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Functions to print the results of the ClarkLDF and ClarkCapeCod methods.

Usage

<!-- %- \method{print}{clark}(x, \dots) -->
# S3 method for class 'ClarkLDF'
print(x, Amountdigits=0, LDFdigits=3, CVdigits=3,
            row.names = FALSE, ...)

# S3 method for class 'ClarkCapeCod'
print(x, Amountdigits=0, ELRdigits=3, Gdigits=4, CVdigits=3,
            row.names = FALSE, ...)

Arguments

x

object resulting from a run of the ClarkLDF or ClarkCapeCod function.

Amountdigits

number of digits to display to the right of the decimal point for "amount" columns

LDFdigits

number of digits to display to the right of the decimal point for the loss development factor (LDF) column

CVdigits

number of digits to display to the right of the decimal point for the coefficient of variation (CV) column

ELRdigits

number of digits to display to the right of the decimal point for the expected loss ratio (ELR) column

Gdigits

number of digits to display to the right of the decimal point for the "growth function factor" column; default of 4 conforms with the table on pp. 67, 68 of Clark's paper

row.names

logical (or character vector), indicating whether (or what) row names should be printed (same as for print.data.frame)

...

further arguments passed to print

Details

Display the default information in "pretty format" resulting from a run of the "LDF Method" or "Cape Cod Method" – a "Development-type" exhibit for Clark's "LDF Method," a "Bornhuetter-Ferguson-type" exhibit for Clark's "Cape Cod Method."

As usual, typing the name of such an object at the console invokes its print method.

Value

data.frames whose columns are the character representation of their respective summary.ClarkLDF or summary.ClarkCapeCod data.frames.

References

Clark, David R., "LDF Curve-Fitting and Stochastic Reserving: A Maximum Likelihood Approach", Casualty Actuarial Society Forum, Fall, 2003

Author

Daniel Murphy

Examples

X <- GenIns
colnames(X) <- 12*as.numeric(colnames(X))
y <- ClarkCapeCod(X, Premium=10000000+400000*0:9, maxage=240)
summary(y) 
#>       Origin CurrentValue  Premium      ELR FutureGrowthFactor FutureValue
#> 1          1      3901463 1.00e+07 0.597026          0.1303902    778463.4
#> 2          2      5339085 1.04e+07 0.597026          0.1594364    989952.1
#> 3          3      4909315 1.08e+07 0.597026          0.1950165   1257443.3
#> 4          4      4588268 1.12e+07 0.597026          0.2391815   1599332.9
#> 5          5      3873311 1.16e+07 0.597026          0.2947437   2041248.2
#> 6          6      3691712 1.20e+07 0.597026          0.3654987   2618546.9
#> 7          7      3483130 1.24e+07 0.597026          0.4562722   3377839.6
#> 8          8      2864498 1.28e+07 0.597026          0.5720671   4371698.3
#> 9          9      1363294 1.32e+07 0.597026          0.7137828   5625139.6
#> 10        10       344014 1.36e+07 0.597026          0.8617075   6996681.1
#> Total  Total     34358090 1.18e+08       NA                 NA  29656345.3
#>       UltimateValue  StdError        CV
#> 1           4679926  269248.7 0.3458721
#> 2           6329037  311328.7 0.3144887
#> 3           6166758  358975.2 0.2854803
#> 4           6187601  412520.2 0.2579326
#> 5           5914559  471780.8 0.2311237
#> 6           6310259  535626.0 0.2045509
#> 7           6860970  601534.4 0.1780826
#> 8           7236196  665860.7 0.1523117
#> 9           6988434  726347.9 0.1291253
#> 10          7340695  786672.0 0.1124350
#> Total      64014435 3402778.6 0.1147403
print(y)  # (or simply 'y') Same as summary(y) but with "pretty formats"
#>  Origin CurrentValue     Premium   ELR FutureGrowthFactor FutureValue
#>       1    3,901,463  10,000,000 0.597             0.1304     778,463
#>       2    5,339,085  10,400,000 0.597             0.1594     989,952
#>       3    4,909,315  10,800,000 0.597             0.1950   1,257,443
#>       4    4,588,268  11,200,000 0.597             0.2392   1,599,333
#>       5    3,873,311  11,600,000 0.597             0.2947   2,041,248
#>       6    3,691,712  12,000,000 0.597             0.3655   2,618,547
#>       7    3,483,130  12,400,000 0.597             0.4563   3,377,840
#>       8    2,864,498  12,800,000 0.597             0.5721   4,371,698
#>       9    1,363,294  13,200,000 0.597             0.7138   5,625,140
#>      10      344,014  13,600,000 0.597             0.8617   6,996,681
#>   Total   34,358,090 118,000,000                           29,656,345
#>  UltimateValue  StdError  CV%
#>      4,679,926   269,249 34.6
#>      6,329,037   311,329 31.4
#>      6,166,758   358,975 28.5
#>      6,187,601   412,520 25.8
#>      5,914,559   471,781 23.1
#>      6,310,259   535,626 20.5
#>      6,860,970   601,534 17.8
#>      7,236,196   665,861 15.2
#>      6,988,434   726,348 12.9
#>      7,340,695   786,672 11.2
#>     64,014,435 3,402,779 11.5

## Greater growth factors when projecting to infinite maximum age
ClarkCapeCod(X, Premium=10000000+400000*0:9, maxage=Inf)
#>  Origin CurrentValue     Premium   ELR FutureGrowthFactor FutureValue
#>       1    3,901,463  10,000,000 0.597             0.2217   1,323,698
#>       2    5,339,085  10,400,000 0.597             0.2508   1,556,996
#>       3    4,909,315  10,800,000 0.597             0.2863   1,846,297
#>       4    4,588,268  11,200,000 0.597             0.3305   2,209,996
#>       5    3,873,311  11,600,000 0.597             0.3861   2,673,721
#>       6    3,691,712  12,000,000 0.597             0.4568   3,272,829
#>       7    3,483,130  12,400,000 0.597             0.5476   4,053,931
#>       8    2,864,498  12,800,000 0.597             0.6634   5,069,599
#>       9    1,363,294  13,200,000 0.597             0.8051   6,344,850
#>      10      344,014  13,600,000 0.597             0.9530   7,738,201
#>   Total   34,358,090 118,000,000                           36,090,118
#>  UltimateValue  StdError  CV%
#>      5,225,161   443,978 33.5
#>      6,896,081   489,929 31.5
#>      6,755,612   541,157 29.3
#>      6,798,264   597,696 27.0
#>      6,547,032   658,893 24.6
#>      6,964,541   722,850 22.1
#>      7,537,061   785,803 19.4
#>      7,934,097   842,378 16.6
#>      7,708,144   889,806 14.0
#>      8,082,215   941,203 12.2
#>     70,448,208 5,378,616 14.9