student_confint {marp} | R Documentation |
A function to calculate Studentized bootstrap confidence interval
student_confint(
n,
B,
t,
m,
BB,
par_hat,
mu_hat,
pr_hat,
haz_hat,
weights,
alpha,
y,
best.model,
which.model = 1
)
n |
number of 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 |
BB |
number of double-bootstrap samples |
par_hat |
estimated parameters |
mu_hat |
estimated mean inter-event times |
pr_hat |
estimated time to event probability |
haz_hat |
estimated hazard rates |
weights |
model weights |
alpha |
significance level |
y |
user-specified time point (used to compute time-to-event probability) |
best.model |
best model based on information criterion (i.e. AIC) |
which.model |
user-specified generating (or true underlying if known) model |
returns list of Studentized bootstrap intervals (including the model-averaged approach).
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
n <- 30 # sample size
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
par_hat <- c(
3.41361e-03, 2.76268e+00, 2.60370e+00, 3.30802e+02, 5.48822e+00, 2.92945e+02, NA,
9.43071e-03, 2.47598e+02, 1.80102e+00, 6.50845e-01, 7.18247e-01)
mu_hat <- c(292.94512, 292.94513, 319.72017, 294.16945, 298.87286, 292.94512)
pr_hat <- c(0.60039, 0.42155, 0.53434, 0.30780, 0.56416, 0.61795)
haz_hat <- matrix(c(
-5.67999, -5.67999, -5.67999, -5.67999, -5.67999, -5.67999,
-5.67999, -5.67999, -5.67999, -5.67999, -5.67999, -6.09420,
-5.99679, -5.91174, -5.83682, -5.77031, -5.71085, -5.65738,
-5.60904, -5.56512, -5.52504, -5.48833, -6.09902, -5.97017,
-5.85769, -5.75939, -5.67350, -5.59856, -5.53336, -5.47683,
-5.42805, -5.38621, -5.35060, -6.17146, -6.09512, -6.02542,
-5.96131, -5.90194, -5.84668, -5.79498, -5.74642, -5.70064,
-5.65733, -5.61624, -5.92355, -5.80239, -5.70475, -5.62524,
-5.55994, -5.50595, -5.46106, -5.42359, -5.39222, -5.36591,
-5.34383, -5.79111, -5.67660, -5.58924, -5.52166, -5.46879,
-5.42707, -5.39394, -5.36751, -5.34637, -5.32946, -5.31596
),length(t),6)
weights <- c(0.00000, 0.21000, 0.02000, 0.55000, 0.00000, 0.22000) # model weights
alpha <- 0.05 # confidence level
y <- 304 # cut-off year for estimating probablity
best.model <- 2
which.model <- 2 # specify the generating model#'
# construct Studentized bootstrap confidence interval
marp::student_confint(
n,B,t,m,BB,par_hat,mu_hat,pr_hat,haz_hat,weights,alpha,y,best.model,which.model
)