ChainLadder 0.2.19

ChainLadder 0.2.18

ChainLadder 0.2.17

ChainLadder 0.2.16

ChainLadder 0.2.15

ChainLadder 0.2.14

ChainLadder 0.2.13

ChainLadder 0.2.12

ChainLadder 0.2.11

ChainLadder 0.2.10

Thanks to Marco De Virgilis.

ChainLadder 0.2.9

ChainLadder 0.2.8

ChainLadder 0.2.7

ChainLadder 0.2.6

ChainLadder 0.2.5

ChainLadder 0.2.4

ChainLadder 0.2.3

Changes

NEWS file

Moved NEWS file to Markdown format.

Triangles may now have non-numeric rownames

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")

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.

New as.LongTriangle function

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")

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()

glmReserve “exposure” attribute may now have names

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)

glmReserve adds support for negative binomial GLM

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 unit tests

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.

Clarified warnings issued by MackChainLadder

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")

Bug fixes

Fixed tail extrapolation

Fixed tail extrapolation in Vignette. (Thanks to Mark Lee.)

ChainLadder 0.2.2 [2015-08-31]

ChainLadder 0.2.1

New Features

Changes

ChainLadder 0.2.0

New Features

Changes

ChainLadder 0.1.9

Changes

ChainLadder 0.1.8

Bug Fixes

ChainLadder 0.1.7

Changes

ChainLadder 0.1.6

New Features

Changes

ChainLadder 0.1.5-6

New Features

ChainLadder 0.1.5-5

Bug Fixes

ChainLadder 0.1.5-4

New Features

Changes

ChainLadder 0.1.5-3

New Features

Changes

ChainLadder 0.1.5-2

New Features

Changes

Bug Fixes

ChainLadder 0.1.5-1

ChainLadder 0.1.5-0

New Features

ChainLadder 0.1.4-4

ChainLadder 0.1.4-3

New Features

ChainLadder 0.1.4-2

Bug fixes

ChainLadder 0.1.4-1

New Features

Bug fixes

ChainLadder 0.1.4-0

New Features

ChainLadder 0.1.3-4

Bug fixes

ChainLadder 0.1.3-3

New Features

User-visible changes

Bug fixes

ChainLadder 0.1.2-13

User-visible changes

Bug fixes

ChainLadder 0.1.2-12

New Features

Bug fixes

ChainLadder 0.1.2-11

Bug fixes

ChainLadder 0.1.2-10

User-visible changes

Bug fixes

ChainLadder 0.1.2-9

User-visible changes

ChainLadder 0.1.2-8

User-visible changes

ChainLadder 0.1.2-7

User-visible changes

ChainLadder 0.1.2-6

User-visible changes

ChainLadder 0.1.2-5

New Features

User-visible changes

Bug fixes

ChainLadder 0.1.2-4

ChainLadder 0.1.2-2

ChainLadder 0.1.2-0

ChainLadder 0.1.1-5

ChainLadder 0.1.1-4

ChainLadder 0.1.1-3

ChainLadder 0.1.1-2

ChainLadder 0.1.1-1