predict and related methods for class ``
# S3 method for spdur predict(object, newdata = NULL, type = "response", truncate = TRUE, na.action = na.exclude, ...) # S3 method for spdur fitted(object, ...) # S3 method for spdur residuals(object, type = c("response"), ...)
Object of class ``
Optional data for which to calculate fitted values, defaults to training data.
Quantity of interest to calculate. Default conditional hazard,
i.e. conditioned on observed survival up to time
For conditional hazard, truncate values greater than 1.
Function determining what should be done with missing values
in newdata. The default is to predict NA (
not used, for compatibility with generic function.
Returns a data frame with 1 column corresponding to
type, in the same
order as the data frame used to estimate
Calculates various types of probabilities, where ``conditional'' is used in
reference to conditioning on the observed survival time of a spell up to
time \(t\), in addition to conditioning on any variables included in the
model (which is always done). Valid values for the
``conditional risk'': \(Pr(Cure=0|Z\gamma, T>t)\)
``conditional cure'': \(Pr(Cure=1|Z\gamma, T>t)\)
``hazard'': \(Pr(T=t|T>t, C=0, X\beta) * Pr(Cure=0|Z\gamma)\)
``failure'': \(Pr(T=t|T>t-1, C=0, X\beta) * Pr(Cure=0|Z\gamma)\)
``unconditional risk'': \(Pr(Cure=0|Z\gamma)\)
``unconditional cure'': \(Pr(Cure=1|Z\gamma)\)
``conditional hazard'' or ``response'': \(Pr(T=t|T>t, C=0, X\beta) * Pr(Cure=0|Z\gamma, T>t)\)
``conditional failure'': \(Pr(T=t|T>t-1, C=0, X\beta) * Pr(Cure=0|Z\gamma, T>t)\)
The vector \(Z\gamma\) indicates the cure/at risk equation covariate vector, while \(X\beta\) indicates the duration equation covariate vector.
forecast.spdur for producing forecasts when future
covariate values are unknown.
#>  0.016552005 0.005726432 0.003026371 0.002446806 0.016492801 0.036707531#> 5007 5006 5570 5039 4751 4877 #> -0.016552005 -0.005726432 -0.003026371 -0.002446806 -0.016492801 -0.036707531