binomORci               package:MCPAN               R Documentation

_S_i_m_u_l_t_a_n_e_o_u_s _c_o_n_f_i_d_e_n_c_e _i_n_t_e_r_v_a_l_s _f_o_r _o_d_d_s _r_a_t_i_o_s

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

     Approximate simultaneous confidence intervals for (weighted
     geometric means of) odds ratios are constructed. Estimates are
     derived from fitting a glm on the logit-link, approximate
     intervals are constructed on the log-link, and transformed to
     origninal scale.

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

     binomORci(x, ...)

     ## Default S3 method:
     binomORci(x, n, names = NULL,
      type = "Dunnett", cmat = NULL, alternative = "two.sided",
      conf.level = 0.95, ...)

     ## S3 method for class 'formula':
     binomORci(formula, data,
      type = "Dunnett", cmat = NULL, alternative = "two.sided",
      conf.level = 0.95, ...)

     ## S3 method for class 'table':
     binomORci(x,
      type = "Dunnett", cmat = NULL, alternative = "two.sided",
      conf.level = 0.95, ...)

     ## S3 method for class 'matrix':
     binomORci(x,
      type = "Dunnett", cmat = NULL, alternative = "two.sided",
      conf.level = 0.95, ...)

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

       x: a numeric vector, giving the number of successes in I
          independent samples, or an object of class '"table"',
          representing the 2xk-table, or an object of class '"matrix"',
          representing the 2xk-table

       n: numeric vector, giving the number of trials (i.e. the sample
          size) in each of the I groups (only required if 'x' is a
          numeric vector, ignored otherwise)  

   names: an optional character string, giving the names of the groups/
          sample in 'x', 'n'; if not specified the possible names of x
          are taken as group names (ignored if 'x' is a table or
          matrix)

 formula: a two-sided formula of the style 'response ~ treatment',
          where 'response' should be a categorical variable with two
          levels, while treatment should be a factor specifying the
          treatment levels

    data: a data.frame, containing the variables specified in formula

    type: a character string, giving the name of a contrast method, as
          defined in 'contrMat(multcomp)'; ignored if 'cmat' is
          sepcified 

    cmat: a optional contrast matrix 

alternative: a single character string, one of "two.sided", "less",
          "greater" 

conf.level: a single numeric value, simultaneous confidence level 

     ...: arguments to be passed to 'binomest', currently only
          'success' labelling the event which should be considered as
          success

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

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

     A object of class "binomORci", a list containing: 

conf.int: a matrix with 2 columns: lower and upper confidence bounds,
          and M rows

alternative : character string, as input

conf.level: single numeric value, as input

estimate: a matrix with 1 column: containing the estimates of the
          contrasts

       x: the observed number of successes

       n: the number of trials

       p: the estimated proportions

 success: a character string labelling the event considered as success

   names: the group names

  method: a character string, specifying the method of interval
          construction

    cmat: the contrast matrix used

_N_o_t_e:

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

     Daniel Gerhard, Frank Schaarschmidt

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

     . . .

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

     Intervals for the risk difference 'binomRDci', summary for odds
     ratio confidence intervals 'summary.binomORci' plot for confidence
     intervals 'plot.sci'

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

     data(liarozole)

     table(liarozole)

     ORlia<-binomORci(Improved ~ Treatment, data=liarozole, success="y")
     ORlia
     summary(ORlia)
     plot(ORlia)

     # if data are available as table:

     tab<-table(liarozole)
     ORlia2<-binomORci(tab, success="y")
     ORlia2

