boot.modreg {dirttee} | R Documentation |

Performs bootstrap on the modreg object.

```
boot.modreg(
reg,
nboot,
level = 0.95,
newdata = NULL,
bw = c("variable", "fixed"),
quiet = FALSE,
terms = NULL,
seed = NULL
)
```

`reg` |
an object of class modreg (output of the modreg function) |

`nboot` |
number of bootstrap replications |

`level` |
confidence level |

`newdata` |
Should be a data frame containing all the variables needed for predictions. If supplied, confidence intervals are calculated for the corresponding predictions. |

`bw` |
Either " |

`quiet` |
if TRUE, printing of the status is suppressed |

`terms` |
character scalar. If supplied, uses this term for confidence intervals of the prediction |

`seed` |
the seed to use |

A nonparametric residual bootstrap is performed to calculate standard errors of parameters and confidence intervals. More details can be found in Seipp et al. (2022).
`newdata`

can be supplied to get confidence intervals for specific predictions. `terms`

can be specified to calculate confidence interval for the contribution of one covariate (useful for P-splines).
`variable`

bandwidth is the default, which has higher coverage than `fix`

, but is computationally much more demanding. A `seed`

can be supplied to guarantee a reproducible result.

a list with the following elements

`confpredict` |
data frame, the confidence intervals for the predictions. |

`confparams` |
data frame, the confidence intervals and standard errors for the parametric regression coefficients. |

`level` |
confidence level |

`na` |
scalar, stating the number of NA bootstrap repetitions. |

`seed` |
scalar, the used seed. |

Seipp, A., Uslar, V., Weyhe, D., Timmer, A., & Otto-Sobotka, F. (2022). Flexible Semiparametric Mode Regression for Time-to-Event Data. Manuscript submitted for publication.

```
data(colcancer)
colcancer80 <- colcancer[1:80, ]
# linear mode regression
regL <- modreg(Surv(logfollowup, death) ~ sex + age, data = colcancer80)
# bootstrap with a fixed bandwidth and 5 iterations, chosen to speed up the function.
# Should in practice be much more than 5 iterations.
btL <- boot.modreg(regL, 5, bw = "fixed", level = 0.9, seed = 100)
# coefficients, SE and confidence intervals
cbind(coef(regL), btL$confparams)
## confidence inverval for smooth effect / predictions
reg <- modreg(Surv(logfollowup, death) ~ sex + s(age, bs = "ps"), data = colcancer80,
control = modreg.control(tol_opt = 10^-2, tol_opt2 = 10^-2, tol = 10^-3))
ndat <- data.frame(sex = rep(colcancer80$sex[1], 200), age = seq(50, 90, length = 200))
# iterations should in practice be much more than 2!
bt <- boot.modreg(reg, 2, bw = "fixed", newdata = ndat, terms = "s(age)", seed = 100)
pr <- predict(reg, newdata = ndat, type = "terms", terms = "s(age)")[, 1]
plot(ndat$age, pr, ylim = c(-0.75, 1.5), type = "l", xlab = "age", ylab = "s(age)")
lines(ndat$age, bt$confpredict$lower, lty = 2)
lines(ndat$age, bt$confpredict$upper, lty = 2)
```

[Package *dirttee* version 1.0.1 Index]