# predict.parfm: Predictions of frailty values for Parametric Frailty Models In parfm: Parametric Frailty Models

## Description

The function `predict.parfm()` computes predictions of frailty values for objects of class `parfm`.

## Usage

 ```1 2``` ```## S3 method for class 'parfm' predict(object, ...) ```

## Arguments

 `object` A parametric frailty model, object of class `parfm`. `...` see `predict()`

## Value

An object of class `predict.parfm`.

## Author(s)

Federico Rotolo [aut, cre], Marco Munda [aut], Andrea Callegaro [ctb]

## References

Glidden D, Vittinghoff E (2004). Modelling Clustered Survival Data From Multicentre Clinical Trials. Statistics in medicine, 23(3), 369–388.

Munda M, Rotolo F, Legrand C (2012). parfm: Parametric Frailty Models in R. Journal of Statistical Software, 51(11), 1-20. DOI 10.18637/jss.v051.i11

`parfm`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35``` ```data(kidney) kidney\$sex <- kidney\$sex - 1 model <- parfm(Surv(time,status) ~ sex + age, cluster = "id", data = kidney, dist = "exponential", frailty = "gamma") u <- predict(model) u # Predictions from semi-parametric Gamma frailty model # via coxph() function model.coxph <- coxph(Surv(time,status) ~ sex + age + frailty(id, frailty = "gamma", eps = 1e-11), outer.max = 15, data = kidney) u.coxph <- exp(model.coxph\$frail) # Plot of predictions from both models par(mfrow = c(1,2)) ylim <- c(0, max(c(u, u.coxph))) plot(u, sort = "i", main = paste("Parametric", "Gamma frailty model", "with Exponential baseline", sep = "\n"), ylim = ylim) names(u.coxph) <- kidney[seq(2,76, 2), "id"] class(u.coxph) <- "predict.parfm" attr(u.coxph, "clustname") <- "id" plot(u.coxph, sort = "i", main = paste("Semi-parametric", "Gamma frailty model", sep = "\n"), ylim = ylim) ```

### Example output

```Loading required package: survival
Gamma frailty model with Exponential baseline
id frailty
1  1.325
2  1.207
3  1.109
4  0.632
5  1.199
6  1.065
7  1.386
8  0.723
9  1.002
10 0.604
11 0.929
12 0.999
13 1.258
14 0.684
15 0.634
16 1.072
17 0.874
18 0.86
19 0.713
20 1.031
21 0.205
22 0.704
23 1.353
24 1.103
25 1.077
26 0.759
27 1.053
28 1.403
29 1.266
30 1.188
31 1.344
32 1.176
33 1.134
34 0.919
35 1.288
36 0.871
37 1.097
38 0.756
Warning message:
In coxpenal.fit(X, Y, strats, offset, init = init, control, weights = weights,  :
Inner loop failed to coverge for iterations 3
```

parfm documentation built on May 2, 2019, 5 p.m.