bootstrap_relative_efficiency {argo} | R Documentation |
bootstrap relative efficiency confidence interval
Description
This function is used to reproduce the ARGO bootstrap confidence interval
Usage
bootstrap_relative_efficiency(
pred_data,
model_good,
model_bench,
l = 50,
N = 10000,
truth = "CDC.data",
sim = "geom",
conf = 0.95,
type = c("mse", "mape", "mae", "mspe", "rmse", "rmspe")
)
Arguments
pred_data |
A matrix that contains the truth vector and the predictions. It can be data.frame or xts object |
model_good |
The model to evaluate, must be in the column names of pred_data |
model_bench |
The model to compare to, must be in the column names of pred_data |
l |
stationary bootstrap mean block length |
N |
number of bootstrap samples |
truth |
the column name of the truth |
sim |
simulation method, pass to boot::tsboot |
conf |
confidence level |
type |
Must be one of "mse" (mean square error), "mape" (mean absolute percentage error), or "mae" (mean absolute error) |
Value
A vector of point estimate and corresponding bootstrap confidence interval
Examples
GFT_xts = xts::xts(exp(matrix(rnorm(1000), ncol=5)), order.by = Sys.Date() - (200:1))
names(GFT_xts) <- paste0("col", 1:ncol(GFT_xts))
names(GFT_xts)[1] <- "CDC.data"
bootstrap_relative_efficiency(
pred_data = GFT_xts,
model_good = "col2",
model_bench = "col3",
truth="CDC.data",
N = 100
)
[Package argo version 3.0.2 Index]