histcutoffs            package:phyloarray            R Documentation

_H_i_s_t_o_g_r_a_m _c_u_t_o_f_f _v_a_l_u_e_s

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

     Calculate cutoff values for standard deviation/signal intensities
     for badspots. A bad spot has high sd/signal intensity. The cutoff
     value is based on a fivenum/boxplot analysis.

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

       histcutoffs(datalist, cutat=1.7)

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

datalist: An object of type 'phyloarray' for which the cutoffs should
          be calculated.

   cutat: A value giving how many times the length of the boxplot the
          cutoff should be set. This is the 'coef'-argument of the
          'boxplot.stats' function.

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

     For the function, the standard deviation/signal values are
     calculated and log-transformed. Using the log-transformed values,
     a boxplot is calculated, with the coefficient for outliers at
     'cutat' times the length of the box itself.

     Concerning 'cutat': If(!) the log-transformed values are normal
     distributed, setting 'cutat' to 1.0, means having a certainty of
     97.7250% of no false negatived. 1.35 has 99.3890% certainty, 1.7
     has 99.8650% certainty and 2.5 has 99.9968% certainty of no false
     negatives. It is stressed that these values are only true for
     normal distributions!

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

     The return value is the object datalist of class 'phyloarray', but
     with one attribute added, i.e. "cutoff"

_N_o_t_e:

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

     Kurt Sys (kurt.sys@advalvas.be)

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

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

     'Scandataraw' 'Phylodata'

     'init.data'

     'calcbackground'

     'boxplot.stats' 'boxplot' 'fivenum'

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

       # load data this-is-escaped-codenormal-bracket40bracket-normal, i.e. this-is-escaped-codenormal-bracket41bracket-normal
       data(Phylodata)

       # show some histograms
       hist(scans$Rsd[,1]/scans$R[,1], nclass=10000, xlim=c(0,5))
       hist(scans$Gsd[,1]/scans$G[,1], nclass=10000, xlim=c(0,1))

       # calculate cutoff values
       scans <- histcutoffs(scans, cutat=2.5)

       # the cutoff values
       attr(scans, "cutoff")

       # which gives the same as
       attributes(scans)$cutoff

