marp_confint {marp} | R Documentation |
A function to apply model-averaged renewal process
marp_confint(data, m, t, B, BB, alpha, y, which.model)
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 |
returns list of point and interval estimation obtained from different renewal models (including model-averaged confidence intervals).
Estimated scale parameters (if applicable) of all six renewal models
Estimated shape parameters (if applicable) of all six renewal models
Negative log-likelihood
Akaike information criterion (AIC)
Bayesian information criterion (BIC)
Estimated mean
Estimated (logit) probabilities
Estimated (log) hazard rates
Model weights calculated based on AIC
Model weights calculated based on BIC
Model selected based on the lowest AIC
Estimated mean obtained from the model with the lowest AIC
Estimated probability obtained from the model with the lowest AIC
Estimated hazard rates obtained from the model with the lowest AIC
Estimated mean obtained from the (true or hypothetical) generating model
Estimated probability obtained from the (true or hypothetical) generating model
Estimated hazard rates obtained from the (true or hypothetical) generating model
Estimated mean obtained from model-averaging (using AIC weights)
Estimated probability obtained from model-averaging (using AIC weights)
Estimated hazard rates obtained from model-averaging (using AIC weights)
Estimated mean obtained from model-averaging (using bootstrapped weights)
Estimated probability obtained from model-averaging (using bootstrapped weights)
Estimated hazard rates obtained from model-averaging (using bootstrapped weights)
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
Median of the percentile bootstrap confidence interval of the estimated mean based on the generating model
Lower limit of the percentile bootstrap confidence interval of the estimated mean based on the generating model
Upper limit of the percentile bootstrap confidence interval of the estimated mean based on the generating model
Median of the percentile bootstrap confidence interval of the estimated mean based on the best model
Lower limit of the percentile bootstrap confidence interval of the estimated mean based on the best model
Upper limit of the percentile bootstrap confidence interval of the estimated mean based on the best model
Median of the percentile bootstrap confidence interval of the estimated probabilities based on the generating model
Lower limit of the percentile bootstrap confidence interval of the estimated probabilities based on the generating model
Upper limit of the percentile bootstrap confidence interval of the estimated probabilities based on the generating model
Median of the percentile bootstrap confidence interval of the estimated probabilities based on the best model
Lower limit of the percentile bootstrap confidence interval of the estimated probabilities based on the best model
Upper limit of the percentile bootstrap confidence interval of the estimated probabilities based on the best model
Median of the percentile bootstrap confidence interval of the estimated hazard rates based on the generating model
Lower limit of the percentile bootstrap confidence interval of the estimated hazard rates based on the generating model
Upper limit of the percentile bootstrap confidence interval of the estimated hazard rates based on the generating model
Median of the percentile bootstrap confidence interval of the estimated hazard rates based on the best model
Lower limit of the percentile bootstrap confidence interval of the estimated hazard rates based on the best model
Upper limit of the percentile bootstrap confidence interval of the estimated hazard rates based on the best model
Lower limit of the studentized bootstrap confidence interval of the estimated mean based on the generating model
Upper limit of the studentized bootstrap confidence interval of the estimated mean based on the generating model
Lower limit of the studentized bootstrap confidence interval of the estimated mean based on the best model
Upper limit of the studentized bootstrap confidence interval of the estimated mean based on the best model
Lower limit of the studentized bootstrap confidence interval of the estimated probabilities based on the generating model
Upper limit of the studentized bootstrap confidence interval of the estimated probabilities based on the generating model
Lower limit of the studentized bootstrap confidence interval of the estimated probabilities based on the best model
Upper limit of the studentized bootstrap confidence interval of the estimated probabilities based on the best model
Lower limit of the studentized bootstrap confidence interval of the estimated hazard rates based on the generating model
Upper limit of the studentized bootstrap confidence interval of the estimated hazard rates based on the generating model
Lower limit of the studentized bootstrap confidence interval of the estimated hazard rates based on the best model
Upper limit of the studentized bootstrap confidence interval of the estimated hazard rates based on the best model
Lower limit of model-averaged studentized bootstrap confidence interval of the estimated mean
Upper limit of model-averaged studentized bootstrap confidence interval of the estimated mean
Lower limit of model-averaged studentized bootstrap confidence interval of the estimated probabilities
Upper limit of model-averaged studentized bootstrap confidence interval of the estimated probabilities
Lower limit of model-averaged studentized bootstrap confidence interval of the estimated hazard rates
Upper limit of model-averaged studentized bootstrap confidence interval of the estimated hazard rates
# 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)