULL,
  originaldata = NULL,
  u,
  s,
  id = NULL,
  ds = NULL
)

Arguments

x: an object of class 'haz2ts', the output of the function fit2ts().
newdata: (optional) A dataframe with columns cointaing the values of the variable u and the time scale s for which predictions are to be obtained.
originaldata: (optional) The original dataset. Provide it to obtain individual predictions for each observation in the data.
u: The name of the variable in newdata, or in originaldata containing values for the variable u.
s: The name of the variable in newdata, or in originaldata containing values for the variable s. Note that over the s axis predictions are provided only within intervals of values, as it is necessary to approximate cumulated quantities.
id: (optional) The name of the variable in newdata, or in originaldata containing the identification of each observation. It is not required for predictions on a new dataset.
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 dataframe. This can be the original dataframe (originaldata), where only the variables id, u and s are selected, or the new data frame (newdata), together with the predicted values for the hazard hazard and its standard errors se_hazard, the cumulative hazard cumhazard and the survival probability survival.

Details

Predictions of cumulated quantities can be provided only within intervals of values on the s time scale.

Examples

# Create the same fake data as in other 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)
  )
)
# Create a new dataset for prediction
newdata <- as.data.frame(cbind("u" = c(2.5, 3.4, 6), "s" = c(.2, .5, 1.3)))

# First - predict on original data
predict(object = fakemod,
  originaldata = fakedata, u = "u", s = "s", id = "id"
)
#>    id    u    s     hazard  se_hazard   cumhazard  survival
#> 1   1 5.43 0.44 0.13740670 0.07599797 0.068130275 0.9341388
#> 2   2 3.25 4.89 0.19402638 0.26679324 0.878037756 0.4155976
#> 3   3 8.15 0.92 0.07537982 0.08786874 0.072945278 0.9296517
#> 4   4 5.53 1.81 0.14230098 0.05878897 0.261603915 0.7698159
#> 5   5 7.28 2.02 0.09957468 0.07538578 0.197452069 0.8208195
#> 6   6 6.61 1.55 0.11306136 0.06689691 0.174471579 0.8399007
#> 7   7 5.91 3.16 0.13795103 0.07874811 0.419712583 0.6572357
#> 8   8 4.94 6.36 0.17380895 0.16712236 1.023687049 0.3592679
#> 9   9 4.25 0.66 0.16062214 0.09048239 0.111104019 0.8948457
#> 10 10 3.86 2.02 0.17251792 0.08212664 0.350826575 0.7041059
#> 11 11 4.05 1.22 0.16653113 0.08299609 0.211805170 0.8091223
#> 12 12 6.86 3.96 0.11983805 0.13696457 0.436677400 0.6461799
#> 13 13 4.94 7.07 0.17804729 0.19944893 1.147048980 0.3175726
#> 14 14 4.46 2.91 0.16848507 0.07543581 0.486023334 0.6150675
#> 15 15 2.14 3.38 0.19342902 0.25092140 0.611328238 0.5426296
#> 16 16 7.56 2.36 0.09528778 0.08449232 0.212827761 0.8082953
#> 17 17 5.55 1.74 0.14135558 0.05906538 0.246494518 0.7815356
#> 18 18 7.60 0.06 0.08160222 0.10813840 0.008160222 0.9918730
#> 19 19 6.46 5.76 0.13931938 0.21728709 0.717137521 0.4881476
#> 20 20 4.96 3.00 0.15953865 0.06751365 0.475091887 0.6218279

# Now - predict on new dataset
predict(object = fakemod,
  newdata = newdata, u = "u", s = "s"
)
#> chosen interval: ds = 0.2
#> Warning: Right boundary adjusted to max(x) = 7.5
#> Warning: Right boundary adjusted to max(x) = 7.5
#> Warning: Right boundary adjusted to max(x) = 7.5
#>     u   s    hazard se_hazard  cumhazard  survival
#> 1 2.5 0.2 0.1689391 0.2039663 0.06733798 0.9348792
#> 2 3.4 0.5 0.1670538 0.1338833 0.09949784 0.9052919
#> 3 6.0 1.3 0.1276804 0.0642310 0.17422551 0.8401074