boot.modreg {dirttee} R Documentation

## Estimate confidence intervals and standard errors for the mode regression fit

### Description

Performs bootstrap on the modreg object.

### Usage

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


### Arguments

 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 "variable" or "fix", determining if the bandwidth of the original fit should be used for the bootstrap fits (fix) or if the bandwith should be recalculated (variable). 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

### Details

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.

### Value

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.

### References

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.

### Examples


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]