msfit                package:pvclust                R Documentation

_C_u_r_v_e _F_i_t_t_i_n_g _f_o_r _M_u_l_t_i_s_c_a_l_e _B_o_o_t_s_t_r_a_p _R_e_s_a_m_p_l_i_n_g

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

     'msfit' performs curve fitting for multiscale bootstrap
     resampling. It generates an object of class 'msfit'. Several
     generic methods are available.

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

     msfit(bp, r, nboot)

     ## S3 method for class 'msfit':
     plot(x, curve=TRUE, main=NULL, sub=NULL, xlab=NULL, ylab=NULL, ...)

     ## S3 method for class 'msfit':
     lines(x, col=2, lty=1, ...)

     ## S3 method for class 'msfit':
     summary(object, digits=3, ...)

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

      bp: numeric vector of bootstrap probability values.

       r: numeric vector of relative sample size of bootstrap samples
          defined as r=n'/n for original sample size n and bootstrap
          sample size n'.

   nboot: numeric value (vector) of the number of bootstrap
          replications.

       x: object of class 'msfit'.

   curve: logical. If 'TRUE', the fitted curve is drawn.

main, sub, xlab, ylab, col, lty: generic graphic parameters.

  object: object of class 'msfit'.

  digits: integer indicating the precision to be used in rounding.

     ...: other parameters to be used in the functions.

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

     function 'msfit' performs the curve fitting for multiscale
     bootstrap resampling. In package 'pvclust' this function is only
     called from the function 'pvclust' (or 'parPvclust'), and may
     never be called from users. However one can access a list of
     'msfit' objects by 'x$msfit', where 'x' is an object of class
     'pvclust'.

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

     'msfit' returns an object of class 'msfit'. It contains the
     following objects: 

       p: numeric vector of p-values. 'au' is AU (Approximately
          Unbiased) p-value computed by multiscale bootstrap
          resampling, which is more accurate than BP value (explained
          below) as unbiased p-value. 'bp' is BP (Bootstrap
          Probability) value, which is simple but tends to be unbiased
          when the absolute value of 'c' (a value in 'coef' vector,
          explained below) is large.

      se: numeric vector of estimated standard errors of p-values.

    coef: numeric vector related to geometric aspects of hypotheses.
          'v' is signed distance and 'c' is curvature of the boundary.

      df: numeric value of the degree of freedom in curve fitting.

     rss: residual sum of squares.

    pchi: p-value of chi-square test based on asymptotic theory.

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

     Ryota Suzuki ryota.suzuki@is.titech.ac.jp

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

     Shimodaira, H. (2004) "Approximately unbiased tests of regions
     using multistep-multiscale bootstrap resampling", _Annals of
     Statistics_, 32, 2616-2641.

     Shimodaira, H. (2002) "An approximately unbiased test of
     phylogenetic tree selection", _Systematic Biology_, 51, 492-508.

