cocoBoot {coconots} | R Documentation |
Bootstrap Based Model Assessment Procedure
Description
Model checking procedure emphasising reproducibility in fitted models, as proposed by Tsay (1992).
Usage
cocoBoot(
coco,
numb.lags = 21,
rep.Bootstrap = 1000,
conf.alpha = 0.05,
julia = FALSE,
julia_seed = NULL
)
Arguments
coco |
An object of class coco |
numb.lags |
Number of lags for which to compute sample autocorrelations (default: 21). |
rep.Bootstrap |
Number of bootstrap replicates to use (default: 1000) |
conf.alpha |
|
julia |
if TRUE, the bootstrap is run with julia (default: FALSE) |
julia_seed |
Seed for the julia implementation. Only used if julia equals TRUE |
Details
Bootstrap-generated acceptance envelopes for the autocorrelation function provides an overall evaluation by comparing it with the sample autocorrelation function in a joint plot.
Value
an object of class cocoBoot. It contains the bootstrapped confidence intervals of the autocorrelations and information on the model specifications.
References
Tsay, R. S. (1992) Model checking via parametric bootstraps in time series analysis. Applied Statistics 41, 1–15.
Examples
lambda <- 1
alpha <- 0.4
set.seed(12345)
data <- cocoSim(order = 1, type = "Poisson", par = c(lambda, alpha), length = 100)
fit <- cocoReg(order = 1, type = "Poisson", data = data)
# bootstrap model assessment - R implementation
boot_r <- cocoBoot(fit, rep.Bootstrap=400)
plot(boot_r)