summary.cv4abc: Calculates the cross-validation prediction error
In bcm-uga/abc: Tools for Approximate Bayesian Computation (ABC)

Description Usage Arguments Details Value See Also Examples

This function calculates the prediction error from an object of class "cv4abc" for each parameter and tolerance level.

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## S3 method for class 'cv4abc'
summary(object, print = TRUE, digits = max(3,
getOption("digits")-3), ...)

`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`.

The prediction error is calculated as \frac{∑((θ^{*}-θ)^2)}{nval\times Var(θ)}, where θ is the true parameter value, θ^{*} is the predicted parameter value, and nval is the number of points where true and predicted values are compared.

The returned value is an object of class "table", where the columns correspond to the parameters and the rows to the different tolerance levels.

cv4abc, plot.cv4abc