# predict.spdur: Predict methods for spdur Objects In spduration: Split-Population Duration (Cure) Regression

## Description

`predict` and related methods for class “`spdur`”.

## Usage

 ```1 2 3 4 5 6 7 8 9``` ```## S3 method for class 'spdur' predict(object, newdata = NULL, type = "response", truncate = TRUE, na.action = na.exclude, ...) ## S3 method for class 'spdur' fitted(object, ...) ## S3 method for class 'spdur' residuals(object, type = c("response"), ...) ```

## Arguments

 `object` Object of class “`spdur`”. `newdata` Optional data for which to calculate fitted values, defaults to training data. `type` Quantity of interest to calculate. Default conditional hazard, i.e. conditioned on observed survival up to time `t`. See below for list of values. For `residuals`, the type of residual to calculate `truncate` For conditional hazard, truncate values greater than 1. `na.action` Function determining what should be done with missing values in newdata. The default is to predict NA (`na.exclude`). `...` not used, for compatibility with generic function.

## Details

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 `type` option include:

• “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.

## Value

Returns a data frame with 1 column corresponding to `type`, in the same order as the data frame used to estimate `object`.

## Note

See `forecast.spdur` for producing forecasts when future covariate values are unknown.

## Examples

 ```1 2 3 4 5 6 7``` ```# get model estimates data(model.coups) ch <- predict(model.coups) head(fitted(model.coups)) head(residuals(model.coups)) ```

spduration documentation built on May 1, 2019, 6:32 p.m.