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Plotting Predicted Run-Off Diagonal

plot.profilePredict.Rd

The function provides a graphical visualization of a 1-step-ahead prediction for the functional (development) profiles (so called new running diagonal) obtained by the S3 method predict() applied to the output of the PARALLAX, REACT, or MACRAME algorithm—the R functions parallelReserve() or mcReserve().

Usage

# S3 method for class 'profilePredict'
plot(
  x,
  xlab = "Development period",
  ylab = "Cumulative claims",
  main = "",
  trueProfiles = NULL,
  default.legend = TRUE,
  ...
)

Arguments

x

an object of the class profileLadder which is the output form parallelReserve() or mcReserve()

xlab

label for the x axis

ylab

label for the y axis

main

title of the plot

trueProfiles

optional parameter (set to NULL by default) providing true profiles, if available. In such case, the predicted diagonal is also graphically compared with the true profile developments. The parameter can be a vector (of the same length as the number of rows in the run-off triangle) providing true values of the next running diagonal or it can be a matrix (an object of the class matrix or triangle) with the dimensions n x m (where n x n is the dimension of the run-off triangle and m > n).

default.legend

logical to indicate whether a default plot legend (utilizing the information from the R class profileLadder) should be provided (DEFAULT)

...

other graphical parameters to plot

Value

A graph with the observed functional development profiles from the input run-off triangle and the predicted new running diagonal (1-step-ahead prediction)

See also

[parallelReserve(), mcReserve(), predict.profileLadder()

Examples

## new running diagonal provided by PARALLAX
print(CameronMutual)
#>       dev
#> origin    1     2     3     4     5     6     7     8     9    10
#>     1  5244  9228 10823 11352 11791 12082 12120 12199 12215 12215
#>     2  5984  9939 11725 12346 12746 12909 13034 13109 13113 13115
#>     3  7452 12421 14171 14752 15066 15354 15637 15720 15744 15786
#>     4  7115 11117 12488 13274 13662 13859 13872 13935 13973 13972
#>     5  5753  8969  9917 10697 11135 11282 11255 11331 11332 11354
#>     6  3937  6524  7989  8543  8757  8901  9013  9012  9046  9164
#>     7  5127  8212  8976  9325  9718  9795  9833  9885  9816  9815
#>     8  5046  8006  8984  9633 10102 10166 10261 10252 10252 10252
#>     9  5129  8202  9185  9681  9951 10033 10133 10182 10182 10183
#>     10 3689  6043  6789  7089  7164  7197  7253  7267  7266  7266
plot(predict(parallelReserve(CameronMutual)))


## new running diagonal with the true profiles
print(covid19CZ)
#>               week1 week2 week3 week4 week5 week6 week7 week8
#> 01/03 - 07/03    27    91   392   676  1158  1366  1396  1482
#> 08/03 - 14/03    99   476  1126  1702  2058  2176  2261    NA
#> 15/03 - 21/03   180   588   970  1173  1286  1356    NA    NA
#> 22/03 - 28/03    35    92   164   200   219    NA    NA    NA
plot(predict(mcReserve(covid19CZ[,4:7])), trueProfiles = covid19CZ)
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