R: Calculate sum of squared prediction errors

sum.sqerror {dse1}

R Documentation

Calculate sum of squared prediction errors

Description

Calculate a weighted sum squared prediction errors for a parameterization.

Usage

    sum.sqerror(coefficients, model=NULL, data=NULL, error.weights=c(1,1,1,1))

Arguments

`coefficients`	A vector of coefficients (parameters).
`model`	an object of class TSmodel which gives the structure of the model for which coefficients are used. `coef(model)` should be the same length as coefficients.
`data`	an object of class TSdata which gives the data with which the model is to be evaluated.
`error.weights`	a vector of weights to be applied to the squared prediction errors.

Details

This function is primarily for use in parameter optimization, which requires that an objective function be specified by a vector of parameters.

Value

The value of the sum squared errors for a prediction horizon given by the length of error.weights. Each period ahead is weighted by the corresponding weight in error.weights.

Examples

if(is.R()) data("eg1.DSE.data.diff", package="dse1")
model <- est.VARX.ls(eg1.DSE.data.diff)
sum.sqerror(1e-10 + coef(model), model=TSmodel(model), 
        data=TSdata(model), error.weights=c(1,1,10))

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