Predictions from a fit loaded out of a SAS OUTHAZ= dataset — predict.hzr

Rebuilds the multiphase model the OUTHAZ= dataset describes – which phases are in it, their shapes, and the fitted parameter vector – and then predicts exactly as predict.hazard() does.

Usage

# S3 method for class 'hzr_outhaz'
predict(
  object,
  newdata,
  type = c("hazard", "survival", "cumulative_hazard"),
  decompose = FALSE,
  se.fit = FALSE,
  level = 0.95,
  conf.type = c("log-log", "logit"),
  ...
)

Arguments

object

An hzr_outhaz object from hzr_read_outhaz().

newdata

Data frame with a time column. Required.

type

One of "hazard", "survival" or "cumulative_hazard"; default "hazard", as in predict.hazard().

decompose

Accepted only to keep this method's argument list identical to predict.hazard()'s, so that a positional call means the same thing for both. TRUE is an error: an OUTHAZ= dataset names its phases but carries no per-phase decomposition, and silently returning the total prediction instead would answer a question that was not asked.

se.fit

Logical; add delta-method standard errors and confidence limits, as predict.hazard() does.

level

Numeric confidence level in (0, 1); default 0.95. Only used when se.fit = TRUE.

SAS draws narrower bands than this by default. PROC HAZPRED takes its width from CLEVEL, whose default is 0.68268948 — documented in the macro source as "(1 sd)" — so its T_ALPHA multiplier is 1 to seven decimals (the literal is truncated) and the band is one standard error, 68.3%, not 95%. Reproducing a SAS figure at this function's default therefore yields a band about 1.96 times wider than the one being checked against, with no error and no warning on either side. Pass the SAS level explicitly to match:

predict(fit, newdata, type = "survival", se.fit = TRUE,
        level = 2 * stats::pnorm(1) - 1, conf.type = "logit")

The default is left at 0.95 deliberately: it is the right R-side default, and silently adopting SAS's would make this method disagree with every other R modelling function.

conf.type

Transform for type = "survival" confidence limits when se.fit = TRUE: "log-log" (default) or "logit", which reproduces SAS PROC HAZPRED's survival limits. A real argument rather than part of ... because a misspelling would otherwise be swallowed silently and return log-log limits that disagree with the SAS job being reproduced. As in predict.hazard(), the value is checked only where it is used, so an ignored one does not make a point or hazard prediction fail.

...

Must be empty. Anything landing here is an error rather than a silently ignored argument.

Value

What predict.hazard() returns for a fit with no covariates: a numeric vector of predictions when se.fit = FALSE, and when se.fit = TRUE a data frame with columns fit, se.fit, lower and upper (the confidence limits at level). The decomposed long-format return of predict.hazard() is not reachable here, because decompose = TRUE is refused.

Details

newdata is required. An OUTHAZ= dataset holds a converged model and no data, so there is no fitted time vector to fall back on; without newdata the prediction would be over nothing.

What is reconstructed, and what is refused

A phase is in the model when its intercept row (E0, C0, L0) carries _STATUS_ = 1, which is the test PROC HAZPRED itself applies. The early phase becomes hzr_phase("cdf") and the late phase hzr_phase("g3"), with the shape estimates read off the DELTA/THALF/NU/M and TAU/GAMMA/ALPHA/ETA rows.

These cases error rather than return a number that looks like a prediction: a dataset carrying covariates (their coefficients cannot be matched to newdata columns from the file alone); a non-zero DELTA (the early-phase time transformation is not implemented); and a G1FLAG that disagrees with the signs of the M and NU estimates.

With se.fit = TRUE there are three more, because the stored covariance is on SAS's estimation scale and has to be mapped onto this package's:

a fit constrained by FIXMNU1, which ties M to 1/NU;
a fit estimating GAMMA, ALPHA or ETA on one of PROC HAZARD's composite late-phase scales – log(GAMMA*ETA - 2) or log(GAMMA*ETA/ALPHA - 2) rather than log() of the parameter. This is the ordinary unconstrained late phase, not an exotic case: with G3FLAG = 1 and no FIXGE2/FIXGAE2, ETA and ALPHA are always on a composite scale and GAMMA is whenever ETA is fixed. So an early + constant + late fit with any free late shape gets point predictions but no standard errors;
a fit under FIXGE2 or FIXGAE2 where a late parameter is derived from an estimated one (ETA = 2/GAMMA, GAMMA = 2/ETA, ALPHA = GAMMA*ETA/2). The derived parameter has no covariance row, so its contribution to the variance would be dropped without trace.

Examples

f <- system.file("extdata", "outhaz-fixture.rds", package = "TemporalHazard")
if (nzchar(f)) {
  fit <- hzr_read_outhaz(f)
  predict(fit, newdata = data.frame(time = c(1, 6, 12)), type = "survival")
}
#> [1] 0.9064419 0.7547405 0.6075586

Predictions from a fit loaded out of a SAS `OUTHAZ=` dataset