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Prediction of a claims triangle

predict.TriangleModel.Rd

The function is internally used by MackChainLadder to forecast future claims.

Usage

# S3 method for class 'TriangleModel'
predict(object,...)
# S3 method for class 'ChainLadder'
predict(object,...)

Arguments

object

a list with two items: Models, Triangle

Models

list of linear models for each development period

Triangle

input triangle to forecast

...

not in use

Value

FullTriangle

forecasted claims triangle

Author

Markus Gesmann

See also

See also chainladder, MackChainLadder

Examples


RAA
#>       dev
#> origin    1     2     3     4     5     6     7     8     9    10
#>   1981 5012  8269 10907 11805 13539 16181 18009 18608 18662 18834
#>   1982  106  4285  5396 10666 13782 15599 15496 16169 16704    NA
#>   1983 3410  8992 13873 16141 18735 22214 22863 23466    NA    NA
#>   1984 5655 11555 15766 21266 23425 26083 27067    NA    NA    NA
#>   1985 1092  9565 15836 22169 25955 26180    NA    NA    NA    NA
#>   1986 1513  6445 11702 12935 15852    NA    NA    NA    NA    NA
#>   1987  557  4020 10946 12314    NA    NA    NA    NA    NA    NA
#>   1988 1351  6947 13112    NA    NA    NA    NA    NA    NA    NA
#>   1989 3133  5395    NA    NA    NA    NA    NA    NA    NA    NA
#>   1990 2063    NA    NA    NA    NA    NA    NA    NA    NA    NA

