upperBoundNull             package:Ringo             R Documentation

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

     The data is assumed to arise from a mixture of two distributions,
     a symmetric null distribution with its mode close to zero, and an
     alternative distribution that is stochastically larger than the
     null. This function tries to pinpoint the minimum of data values
     that are more likely to arise from the alternative distribution,
     i.e. an upper bound for values following the null distribution.

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

     upperBoundNull(x, modeMethod = "shorth", limits = c(-1, 1), prob = 0.99, ...)

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

       x: numeric vector of data values

modeMethod: character string; which method to use for estimating the
          mode of the null distribution; see details

  limits: numeric of length 2; data values within this range are used
          for estimating the mode of the null distribution

    prob: quantile of the null distribution that will be used as an
          upper bound

     ...: additional arguments that are passed on to the function for
          mode estimation

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

     For estimating the mode of the null distribution, current options
     are

     "_s_h_o_r_t_h" the function 'shorth'

     "_h_a_l_f._r_a_n_g_e._m_o_d_e" the function 'half.range.mode'

     "_n_u_l_l" does not estimate the mode from the data, but sets it to 0

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

     a single numeric value which is the estimated upper bound for the
     null distribution.

_N_o_t_e:

     This way of estimating the null distribution is mentioned in the
     PhD thesis of Richard Bourgon.

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

     Joern Toedling toedling@ebi.ac.uk, based on suggestions by Richard
     Bourgon

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

     'shorth', 'half.range.mode'

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

       exDir <- system.file("exData",package="Ringo")
       load(file.path(exDir,"exampleProbeAnno.rda"))
       load(file.path(exDir,"exampleX.rda"))
       smoothX <- computeRunningMedians(exampleX, probeAnno=exProbeAnno,
            modColumn = "Cy5", allChr = "9", winHalfSize = 400)
       apply(exprs(smoothX), 2, upperBoundNull)

