rmspfc: Rate-making system based on the posteriori freuency component
In nlirms: Non-Life Insurance Rate-Making System

Description Usage Arguments Details Value Author(s) References Examples

rmspfc() function gives the rate-making system based on the posteriori freuency component. Values given by rmspfc() function is equal to with expected number of claims given by enc.family (i.e. enc.PGA, enc.PIGA, enc.PGIG) for different amounts of time and claim.

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rmspfc(time = 5, claim = 5, fmu = .2, fsigma = 2, fnu = 1,
  family = "NO", round = 2, size = 8, padlength = 4, padwidth = 2,
  ...)

`time`	time period to designing of rate-making system based on the posteriori freuency component
`claim`	number of claims to designing of rate-making system based on the posteriori freuency component
`fmu`	mu parameter of frequency model in designing of rate-making system
`fsigma`	sigma parameter of frequency model in designing of rate-making system
`fnu`	nu parameter of frequency model in designing of rate-making system
`family`	a nlirms.family object, which is used to define the frequency model to designing of rate-making system
`round`	rounds the rate-making system values to the specified number of decimal places
`size`	indicates the size of graphical table for rate-making system
`padlength`	indicates the length of each graphical table cells
`padwidth`	indicates the width of each graphical table cells
`...`	for further arguments

rmspfc() function gives the rate-making system in the form of a table where each table cells is related to the one time and claim. for example the cell with time=2 and claim=1, shows the expected number of claims in next year for a ploicyholder that who had a one claim in past two years.

rmspfc() function return the expected number of claims of policyholders based on the different models.

Saeed Mohammadpour (s.mohammadpour1111@gmail.com), Soodabeh Mohammadpoor Golojeh (s.mohammadpour@gmail.com)

Frangos, N. E., & Vrontos, S. D. (2001). Design of optimal bonus-malus systems with a frequency and a severity component on an individual basis in automobile insurance. ASTIN Bulletin: The Journal of the IAA, 31(1), 1-22.

Lemaire, J. (1995) Bonus-Malus Systems in Automobile Insurance, Kluwer Academic Publishers, Massachusetts.

MohammadPour, S., Saeedi, K., & Mahmoudvand, R. (2017). Bonus-Malus System Using Finite Mixture Models. Statistics, Optimization & Information Computing, 5(3), 179-187.

Najafabadi, A. T. P., & MohammadPour, S. (2017). A k-Inflated Negative Binomial Mixture Regression Model: Application to Rate–Making Systems. Asia-Pacific Journal of Risk and Insurance, 12.

Rigby, R. A., & Stasinopoulos, D. M. (2005). Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society: Series C (Applied Statistics), 54(3), 507-554.

Stasinopoulos, D. M., Rigby, B. A., Akantziliotou, C., Heller, G., Ospina, R., & Motpan, N. (2010). gamlss. dist: Distributions to Be Used for GAMLSS Modelling. R package version, 4-0.

Stasinopoulos, D. M., & Rigby, R. A. (2007). Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, 23(7), 1-46.

# rate-Making system based on the Poisson-Gamma model for frequency component
rmspfc(time = 5, claim = 5, fmu = .2, fsigma = 2, fnu = 1, family = "PGA", round
= 2, size = 8, padlength = 4, padwidth = 2)

# rate-Making system based on the Poisson-Inverse Gamma model for frequency component
rmspfc(time = 5, claim = 5, fmu = .2, fsigma = 2, fnu = 1, family = "PIGA",
round = 2, size = 8, padlength = 4, padwidth = 2)

# rate-Making system based on the Poisson-Generalized Inverse Gaussian model for frequency
rmspfc(time = 5, claim = 5, fmu = .2, fsigma = 2, fnu = 1, family = "PGIG",
round = 2, size = 8, padlength = 4, padwidth = 2)