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

     '_G_r_o_u_p' group of car (1 to 4), <1 litre, 1-1.5 litre, 1.5-2 litre,
          >2 litre.

     '_A_g_e' of driver in 4 ordered groups, <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)