CL <- chainladder(RAA)
CL
#> $Models
#> $Models[[1]]
#> 
#> Call:
#> lm(formula = y ~ x + 0, data = data.frame(x = Triangle[, i], 
#>     y = Triangle[, i + 1]), weights = weights[, i]/Triangle[, 
#>     i]^delta[i])
#> 
#> Coefficients:
#>     x  
#> 2.999  
#> 
#> 
#> $Models[[2]]
#> 
#> Call:
#> lm(formula = y ~ x + 0, data = data.frame(x = Triangle[, i], 
#>     y = Triangle[, i + 1]), weights = weights[, i]/Triangle[, 
#>     i]^delta[i])
#> 
#> Coefficients:
#>     x  
#> 1.624  
#> 
#> 
#> $Models[[3]]
#> 
#> Call:
#> lm(formula = y ~ x + 0, data = data.frame(x = Triangle[, i], 
#>     y = Triangle[, i + 1]), weights = weights[, i]/Triangle[, 
#>     i]^delta[i])
#> 
#> Coefficients:
#>     x  
#> 1.271  
#> 
#> 
#> $Models[[4]]
#> 
#> Call:
#> lm(formula = y ~ x + 0, data = data.frame(x = Triangle[, i], 
#>     y = Triangle[, i + 1]), weights = weights[, i]/Triangle[, 
#>     i]^delta[i])
#> 
#> Coefficients:
#>     x  
#> 1.172  
#> 
#> 
#> $Models[[5]]
#> 
#> Call:
#> lm(formula = y ~ x + 0, data = data.frame(x = Triangle[, i], 
#>     y = Triangle[, i + 1]), weights = weights[, i]/Triangle[, 
#>     i]^delta[i])
#> 
#> Coefficients:
#>     x  
#> 1.113  
#> 
#> 
#> $Models[[6]]
#> 
#> Call:
#> lm(formula = y ~ x + 0, data = data.frame(x = Triangle[, i], 
#>     y = Triangle[, i + 1]), weights = weights[, i]/Triangle[, 
#>     i]^delta[i])
#> 
#> Coefficients:
#>     x  
#> 1.042  
#> 
#> 
#> $Models[[7]]
#> 
#> Call:
#> lm(formula = y ~ x + 0, data = data.frame(x = Triangle[, i], 
#>     y = Triangle[, i + 1]), weights = weights[, i]/Triangle[, 
#>     i]^delta[i])
#> 
#> Coefficients:
#>     x  
#> 1.033  
#> 
#> 
#> $Models[[8]]
#> 
#> Call:
#> lm(formula = y ~ x + 0, data = data.frame(x = Triangle[, i], 
#>     y = Triangle[, i + 1]), weights = weights[, i]/Triangle[, 
#>     i]^delta[i])
#> 
#> Coefficients:
#>     x  
#> 1.017  
#> 
#> 
#> $Models[[9]]
#> 
#> Call:
#> lm(formula = y ~ x + 0, data = data.frame(x = Triangle[, i], 
#>     y = Triangle[, i + 1]), weights = weights[, i]/Triangle[, 
#>     i]^delta[i])
#> 
#> Coefficients:
#>     x  
#> 1.009  
#> 
#> 
#> 
#> $Triangle
#>       dev
#> origin    1     2     3     4     5     6     7     8     9    10
#>   1981 5012  8269 10907 11805 13539 16181 18009 18608 18662 18834
#>   1982  106  4285  5396 10666 13782 15599 15496 16169 16704    NA
#>   1983 3410  8992 13873 16141 18735 22214 22863 23466    NA    NA
#>   1984 5655 11555 15766 21266 23425 26083 27067    NA    NA    NA
#>   1985 1092  9565 15836 22169 25955 26180    NA    NA    NA    NA
#>   1986 1513  6445 11702 12935 15852    NA    NA    NA    NA    NA
#>   1987  557  4020 10946 12314    NA    NA    NA    NA    NA    NA
#>   1988 1351  6947 13112    NA    NA    NA    NA    NA    NA    NA
#>   1989 3133  5395    NA    NA    NA    NA    NA    NA    NA    NA
#>   1990 2063    NA    NA    NA    NA    NA    NA    NA    NA    NA
#> 
#> $delta
#> [1] 1 1 1 1 1 1 1 1 1
#> 
#> $weights
#>       dev
#> origin 1  2  3  4  5  6  7  8  9 10
#>   1981 1  1  1  1  1  1  1  1  1  1
#>   1982 1  1  1  1  1  1  1  1  1 NA
#>   1983 1  1  1  1  1  1  1  1 NA NA
#>   1984 1  1  1  1  1  1  1 NA NA NA
#>   1985 1  1  1  1  1  1 NA NA NA NA
#>   1986 1  1  1  1  1 NA NA NA NA NA
#>   1987 1  1  1  1 NA NA NA NA NA NA
#>   1988 1  1  1 NA NA NA NA NA NA NA
#>   1989 1  1 NA NA NA NA NA NA NA NA
#>   1990 1 NA NA NA NA NA NA NA NA NA
#> 
#> attr(,"class")
#> [1] "ChainLadder"   "TriangleModel" "list"         
predict(CL)
#>       dev
#> origin    1         2         3        4        5        6        7        8
#>   1981 5012  8269.000 10907.000 11805.00 13539.00 16181.00 18009.00 18608.00
#>   1982  106  4285.000  5396.000 10666.00 13782.00 15599.00 15496.00 16169.00
#>   1983 3410  8992.000 13873.000 16141.00 18735.00 22214.00 22863.00 23466.00
#>   1984 5655 11555.000 15766.000 21266.00 23425.00 26083.00 27067.00 27967.34
#>   1985 1092  9565.000 15836.000 22169.00 25955.00 26180.00 27277.85 28185.21
#>   1986 1513  6445.000 11702.000 12935.00 15852.00 17649.38 18389.50 19001.20
#>   1987  557  4020.000 10946.000 12314.00 14428.00 16063.92 16737.55 17294.30
#>   1988 1351  6947.000 13112.000 16663.88 19524.65 21738.45 22650.05 23403.47
#>   1989 3133  5395.000  8758.905 11131.59 13042.60 14521.43 15130.38 15633.68
#>   1990 2063  6187.677 10045.834 12767.13 14958.92 16655.04 17353.46 17930.70
#>       dev
#> origin        9       10
#>   1981 18662.00 18834.00
#>   1982 16704.00 16857.95
#>   1983 23863.43 24083.37
#>   1984 28441.01 28703.14
#>   1985 28662.57 28926.74
#>   1986 19323.01 19501.10
#>   1987 17587.21 17749.30
#>   1988 23799.84 24019.19
#>   1989 15898.45 16044.98
#>   1990 18234.38 18402.44