caesar               package:Fahrmeir               R Documentation

_C_a_e_s_a_r_i_a_n _B_i_r_t_h _S_t_u_d_y

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

     Data on infection from births by Caesarian section

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

     data(caesar)

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

     A data frame with 24 observations on the following 7 variables.

     _y a factor with levels '1' '2' '3', the response

     _w number of patients in group

     _n_o_p_l_a_n a factor with levels 'not' 'planned',  was the caesarian
          planned?

     _f_a_c_t_o_r a factor with levels 'risk factors' 'without',  was there
          risk factors?

     _a_n_t_i_b a factor with levels 'antibiotics' 'without'

     _y_l logistic response, 0=no infection

     _p_a_t_c_o covariate pattern number

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

     Infection from birth by Caesarian section. The response variable, 
     'y', has levels 1=type I infection, 2=type II infection,  3=none
     infection. Where risk-factors (diabetes, overweight, others)
     present? Where antibiotics used as prophylaxis? Aim is to  analyse
     effects on response by covariates.

_A_u_t_h_o_r(_s):

     Kjetil Halvorsen

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

     Ludwig Fahrmeir, Gerhard Tutz (1994):  Multivariate Statistical
     Modelling Based on Generalized Linear Models. Springer Series in
     Statistics.  Springer Verlag. New-York  Berlin Heidelberg

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

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

     summary(caesar)
     caesar.glm1 <- glm(yl ~ noplan+factor+antib, data=caesar, weight=w, 
                            family=binomial(link="logit"))
     caesar.glm2 <- glm(yl ~ noplan+factor+antib, data=caesar, weight=w, 
                        family=binomial(link="probit"))
     summary(caesar.glm1)
     summary(caesar.glm2)

