summary.cv4abc: Calculates the cross-validation prediction error
Description
This function calculates the prediction error from an object of class
"cv4abc" for each parameter and tolerance level.
Usage
# S3 method for cv4abc
summary(object, print = TRUE, digits = max(3,
getOption("digits")-3), ...)Value
The returned value is an object of class "table", where the
columns correspond to the parameters and the rows to the different
tolerance levels.
Arguments
- object
an object of class "abc".
- print
logical, if TRUE prints messages. Mainly for internal use.
- digits
the digits to be rounded to. Can be a vector of the same length as the
number of parameters, when each parameter is rounded to its
corresponding digits.
- ...
other arguments passed to density.
Details
The prediction error is calculated as
\(\frac{\sum((\theta^{*}-\theta)^2)}{nval\times Var(\theta)}\), where
\(\theta\) is the true parameter value, \(\theta^{*}\) is the
predicted parameter value, and \(nval\) is the number of points where true and predicted values are compared.