predict                package:MEDME                R Documentation

_A_p_p_l_y_i_n_g _t_h_e _l_o_g_i_s_t_i_c _m_o_d_e_l _o_n _M_e_D_I_P _e_n_r_i_c_h_m_e_n_t _d_a_t_a

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

     This allows the probe-level determination of MeDIP smoothed data,
     as well as absolute and relative methylation levels (AMS and RMS
     respectively)

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

     predict(data, MEDMEfit, MEDMEextremes = c(1,32), wsize = 1000, wFunction='linear')

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

    data: An object of class MEDMEset

MEDMEfit: the model obtained from the MEDME.model function 

MEDMEextremes: vector; the background and saturation values as
          determined by the fitting of the model on the calibration
          data

   wsize: number; the size of the smoothing window, in bp 

wFunction: string; the type of weighting function, to choose among
          linear, exp, log or none 

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

     An object of class MEDMEset. The resulting smoothed data, the
     absolute  and relative methylation score (AMS and RMS) are saved
     in the smoothed, AMS and RMS slots, respectively.

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

     <URL:
     http://genome.cshlp.org/cgi/content/abstract/gr.080721.108v1>

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

     'smooth', 'CGcount', 'MEDME'

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

     data(testMEDMEset)
     ## just an example with the first 1000 probes
     testMEDMEset = smooth(data = testMEDMEset[1:1000, ])
     library(BSgenome.Hsapiens.UCSC.hg18)
     testMEDMEset = CGcount(data = testMEDMEset)
     MEDMEmodel = MEDME(data = testMEDMEset, sample = 1, CGcountThr = 1, figName = NULL)
     testMEDMEset = predict(data = testMEDMEset, MEDMEfit = MEDMEmodel, MEDMEextremes = c(1,32), wsize = 1000, wFunction='linear')

