BOOTSimpson              package:BSagri              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 _S_i_m_p_s_o_n _i_n_d_i_c_e_s

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

     NOTE: This is a Test-version and is not sufficiently checked for
     correctness so far. Simultaneous confidence intervals for
     differences and ratios of Simpsons indices of diversity are
     calculated from data sets with repeated samples of communities and
     designs with  more than two treatments groups. The intervals are
     calculated based on a stratified, non-parametric  ordinary
     bootstrap sample of Simpsonindices, and applying the Algorithm of
     Besag et al.(1995) on the joint empirical distribution of
     differences (BOOTSimpsonD) or ratios (BOOTSimpsonR) of the
     original distribution.

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

     BOOTSimpsonD(X, f, type = "Dunnett", cmat = NULL, conf.level = 0.95,
      alternative=c("two.sided", "less", "greater"), madj=TRUE, ...)

     BOOTSimpsonR(X, f, type = "Dunnett", cmat = NULL, conf.level = 0.95,
      alternative=c("two.sided", "less", "greater"), madj=TRUE, ...)

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

       X: a data.frame of dimension n times p, containing integer
          entries as species counts of p species from n independent
          samplings

       f: a factor, usually with more than two levels. Must be of
          length n, when X is an n times p matrix 

    type: a single character string, defining a contrast type.
          Supported options are 'Dunnett','Tukey','Sequen'; for more
          options, see '?contrMatratio' in 'mratios' when using
          'BOOTSimpsonR' or 'contrMat' in 'multcomp' when using
          'BOOTSimpsonD' 

    cmat: user defined contrasts: when using 'BOOTSimpsonD', this
          should be a numeric contrast matrix, with k columns when the
          factor 'f' has k levels and should define differences of
          these levels; when using 'BOOTSimpsonR', this should be a
          list with two numeric contrast matrices in the entries 'numC'
          and 'denC' for numerators and denominator of the ratios,
          respectively; each having k columns when the factor 'f' has k
          levels 

conf.level: a single numeric value between 0.5 and 1  

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

    madj: a single logical value, indicating whether simultaneous (if
          'TRUE', the default) or marginal confidence intervals (if
          FALSE) shall be computed 

     ...: Further arguments to be passed to the function 'boot' from
          package 'boot'; most importantly, the number of Bootstrap
          samples can be chosen via the parameter 'R' (default is
          'R=999'); see '?boot' for further options. 

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

     NOTE: This is a test version!

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

     If 'madj=TRUE', an object of class "SCSnp", see 'SCSnp' for
     details. If 'madj=FALSE', an object of class "CInp", see 'CInp'
     for details.

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

     Frank Schaarschmidt

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

     'SCSnp', these function internally make use of 'CCDiff.boot',
     'CCDiff.default', 'CCRatio.boot', 'CCRatio.default', 'boot' and
     'estSimpsonf'.

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

     X<-t(rmultinom(n=40,size=100,
      prob=c(0.3,0.2,0.2,0.1,0.1,0.05,0.05)))

     colnames(X)<-paste("Sp",1:7,sep="")

     DAT<-as.data.frame(X)

     f<-as.factor(rep(c("A","B","C","D"),each=10))

     SCIdunnettd<-BOOTSimpsonD(X=DAT, f=f, type = "Dunnett",
      conf.level = 0.95, alternative = "two.sided")

     SCIdunnettd

     SCIdunnettr<-BOOTSimpsonR(X=DAT, f=f, type = "Dunnett",
      conf.level = 0.95, alternative = "two.sided")

     SCIdunnettr

