glmD                 package:Design                 R Documentation

_D_e_s_i_g_n _V_e_r_s_i_o_n _o_f _g_l_m

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

     This function saves 'Design' attributes with the fit object so
     that 'anova.Design', 'plot.Design', etc. can be used just as with
     'ols' and other fits.  No 'validate' or 'calibrate' methods exist
     for 'glmD' though.

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

     glmD(formula, family = gaussian, data = list(), weights = NULL, subset =
     NULL, na.action = na.fail, start = NULL, offset = NULL, control =
     glm.control(...), model = TRUE, method = "glm.fit", x = FALSE, y = TRUE,
     contrasts = NULL, ...)

     ## S3 method for class 'glmD':
     print(x, digits=4, ...)

_A_r_g_u_m_e_n_t_s:

 formula: 

  family: 

    data: 

 weights: 

  subset: 

na.action: 

   start: 

  offset: 

 control: 

   model: 

  method: 

       x: 

       y: 

contrasts: see 'glm'; for 'print', 'x' is the result of 'glmD'

     ...: ignored for 'print'

  digits: number of significant digits to print

_V_a_l_u_e:

     a fit object like that produced by 'glm' but with 'Design'
     attributes and a 'class' of '"Design"', '"glmD"', and '"glm"' or
     '"glm.null"'.

_S_e_e _A_l_s_o:

     'glm','Design'

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

     ## Dobson (1990) Page 93: Randomized Controlled Trial :
     counts <- c(18,17,15,20,10,20,25,13,12)
     outcome <- gl(3,1,9)
     treatment <- gl(3,3)
     f <- glm(counts ~ outcome + treatment, family=poisson())
     f
     anova(f)
     summary(f)
     f <- glmD(counts ~ outcome + treatment, family=poisson())
     # could have had rcs( ) etc. if there were continuous predictors
     f
     anova(f)
     summary(f, outcome=c('1','2','3'), treatment=c('1','2','3'))

