summary.MunichChainLadder: Summary and print function for Munich-chain-ladder

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/MunichChainLadderFunctions.R

Description

summary and print methods for a MunichChainLadder object

Usage

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## S3 method for class 'MunichChainLadder'
summary(object, ...)

## S3 method for class 'MunichChainLadder'
print(x, ...)

Arguments

x, object

object of class "MunichChainLadder"

...

optional arguments to print or summary methods

Details

print.MunichChainLadder calls summary.MunichChainLadder and prints a formatted version of the summary.

Value

summary.MunichChainLadder gives a list of two elements back

ByOrigin

data frame with Latest Paid (latest actual paid claims costs), Latest Incurred (latest actual incurred claims position), Latest P/I Ratio (ratio of latest paid/incurred claims), Ult. Paid (estimate ultimate claims cost based on the paid triangle), Ult. Incurred (estimate ultimate claims cost based on the incurred triangle),Ult. P/I Ratio (ratio of ultimate paid forecast / ultimate incurred forecast)

Totals

data frame of totals over all origin periods. The items follow the same naming convention as in ByOrigin above

Author(s)

Markus Gesmann

See Also

See also MunichChainLadder, plot.MunichChainLadder

Examples

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Example output

Welcome to ChainLadder version 0.2.9

Type vignette('ChainLadder', package='ChainLadder') to access
the overall package documentation.

See demo(package='ChainLadder') for a list of demos.

More information is available on the ChainLadder project web-site:
https://github.com/mages/ChainLadder

To suppress this message use:
suppressPackageStartupMessages(library(ChainLadder))

Warning messages:
1: In Mack.S.E(CL[["Models"]], FullTriangle, est.sigma = est.sigma,  :
  'loglinear' model to estimate sigma_n doesn't appear appropriate. 
p-value > 5.
 est.sigma will be overwritten to 'Mack'.
 Mack's estimation method will be used instead.
2: In Mack.S.E(CL[["Models"]], FullTriangle, est.sigma = est.sigma,  :
  'loglinear' model to estimate sigma_n doesn't appear appropriate. 
p-value > 5.
 est.sigma will be overwritten to 'Mack'.
 Mack's estimation method will be used instead.
MunichChainLadder(Paid = MCLpaid, Incurred = MCLincurred)

  Latest Paid Latest Incurred Latest P/I Ratio Ult. Paid Ult. Incurred
1       2,131           2,174            0.980     2,131         2,174
2       2,348           2,454            0.957     2,385         2,443
3       4,494           4,644            0.968     4,554         4,634
4       5,850           6,142            0.952     6,070         6,182
5       4,648           4,852            0.958     4,879         4,958
6       4,010           4,406            0.910     4,599         4,672
7       2,044           5,022            0.407     7,505         7,655
  Ult. P/I Ratio
1          0.980
2          0.976
3          0.983
4          0.982
5          0.984
6          0.984
7          0.980

Totals
            Paid Incurred P/I Ratio
Latest:   25,525   29,694      0.86
Ultimate: 32,121   32,720      0.98
$ByOrigin
  Latest Paid Latest Incurred Latest P/I Ratio Ult. Paid Ult. Incurred
1        2131            2174        0.9802208  2131.000      2174.000
2        2348            2454        0.9568052  2384.842      2443.222
3        4494            4644        0.9677003  4553.624      4634.358
4        5850            6142        0.9524585  6069.509      6182.347
5        4648            4852        0.9579555  4878.950      4957.805
6        4010            4406        0.9101226  4598.996      4672.402
7        2044            5022        0.4070092  7504.576      7655.378
  Ult. P/I Ratio
1      0.9802208
2      0.9761052
3      0.9825792
4      0.9817483
5      0.9840948
6      0.9842894
7      0.9803012

$Totals
             Paid Incurred P/I Ratio
Latest:   25525.0 29694.00 0.8596013
Ultimate: 32121.5 32719.51 0.9817230

  Latest Paid Latest Incurred Latest P/I Ratio Ult. Paid Ult. Incurred
1        2131            2174        0.9802208  2131.000      2174.000
2        2348            2454        0.9568052  2384.842      2443.222
3        4494            4644        0.9677003  4553.624      4634.358
4        5850            6142        0.9524585  6069.509      6182.347
5        4648            4852        0.9579555  4878.950      4957.805
6        4010            4406        0.9101226  4598.996      4672.402
7        2044            5022        0.4070092  7504.576      7655.378
  Ult. P/I Ratio
1      0.9802208
2      0.9761052
3      0.9825792
4      0.9817483
5      0.9840948
6      0.9842894
7      0.9803012

ChainLadder documentation built on Jan. 9, 2022, 5:06 p.m.