loess                 package:modreg                 R Documentation

_L_o_c_a_l _P_o_l_y_n_o_m_i_a_l _R_e_g_r_e_s_s_i_o_n _F_i_t_t_i_n_g

_D_e_s_c_r_i_p_t_i_o_n:

     Fit a polynomial surface determined by one or more numerical
     predictors, using local fitting.

_U_s_a_g_e:

     loess(formula, data, weights, subset, na.action, model = FALSE,
           span = 0.75, enp.target, degree = 2,
           parametric = FALSE, drop.square = FALSE, normalize = TRUE,
           family = c("gaussian", "symmetric"),
           method = c("loess", "model.frame"),
           control = loess.control(...), ...)

_A_r_g_u_m_e_n_t_s:

 formula: a formula specifying the response and one or more numeric
          predictors (best specified via an interaction, but can also
          be specified additively).

    data: an optional data frame within which to look first for the
          response, predictors and weights.

 weights: optional weights for each case.

  subset: an optional specification of a subset of the data to be used.

na.action: the action to be taken with missing values in the response
          or predictors.  The default is to stop.

   model: should the model frame be returned?

    span: the parameter alpha which controls the degree of smoothing.

enp.target: an alternative way to specify `span', as the approximate
          equivalent number of parameters to be used.

  degree: the degree of the polynomials to be used, up to 2.

parametric: should any terms be fitted globally rather than locally? 
          Terms can be specified by name, number or as a logical vector
          of the same length as the number of predictors.

drop.square: for fits with more than one predictor and `degree=2',
          should the quadratic term (and cross-terms) be dropped for
          particular predictors?  Terms are specified in the same way
          as for `parametric'.

normalize: should the predictors be normalized to a common scale if
          there is more than one?  The normalization used is to set the
          10% trimmed standard deviation to one.  Set to false for
          spatial coordinate predictors and others know to be a common
          scale.

  family: if `"gaussian"' fitting is by least-squares, and if
          `"symmetric"' a re-descending M estimator is used with
          Tukey's biweight function.

  method: fit the model or just extract the model frame.

 control: control parameters: see `loess.control'.

     ...: control parameters can also be supplied directly.

_D_e_t_a_i_l_s:

     Fitting is done locally.  That is, for the fit at point x, the fit
     is made using points in a neighbourhood of x, weighted by their
     distance from x (with differences in `parametric' variables being
     ignored when computing the distance). The size of the
     neighbourhood is controlled by alpha (set by `span' or
     `enp.target').  For alpha < 1, the neighbourhood includes
     proportion alpha of the points, and these have tricubic weighting
     (proportional to (1 - (dist/maxdist)^3)^3. For alpha > 1, all
     points are used, with the `maximum distance' assumed to be alpha
     times the actual maximum distance.

     For the default family, fitting is by (weighted) least squares.
     For `family="symmetric"' a few iterations of an M-estimation
     procedure with Tukey's biweight are used. Be aware that as the
     initial value is the least-squares fit, this need not be a very
     resistant fit.

     It can be important to tune the control list to achieve acceptable
     speed. See loess.control for details.

_V_a_l_u_e:

     An object of class `"loess"'.

_N_o_t_e:

     As this is based on the `cloess' package available at `netlib', it
     is similar to but not identical to the `loess' function of S. In
     particular, conditioning is not implemented.

     The memory usage of this implementation of `loess' is roughly
     quadratic in the number of points, with 1000 points taking about
     10Mb.

_A_u_t_h_o_r(_s):

     B.D. Ripley, based on the `cloess' package of Cleveland, Grosse
     and Shyu.

_R_e_f_e_r_e_n_c_e_s:

     W.S. Cleveland, E. Grosse and W.M. Shyu (1992) Local regression
     models. Chapter 8 of Statistical Models in S eds J.M. Chambers and
     T.J. Hastie, Wadsworth & Brooks/Cole.

_S_e_e _A_l_s_o:

     `loess.control', `predict.loess'

_E_x_a_m_p_l_e_s:

     data(cars)
     cars.lo <- loess(dist ~ speed, cars)
     predict(cars.lo, data.frame(speed=seq(5, 30, 1)), se=TRUE)
     # to get extrapolation
     cars.lo2 <- loess(dist ~ speed, cars,
       control=loess.control(surface="direct"))
     predict(cars.lo2, data.frame(speed=seq(5, 30, 1)), se=TRUE)

