# Methods for Function summary

`summary-methods.Rd`

Methods for function `summary`

to calculate summary statistics from a "MultiChainLadder" object.

## Usage

```
# S4 method for class 'MultiChainLadder'
summary(object, portfolio=NULL,...)
```

## 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.

## Value

The `summary`

function returns an object of class "MultiChainLadderSummary" that has the following slots:

- Triangles
input triangles

- FullTriangles
predicted triangles

- S.E.Full
a list of prediction errors for each cell

- S.E.Est.Full
a list of estimation errors for each cell

- S.E.Proc.Full
a list of process errors for each cell

- Ultimate
predicted ultimate losses for each triangle and portfolio

- Latest
latest observed losses for each triangle and portfolio

- IBNR
predicted IBNR for each triangle and portfolio

- S.E.Ult
a matrix of prediction errors of ultimate losses for each triangle and portfolio

- S.E.Est.Ult
a matrix of estimation errors of ultimate losses for each triangle and portfolio

- S.E.Proc.Ult
a matrix of process errors of ultimate losses for each triangle and portfolio

- report.summary
summary statistics for each triangle and portfolio

- coefficients
estimated coefficients from

`systemfit`

. They are put into the matrix format for GMCL- coefCov
estimated variance-covariance matrix returned by

`systemfit`

- residCov
estimated residual covariance matrix returned by

`systemfit`

- rstandard
standardized residuals

- fitted.values
fitted.values

- residCor
residual correlation

- model.summary
summary statistics for the cofficients including p-values

- portfolio
how portfolio is calculated

## Author

Wayne Zhang actuary_zhang@hotmail.com

## See also

See Also `MultiChainLadder`

## Examples

```
data(GenIns)
fit.bbmw=MultiChainLadder(list(GenIns),fit.method="OLS", mse.method="Independence")
summary(fit.bbmw)
#> $`Summary Statistics for Input Triangle`
#> Latest Dev.To.Date Ultimate IBNR S.E CV
#> 1 3,901,463 1.0000 3,901,463 0 0 0.000
#> 2 5,339,085 0.9826 5,433,719 94,634 75,535 0.798
#> 3 4,909,315 0.9127 5,378,826 469,511 121,700 0.259
#> 4 4,588,268 0.8661 5,297,906 709,638 133,551 0.188
#> 5 3,873,311 0.7973 4,858,200 984,889 261,412 0.265
#> 6 3,691,712 0.7223 5,111,171 1,419,459 411,028 0.290
#> 7 3,483,130 0.6153 5,660,771 2,177,641 558,356 0.256
#> 8 2,864,498 0.4222 6,784,799 3,920,301 875,430 0.223
#> 9 1,363,294 0.2416 5,642,266 4,278,972 971,385 0.227
#> 10 344,014 0.0692 4,969,825 4,625,811 1,363,385 0.295
#> Total 34,358,090 0.6478 53,038,946 18,680,856 2,447,618 0.131
#>
```