Insurance                package:MASS                R Documentation

_N_u_m_b_e_r_s _o_f _C_a_r _I_n_s_u_r_a_n_c_e _c_l_a_i_m_s

_D_e_s_c_r_i_p_t_i_o_n:

     The data given in data frame 'Insurance' consist of the numbers of
     policyholders of an insurance company who were exposed to risk,
     and the numbers of car insurance claims made by those
     policyholders in the third quarter of 1973.

_U_s_a_g_e:

     Insurance

_F_o_r_m_a_t:

     This data frame contains the following columns:

     '_D_i_s_t_r_i_c_t' factor: district of residence of policyholder (1 to 4):
          4 is major cities.

     '_G_r_o_u_p' an ordered factor: group of car with levels  <1 litre,
          1-1.5 litre, 1.5-2 litre, >2 litre.

     '_A_g_e' an ordered factor: the age of the insured in 4 groups
          labelled <25, 25-29, 30-35, >35.

     '_H_o_l_d_e_r_s' numbers of policyholders.

     '_C_l_a_i_m_s' numbers of claims


_S_o_u_r_c_e:

     L. A. Baxter, S. M. Coutts and G. A. F. Ross (1980) Applications
     of linear models in motor insurance. _Proceedings of the 21st
     International Congress of Actuaries, Zurich_ pp. 11-29.

     M. Aitkin, D. Anderson, B. Francis and J. Hinde (1989)
     _Statistical Modelling in GLIM._ Oxford University Press.

_R_e_f_e_r_e_n_c_e_s:

     Venables, W. N. and Ripley, B. D. (1999) _Modern Applied
     Statistics with S-PLUS._ Third Edition. Springer.

_E_x_a_m_p_l_e_s:

     ## main-effects fit as Poisson GLM with offset
     glm(Claims ~ District + Group + Age + offset(log(Holders)),
         data = Insurance, family = poisson)

     # same via loglm
     loglm(Claims ~ District + Group + Age + offset(log(Holders)),
           data = Insurance)