lognorm_bstrp {marp} | R Documentation |
A function to generate (double) bootstrap samples and fit Log-Normal renewal model
lognorm_bstrp(n, t, B, BB, par_hat, mu_hat, pr_hat, haz_hat, y)
n |
number of inter-event times |
t |
user-specified time intervals (used to compute hazard rate) |
B |
number of bootstrap samples |
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 |
y |
user-specified time point (used to compute time-to-event probability) |
returns list of estimates after fitting Log-Normal renewal model on (double) bootstrap samples
Estimated mean from bootstrapped samples
Estimated probability from bootstrapped samples
Estimated hazard rates from bootstrapped samples
Variance of estimated mean
Variance of estimated probability
Variance of estimated hazard rates
Variance of estimated mean of bootstrapped samples (via double-bootstrapping)
Variance of estimated probability of bootstrapped samples (via double-bootstrapping)
Variance of estimated hazard rates of bootstrapped samples (via double-bootstrapping)
Pivot quantity of the estimated mean
Pivot quantity of the estimated probability
Pivot quantity of the estimated hazard rates
# set some parameters
n <- 30 # sample size
t <- seq(100, 200, by = 10) # time intervals
B <- 100 # number of bootstraps
BB <- 100 # number of double-bootstraps
# m <- 10 # number of iterations for MLE optimization
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)
y <- 304 # cut-off year for estimating probablity
# generate bootstrapped samples then fit renewal model
res <- marp::lognorm_bstrp(n, t, B, BB, par_hat, mu_hat, pr_hat, haz_hat, y)