knitr::opts_chunk$set(echo = TRUE) suppressPackageStartupMessages(library(ChainLadder))
Moved NEWS file into RMarkdown package vignette format.
Previously it was required that the row and column names of a triangle be convertible to numeric, although that "requirement" did not always cause a problem. For example, the following sets the rownames of GenIns to the beginning Date of the accident year.
x <- GenIns rownames(x) <- paste0(2001:2010, "-01-01") x
A plot with the lattice=TRUE
option, which previously would blow up,
now displays with nice headings.
plot(x, lattice=TRUE)
It can often be useful to have "origin" values that are not necessarily convertible to numeric. For example, suppose you have a table of claim detail at various evaluation dates. Invariably, such a table will have a Date field holding the date of loss. It would be nice to be able to summarize that data by accident year "cuts". It turns out there's a builtin function in R that will get you most of the way there. It's called 'cut'.
Here we take the GenIns data in long format and
generate 50 claims per accident period.
We assign each claim a random date within the year.
The incurred (or paid) "value" given is a random perturbation of one-fiftieth of
GenInsLong$value.
We accumulate the detail into an accident year triangle using
ChainLadder's as.triangle
method.
The summarized triangle displayed at the end is very similar to GenIns
, and
has informative row labels.
x <- GenInsLong # start off y with x's headings y <- x[0,] names(y)[1] <- "lossdate" set.seed(1234) n = 50 # number of simulated claims per accident perior for (i in 1:nrow(x)) { y <- rbind(y, data.frame( lossdate = as.Date( as.numeric(as.Date(paste0(x[i, "accyear"]+2000, "-01-01"))) + round(runif(n, 0, 364),0), origin = "1970-01-01"), devyear = x[i, "devyear"], incurred.claims = rnorm(n, mean = x[i, "incurred claims"] / n, sd = x[i, "incurred claims"]/(10*n)) )) } # here's the magic cut y$ay <- cut(y$lossdate, breaks = "years") # this summarized triangle is very similar to GenIns as.triangle(y, origin = "ay", dev = "devyear", value = "incurred.claims")
The user is encouraged to experiment with other cut's --
e.g., breaks = "quarters"
will generate accident quarter triangles.
A new function, as.LongTriangle
, will convert a triangle
from "wide" (matrix) format to "long" (data.frame) format.
This differs from ChainLadder's
as.data.frame.triangle method in that the
rownames and colnames of Triangle are stored as factors.
This feature can be particularly important when plotting a triangle
because the order of the "origin" and "dev" values is important.
Additionally, the columns of the resulting data frame may be renamed from the default values ("origin", "dev", and "value") using the "varnames" argument for "origin"/"dev" and the "value.name" argument for "value".
In the following example, the GenIns
triangle in ChainLadder
is converted to a data.frame
with non-default names:
GenLong <- as.LongTriangle(GenIns, varnames = c("accident year", "development age"), value.name = "Incurred Loss") head(GenLong)
In the following plot, the last accident year and the last development age are shown last, rather than second as they would have been if displayed alphabetically (ggplot's default for character data):
library(ggplot2) ggplot(GenLong, aes(x=`development age`, y = `Incurred Loss`, group = `accident year`, color = `accident year`)) + geom_line()
Previously, when an "exposure" attribute was assigned to a triangle
for use with glmReserve
, it was assumed/expected that the user would supply the values in the same order as the accident years.
Then, behind the scenes, glmReserve would use an arithmetic formula to match
the exposure with the appropriate accident year using the numeric "origin" values
after the triangle had been converted to long format.
glmReserve
now allows for "exposure" to have "names"
that coincide with the rownames of the triangle,
which are used to match to origin in long format.
Here is an example, newly found in ?glmReserve
.
GenIns2 <- GenIns rownames(GenIns2) <- paste0(2001:2010, "-01-01") expos <- (7 + 1:10 * 0.4) * 10 names(expos) <- rownames(GenIns2) attr(GenIns2, "exposure") <- expos glmReserve(GenIns2)
The glmReserve
function now supports the negative binomial GLM,
a more natural way to model over-dispersion in count data.
The model is fitted through the glm.nb
function from the MASS
package.
To fit the negative binomial GLM to the loss triangle,
simply set nb = TRUE
in calling the glmReserve function:
(fit6 <- glmReserve(GenIns, nb = TRUE))
New files in the /inst/unittests/
folder can be used for
future enhancements
Contributors of new contributions to those R files are encouraged to utilize those runit scripts for testing, and, of course, add other runit scripts as warrantted.
By default, R's lm
method generates a warning when it detects
an "essentially perfect fit".
This can happen when one column of a triangle is identical to the previous column;
i.e., when all link ratios in a column are the same.
In the example below, the second column is a fixed constant, 1.05,
times the first column.
