loessFit                package:limma                R Documentation

_F_a_s_t _S_i_m_p_l_e _L_o_e_s_s

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

     A fast version of locally weighted regression when there is only
     one x-variable and only the fitted values and residuals are
     required.

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

     loessFit(y, x, weights=NULL, span=0.3, bin=0.01/(2-is.null(weights)), iterations=4)

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

       y: numeric vector of response values.  Missing values are
          allowed.

       x: numeric vector of predictor values  Missing values are
          allowed.

 weights: numeric vector of non-negative weights.  Missing values are
          allowed.

    span: numeric parameter between 0 and 1 specifying proportion of
          data to be used in the local regression moving window. Larger
          numbers give smoother fits.

     bin: numeric value between 0 and 1 giving the proportion of the
          data which can be grouped in a single bin when doing local
          regression fit. 'bin=0' forces an exact local regression fit
          with no interpolation.

iterations: number of iterations of loess fit

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

     This function is a low-level equivalent to 'lowess' in the base
     library if 'weights' is null and to 'loess' in the modreg package
     otherwise. It is used by 'normalizeWithinArrays'. The parameters
     'span', 'cell' and 'iterations' have the same meaning as in
     'loess'. 'span' is equivalent to the argument 'f' to 'lowess' and
     'iterations' is equivalent to 'iter+1'. Unlike 'lowess' this
     function returns values in original rather than sorted order.

     The parameter 'bin' is equivalent to 'delta=bin*diff(range(x))' in
     a call to 'lowess' when 'weights=NULL' or to 'cell=bin/span' in a
     call to 'loess' when 'weights' are given.

     The treatment of missing values is analogous to 'na.exclude'.

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

     A list with components 

  fitted: numeric vector of same length as 'y' giving the loess fit

residuals: numeric vector of same length as 'x' giving residuals from
          the fit

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

     Gordon Smyth

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

     An overview of LIMMA functions for normalization is given in
     4.Normalization.

     See also 'lowess' and 'loess' in the stats package.

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

     y <- rnorm(1000)
     x <- rnorm(1000)
     w <- rep(1,1000)
     # The following are equivalent apart from execution time
     system.time(fit <- loessFit(y,x)$fitted)
     system.time(fit <- loessFit(y,x,w)$fitted)
     system.time(fit <- fitted(loess(y~x,weights=w,span=0.3,family="symmetric",iterations=4)))
     # Similar but with sorted x-values
     system.time(fit <- lowess(x,y,f=0.3)$y)

