ACF1                  package:DAAG                  R Documentation

_A_b_e_r_r_a_n_t _C_r_y_p_t _F_o_c_i _i_n _R_a_t _C_o_l_o_n_s

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

     Numbers of aberrant crypt foci (ACF) in the  section 1 of the
     colons of 22 rats subjected to a single dose of the carcinogen
     azoxymethane (AOM), sacrificed at 3 different times.

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

     ACF1

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

     This data frame contains the following columns:

     _c_o_u_n_t The number of ACF observed in section 1 of each rat colon

     _e_n_d_t_i_m_e Time of sacrifice, in weeks following injection of AOM

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

     Ranjana P. Bird, Faculty of Human Ecology, University of Manitoba,
     Winnipeg, Canada.

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

     E.A. McLellan, A. Medline and R.P. Bird.  Dose response and
     proliferative characteristics of aberrant crypt foci: putative
     preneoplastic lesions in rat colon.  Carcinogenesis, 12(11):
     2093-2098,  1991.

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

     sapply(split(ACF1$count,ACF1$endtime),var)
     plot(count ~ endtime, data=ACF1, pch=16)
     pause()
     print("Poisson Regression - Example 8.3")
     ACF.glm0 <- glm(formula = count ~ endtime, family = poisson, data = ACF1)
     summary(ACF.glm0)

     # Is there a quadratic effect?
     pause()

     ACF.glm <- glm(formula = count ~ endtime + I(endtime^2),
       family = poisson, data = ACF1)
     summary(ACF.glm)

     # But is the data really Poisson?  If not, try quasipoisson:
     pause()

     ACF.glm <- glm(formula = count ~ endtime + I(endtime^2),
       family = quasipoisson, data = ACF1)
     summary(ACF.glm)

