View source: R/features_02_claim_size.R
| claim_size | R Documentation | 
Simulates and returns the size of each of the claims occurring in each of
the periods, given its cumulative distribution function.
 
 Note that claim_size() aims to model the claim sizes
without inflation.
claim_size(frequency_vector, simfun, type = c("r", "p"), ...)
frequency_vector | 
 a vector of claim frequencies for all the periods.  | 
simfun | 
 optional alternative sampling distribution; see Details.  | 
type | 
 the type of   | 
... | 
 other arguments/parameters to be passed onto   | 
By default claim_size() assumes a left truncated power
normal distribution: S^0.2 ~ Normal (9.5, sd = 3), left truncated
at 30. The truncation is done via resampling for rejected values. 
 
Users can opt to use alternative distributions if desired. As discussed in
claim_frequency, users can declare such specification through
the simfun argument, which takes both random generation functions
(type = "r", the default) and cumulative distribution functions
(type = "p"). See Examples. 
 
For the latter, claim_size() will first search for the existence of the
corresponding r-function. If it notes the existence of such an r-function
(e.g. rweibull for simfun = pweibull), it will directly apply the
r-function to optimise simulation efficiency. Otherwise, the function uses
a numerical inverse transform method for simulation (see
simulate_cdf), which may not be the most efficient and can
potentially result in errors if an appropriate range is not specified in
the optional arguments.
A list of claim sizes such that the ith component of the
list gives the sizes for all claims that occurred in period i.
n_vector <- c(90, 79, 102, 78, 86, 88, 116, 84, 93, 104)
claim_size(n_vector)[[1]] # gives the sizes for all
                          # all claims incurred in period 1
# use some custom pre-defined distribution function
claim_size(n_vector, stats::rweibull, shape = 4, scale = 100000)[[1]]
# equivalently
claim_size(n_vector, stats::pweibull, "p", shape = 4, scale = 100000)[[1]]
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