multcomp              package:multcomp              R Documentation

_G_e_n_e_r_a_l _I_n_f_o_r_m_a_t_i_o_n _o_n _t_h_e _m_u_l_t_c_o_m_p _P_a_c_k_a_g_e

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

     The 'multcomp' package allows for multiple comparisons of k groups
     in general linear models. We use the unifying representations of
     multiple contrast tests, which include all common multiple
     comparison procedures, such as the many-to-one comparisons of
     Dunnett, the all-pairwise comparisons of Tukey and many other
     procedures. We provide a list of 9 standard procedures (Dunnett,
     Tukey, Sequen, AVE, Changepoint, Williams, Marcus, McDermott,
     Tetrade), where the user selects the  comparisons of his interest.
     In addition, a free input interface  for the contrast matrix
     allows for more special comparisons.

     The comparisons itself are not restricted to balanced or simple
     designs. Instead, the programs are designed to suit multiple
     comparisons within the general linear model, thus allowing for
     covariates, nested effects, correlated means and missing values.
     The program is designed for the normal set-up with a common
     (possibly unknown) variance and a known covariance matrix. But
     instead of using the usual Bonferroni and Holm procedures, we take
     the exact correlations between the test statistics into account by
     use of the multivariate t-distribution. The resulting procedures
     are therefore more powerful (the Bonferroni and Holm adjusted 
     p-values are reported for reference). We also allow the user to
     perform an asymptotic analysis based on the multivariate normal
     distribution (as required e.g. in multiple comparisons based on
     asymptotic rank transformations; assumed asymptotic normality when
     comparing binomial parameters; etc.). Two functions will be
     provided. The first one computes confidence intervals for the
     common single step procedures ('simint').  This approach can be
     uniformly improved by applying the closed testing principle, what
     is implemented in the second function ('simtest';  but no
     confidence intervals are available for the latter procedure). Use
     either 'csimint' or 'csimtest' if you want to pass the estimates
     by hand. 

     For testing and validation purposes we included some examples from
     Westfall et al. (1999).

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

     Frank Bretz <bretz@ifgb.uni-hannover.de>, Torsten Hothorn
     <Torsten.Hothorn@rzmail.uni-erlangen.de> and Peter Westfall
     <WESTFALL@ba.ttu.edu>

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

     P. H. Westfall, R. D. Tobias, D. Rom, R. D. Wolfinger, Y. Hochberg
     (1999). _Multiple Comparisons and Multiple Tests Using the SAS
     System_. Cary, NC: SAS Institute Inc.

     Peter Westfall (1997), Multiple testing of general contrasts using
     logical constraints and correlations, _Journal of the American
     Statistical Association_, *92*(437), 299-306.

     Frank Bretz, Alan Genz and Ludwig A. Hothorn (2001), On the
     numerical availability of multiple comparison procedures.
     _Biometrical Journal_, *43*(5), 645-656.

