epilepsy             package:robustbase             R Documentation

_E_p_i_l_e_p_s_y _A_t_t_a_c_k_s _D_a_t_a _S_e_t

_D_e_s_c_r_i_p_t_i_o_n:

     Data from a clinical trial of 59 patients with epilepsy (Breslow,
     1996) in order to illustrate diagnostic techniques in Poisson
     regression.

_U_s_a_g_e:

     data(epilepsy)

_F_o_r_m_a_t:

     A data frame with 59 observations on the following 11 variables.

     '_I_D' Patient identification number

     '_Y_1' Number of epilepsy attacks patients have during the first
          follow-up period

     '_Y_2' Number of epilepsy attacks patients have during the second
          follow-up period

     '_Y_3' Number of epilepsy attacks patients have during the third
          follow-up period

     '_Y_4' Number of epilepsy attacks patients have during the forth
          follow-up period

     '_B_a_s_e' Number of epileptic attacks recorded during 8 week period
          prior to randomization

     '_A_g_e' Age of the patients

     '_T_r_t' a factor with levels 'placebo' 'progabide' indicating
          whether the anti-epilepsy drug Progabide has been applied or
          not

     '_Y_s_u_m' Total number of epilepsy attacks patients have during the
          four follow-up periods  

     '_A_g_e_1_0' Age of the patients devided by 10

     '_B_a_s_e_4' Variable 'Base' devided by 4

_D_e_t_a_i_l_s:

     Thall and Vail reported data from  a clinical trial of 59 patients
     with epilepsy, 31 of whom were randomized to receive the
     anti-epilepsy drug Progabide and 28 of whom received a placebo.
     Baseline data consisted of the patient's age and the number of
     epileptic seizures recorded during 8 week period prior to
     randomization. The response consisted of counts of seizures
     occuring during the four consecutive follow-up periods of two
     weeks each.

_S_o_u_r_c_e:

     Thall, P.F. and Vail S.C. (1990) Some covariance models for
     longitudinal count data with overdispersion. _Biometrics_ *46*,
     657-671.

_R_e_f_e_r_e_n_c_e_s:

     Diggle, P.J., Liang, K.Y., and Zeger, S.L. (1994) _Analysis of
     Longitudinal Data_; Clarendon Press.

     Breslow N. E. (1996) Generalized linear models: Checking
     assumptions and strengthening conclusions. _Statistica Applicata_
     *8*, 23-41.

_E_x_a_m_p_l_e_s:

     data(epilepsy)
     str(epilepsy)
     pairs(epilepsy[,c("Ysum","Base4","Trt","Age10")])

     Efit1 <- glm(Ysum ~ Age10 + Base4*Trt, family=poisson, data=epilepsy)
     summary(Efit1)

     ## Robust Fit : 