ChainLadder previously issued the lm warning below.
x <- matrix(byrow = TRUE, nrow = 4, ncol = 4, dimnames = list(origin = LETTERS[1:4], dev = 1:4), data = c( 100, 105, 106, 106.5, 200, 210, 211, NA, 300, 315, NA, NA, 400, NA, NA, NA) ) mcl <- MackChainLadder(x, est.sigma = "Mack") Warning messages: 1: In summary.lm(x) : essentially perfect fit: summary may be unreliable 2: In summary.lm(x) : essentially perfect fit: summary may be unreliable 3: In summary.lm(x) : essentially perfect fit: summary may be unreliable
which may have raised a concern with the user when none was warranted.
Now ChainLadder issues an "informational warning":
x <- matrix(byrow = TRUE, nrow = 4, ncol = 4, dimnames = list(origin = LETTERS[1:4], dev = 1:4), data = c( 100, 105, 106, 106.5, 200, 210, 211, NA, 300, 315, NA, NA, 400, NA, NA, NA) )
mcl <- MackChainLadder(x, est.sigma = "Mack")
Fixed tail extrapolation in Vignette. (Thanks to Mark Lee.)
Added back functionality to estimate the index parameter for the compound Poisson model in 'glmReserve' (now depends on package cplm). This works for both 'formula' and 'bootstrap'.
Added methods 'resid' and plot for class 'glmReserve' (now depends on ggplot2)
New function PaidIncurredChain by Fabio Concina, based on the 2010 Merz & Wuthrich paper Paid-incurred chain claims reserving method
plot.MackChainLadder and plot.BootChainLadder gained new argument
'which', allowing users to specify which sub-plot to display.
Thanks to Christophe Dutang for this suggestion.
Updated NAMESPACE file to comply with new R CMD checks in R-3.3.0
Removed package dependencies on grDevices and Hmisc
Expanded package vignette with new paragraph on importing spreadsheet data, a new section "Paid-Incurred Chain Model" and an added example for a full claims development picture in the "One Year Claims Development Result" section.
New generic function CDR to estimate the one year claims development result. S3 methods for the Mack and bootstrap model have been added already:
New function tweedieReserve to estimate reserves in a GLM framework, including the one year claims development result.
Package vignette has new chapter 'One Year Claims Development Result'.
New example data MW2008 and MW2014 form the Merz & Wuthrich (2008, 2014) papers
Source code development moved from Google Code to GitHub: https://github.com/mages/ChainLadder
as.data.frame.triangle now gives warning message when dev. period is a character
Alessandro Carrato, Giuseppe Crupi and Mario Wuthrich have been added as authors, thanks to their major contribution to code and documentation
Christophe Dutang, Arnaud Lacoume and Arthur Charpentier have been added as contributors, thanks to their feedback, guidance and code contribution
A new function, CLFMdelta, finds the value of delta such that the model coefficients resulting from the 'chainladder' function with that value for argument delta are consistent with an input vector of 'selected' age-to-age factors, subject to restrictions on the 'selected' factors relative to the input 'Triangle'. See the paper "A Family of Chain-Ladder Factor Models for Selected Link Ratios" by Bardis, Majidi, Murphy: http://www.variancejournal.org/issues/?fa=article&abstrID=6943
A new 'coef' method returns the age-to-age factor coefficients of the regression models estimated by the 'chainladder' function.
Exports a function "LRfunction" that calculates a Triangle's link ratio function and can be used to plot the space of "reasonable link ratio selections" per the CLFM paper.
ClarkLDF and ClarkCapeCod functions: additional functionality
A 'vcov' method now exists to produce the covariance matrix of the estimated parameters using the approach in Clark's paper
Additional values (in lists) returned by Clark's methods:
Fine-tuning of maximum likelihood numerical algorithm's control parameters
If the solution is found at the boundary of the parameter region, it is conceivable that a "more optimal" solution might exist if the boundary constraints were not as conservative, so a warning is given
The parameters returned by the methods were the scaled versions; they now at their original scales.
The loss development factor (LDF) being returned by ClarkCapeCod was not documented
New implementation of the methods in David Clark's "LDF Curve Fitting" paper in the 2003 Forum by Daniel Murphy.
'MackChainLadder' has new argument 'alpha' as an additional weighting parameter. As a result, the argument 'weights' is now just that, weights should be between 0 and 1. The argument 'alpha' describes the different chain ladder age-to-age factors: The default for alpha for all development periods is 1. See Mack's 1999 paper: alpha=1 gives the historical chain ladder age-to-age factors, alpha=0 gives the straight average of the observed individual development factors and alpha=2 is the result of an ordinary regression with intercept 0.
Basic 'chainladder' function now available using linear models. See ?chainladder for more information.
More examples for 'MackChainLadder' demonstrate how to apply the MackChainLadder over several triangles in 'one-line'.
'as.data.frame.triangle' has new argument 'lob' (e.g. line of business) which allows to set an additional label column in the data frame output.
'MackChainLadder': Latest position of incomplete triangles were in some cases not returned correctly. Thanks to Ben Escoto for reporting and providing a patch.
'MackChainLadder':
New triangle class with S3 methods for plot, print and conversion from triangles to data.frames and vis versa
New utility functions 'incr2cum' and 'cum2incr' to convert incremental triangles into cumulative triangles and vis versa. Thanks to Chritophe Dutang.
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