summary-methods: Methods for Function summary

Description

Methods for function summary to calculate summary statistics from a "MultiChainLadder" object.

Usage

## S3 method for class 'MultiChainLadder':
summary(object, portfolio=NULL,...)

Arguments

object

object of class "MultiChainLadder"

portfolio

character strings specifying which triangles to be summed up as portfolio.

...

optional arguments to summary methods

Value

The summary function returns an object of class "MultiChainLadderSummary" that has the following slots:
Trianglesinput triangles
FullTrianglespredicted triangles
S.E.Fulla list of prediction errors for each cell
S.E.Est.Fulla list of estimation errors for each cell
S.E.Proc.Fulla list of process errors for each cell
Ultimatepredicted ultimate losses for each triangle and portfolio
Latestlatest observed losses for each triangle and portfolio
IBNRpredicted IBNR for each triangle and portfolio
S.E.Ulta matrix of prediction errors of ultimate losses for each triangle and portfolio
S.E.Est.Ulta matrix of estimation errors of ultimate losses for each triangle and portfolio
S.E.Proc.Ulta matrix of process errors of ultimate losses for each triangle and portfolio
report.summarysummary statistics for each triangle and portfolio
coefficientsestimated coefficients from systemfit. They are put into the matrix format for GMCL
coefCovestimated variance-covariance matrix returned by systemfit
residCovestimated residual covariance matrix returned by systemfit
rstandardstandardized residuals
fitted.valuesfitted.values
residCorresidual correlation
model.summarysummary statistics for the cofficients including p-values
portfoliohow portfolio is calculated

Details

summary calculations the summary statistics for each triangle and the whole portfolio from portfolio. portfolio defaults to the sum of all input triangles. It can also be specified as "i+j" format, which means the sum of the i-th and j-th triangle as portfolio. For example, "1+3" means the sum of the first and third triangle as portfolio.

Examples

Run this code

data(GenIns)
fit.bbmw=MultiChainLadder(list(GenIns),fit.method="OLS", mse.method="Independence")
summary(fit.bbmw)