marp_confint {marp}

A function to apply model-averaged renewal process

Description

A function to apply model-averaged renewal process

Usage

marp_confint(data, m, t, B, BB, alpha, y, which.model)

Arguments

data

input inter-event times

m

the number of iterations in nlm

t

user-specified time intervals (used to compute hazard rate)

B

number of bootstrap samples

BB

number of double-bootstrap samples

alpha

significance level

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 point and interval estimation obtained from different renewal models (including model-averaged confidence intervals).

par1

Estimated scale parameters (if applicable) of all six renewal models

par2

Estimated shape parameters (if applicable) of all six renewal models

logL

Negative log-likelihood

AIC

Akaike information criterion (AIC)

BIC

Bayesian information criterion (BIC)

mu_hat

Estimated mean

pr_hat

Estimated (logit) probabilities

haz_hat

Estimated (log) hazard rates

weights_AIC

Model weights calculated based on AIC

weights_BIC

Model weights calculated based on BIC

model_best

Model selected based on the lowest AIC

mu_best

Estimated mean obtained from the model with the lowest AIC

pr_best

Estimated probability obtained from the model with the lowest AIC

haz_best

Estimated hazard rates obtained from the model with the lowest AIC

mu_gen

Estimated mean obtained from the (true or hypothetical) generating model

pr_gen

Estimated probability obtained from the (true or hypothetical) generating model

haz_gen

Estimated hazard rates obtained from the (true or hypothetical) generating model

mu_aic

Estimated mean obtained from model-averaging (using AIC weights)

pr_aic

Estimated probability obtained from model-averaging (using AIC weights)

haz_aic

Estimated hazard rates obtained from model-averaging (using AIC weights)

mu_bstrp

Estimated mean obtained from model-averaging (using bootstrapped weights)

pr_bstrp

Estimated probability obtained from model-averaging (using bootstrapped weights)

haz_bstrp

Estimated hazard rates obtained from model-averaging (using bootstrapped weights)

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

mu_lower_gen

Lower limit of the studentized bootstrap confidence interval of the estimated mean based on the generating model

mu_upper_gen

Upper limit of the studentized bootstrap confidence interval of the estimated mean based on the generating model

mu_lower_best

Lower limit of the studentized bootstrap confidence interval of the estimated mean based on the best model

mu_upper_best

Upper limit of the studentized bootstrap confidence interval of the estimated mean based on the best model

pr_lower_gen

Lower limit of the studentized bootstrap confidence interval of the estimated probabilities based on the generating model

pr_upper_gen

Upper limit of the studentized bootstrap confidence interval of the estimated probabilities based on the generating model

pr_lower_best

Lower limit of the studentized bootstrap confidence interval of the estimated probabilities based on the best model

pr_upper_best

Upper limit of the studentized bootstrap confidence interval of the estimated probabilities based on the best model

haz_lower_gen

Lower limit of the studentized bootstrap confidence interval of the estimated hazard rates based on the generating model

haz_upper_gen

Upper limit of the studentized bootstrap confidence interval of the estimated hazard rates based on the generating model

haz_lower_best

Lower limit of the studentized bootstrap confidence interval of the estimated hazard rates based on the best model

haz_upper_best

Upper limit of the studentized bootstrap confidence interval of the estimated hazard rates based on the best model

mu_lower_ma

Lower limit of model-averaged studentized bootstrap confidence interval of the estimated mean

mu_upper_ma

Upper limit of model-averaged studentized bootstrap confidence interval of the estimated mean

pr_lower_ma

Lower limit of model-averaged studentized bootstrap confidence interval of the estimated probabilities

pr_upper_ma

Upper limit of model-averaged studentized bootstrap confidence interval of the estimated probabilities

haz_lower_ma

Lower limit of model-averaged studentized bootstrap confidence interval of the estimated hazard rates

haz_upper_ma

Upper limit of model-averaged studentized bootstrap confidence interval of the estimated hazard rates

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
alpha <- 0.05 # confidence level
y <- 304 # cut-off year for estimating probability
B <- 100 # number of bootstraps
BB <- 100 # number of double bootstraps
which.model <- 2 # specify the generating model

# construct confidence invtervals
res <- marp::marp_confint(data,m,t,B,BB,alpha,y,which.model)