| percent_confint {marp} | R Documentation |
A function to calculate percentile bootstrap confidence interval
Description
A function to calculate percentile bootstrap confidence interval
Usage
percent_confint(data, B, t, m, y, which.model = 1)
Arguments
data |
input inter-event times |
B |
number of bootstrap samples |
t |
user-specified time intervals (used to compute hazard rate) |
m |
the number of iterations in nlm |
y |
user-specified time point (used to compute time-to-event probability) |
which.model |
user-specified generating (or true underlying if known) model |
Value
returns list of percentile bootstrap intervals (including the model-averaged approach).
- weights_bstp
Model weights calculated by bootstrapping, that is, the frequency of each model being selected as the best model is divided by the total number of bootstraps
- mu_gen
Median of the percentile bootstrap confidence interval of the estimated mean based on the generating model
- mu_gen_lower
Lower limit of the percentile bootstrap confidence interval of the estimated mean based on the generating model
- mu_gen_upper
Upper limit of the percentile bootstrap confidence interval of the estimated mean based on the generating model
- mu_best
Median of the percentile bootstrap confidence interval of the estimated mean based on the best model
- mu_best_lower
Lower limit of the percentile bootstrap confidence interval of the estimated mean based on the best model
- mu_best_upper
Upper limit of the percentile bootstrap confidence interval of the estimated mean based on the best model
- pr_gen
Median of the percentile bootstrap confidence interval of the estimated probabilities based on the generating model
- pr_gen_lower
Lower limit of the percentile bootstrap confidence interval of the estimated probabilities based on the generating model
- pr_gen_upper
Upper limit of the percentile bootstrap confidence interval of the estimated probabilities based on the generating model
- pr_best
Median of the percentile bootstrap confidence interval of the estimated probabilities based on the best model
- pr_best_lower
Lower limit of the percentile bootstrap confidence interval of the estimated probabilities based on the best model
- pr_best_upper
Upper limit of the percentile bootstrap confidence interval of the estimated probabilities based on the best model
- haz_gen
Median of the percentile bootstrap confidence interval of the estimated hazard rates based on the generating model
- haz_gen_lower
Lower limit of the percentile bootstrap confidence interval of the estimated hazard rates based on the generating model
- haz_gen_upper
Upper limit of the percentile bootstrap confidence interval of the estimated hazard rates based on the generating model
- haz_best
Median of the percentile bootstrap confidence interval of the estimated hazard rates based on the best model
- haz_best_lower
Lower limit of the percentile bootstrap confidence interval of the estimated hazard rates based on the best model
- haz_best_upper
Upper limit of the percentile bootstrap confidence interval of the estimated hazard rates based on the best model
Examples
# generate random data
set.seed(42)
data <- rgamma(30, 3, 0.01)
# set some parameters
m <- 10 # number of iterations for MLE optimization
t <- seq(100,200,by=10) # time intervals
y <- 304 # cut-off year for estimating probablity
B <- 100 # number of bootstraps
BB <- 100 # number of double bootstraps
which.model <- 2 # specify the generating model
# construct percentile bootstrap confidence invtervals
marp::percent_confint(data, B, t, m, y, which.model)