monoSmu                 package:lumi                 R Documentation

_M_o_n_o_t_o_n_i_c _s_m_o_o_t_h _m_e_t_h_o_d

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

     Fit the monotonic-constraint spline curve

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

     monoSmu(x, y, newX = NULL, nSupport = min(200, length(x)), nKnots = 6, rotate = FALSE, ifPlot = FALSE, xlab = 'x', ylab = 'y', ...)

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

       x: a vector represents x values 

       y: a vector represents y values 

    newX: the new values to be transformed. If not provided, "x" will
          be used. 

nSupport: downsampled data points 

  nKnots: parameter used by 'monoSpline' 

  rotate: determine whether to rotate the axis with 45 degrees in
          clockwise, i.e., fit the curve in the MA-plot. 

  ifPlot: determine whether to plot intermediate results 

    xlab: the xlab of the plot 

    ylab: the ylab of the plot 

     ...: parameters used by 'supsmu' and 'plot' 

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

     function called by lumiN.rsn. The function first fits a monotonic
     spline between vector x and y, then transforms the vector newX
     based on the fitted spline. (After transformation the fitted
     spline is supposed to be a diagonal line, i.e., x=y)

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

     Return the transformed "newX" based on the smoothed curve

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

     Simon Lin, Pan Du

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

     Lin, S.M., Du, P., Kibbe, W.A.,  {\it Model-based
     Variance-stabilizing Transformation for Illumina Microarray Data},
     submitted

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

     'monoSpline'

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

     x <- sort(runif(200) * 100)
     y <- x + rnorm(200) * 10
     temp <- monoSmu(x, y, ifPlot=TRUE)

