locfit                package:locfit                R Documentation

_L_o_c_a_l _R_e_g_r_e_s_s_i_o_n, _L_i_k_e_l_i_h_o_o_d _a_n_d _D_e_n_s_i_t_y _E_s_t_i_m_a_t_i_o_n.

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

     'locfit' is the model formula-based interface to the Locfit
     library for fitting local regression and likelihood models.

     'locfit' is implemented as a front-end to 'locfit.raw'. See that
     function for options to control smoothing parameters, fitting
     family and other aspects of the fit.

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

     locfit(formula, data, weights, cens, base, subset, geth, ..., lfproc)

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

 formula: Model Formula; e.g. 'y~x' for a regression model; '~x' for a
          density estimation model 

    data: Data Frame. 

 weights: Prior weights (or sample sizes) for individual observations.
          This is typically used where observations have unequal
          variance. 

    cens: Censoring indicator. '1' (or 'TRUE') denotes a censored
          observation. '0' (or 'FALSE') denotes uncensored. 

    base: Baseline for local fitting. For local regression models,
          specifying a 'base' is equivalent to using 'y-base' as the
          reponse. But 'base' also works for local likelihood. 

  subset: Subset observations in the data frame. 

    geth: Don't use. 

     ...: Other arguments to 'locfit.raw()' (or the 'lfproc'). 

  lfproc: A processing function to compute the local fit. Default is
          'locfit.raw()'. Other choices include 'locfit.robust()',
          'locfit.censor()' and 'locfit.quasi()'. 

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

     An object with class '"locfit"'. A standard set of methods for
     printing, ploting, etc. these objects is provided.

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

     Loader, C. (1999). Local Regression and Likelihood. Springer, New
     York.

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

     'locfit.raw'

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

     # fit and plot a univariate local regression
     data(ethanol)
     fit <- locfit(NOx~E,data=ethanol)
     plot(fit,get.data=TRUE)

     # a bivariate local regression with smaller smoothing parameter
     fit <- locfit(NOx~E+C, data=ethanol, scale=0, alpha=0.5)
     plot(fit)

     # density estimation
     data(geyser)
     fit <- locfit(~geyser, alpha=c(0.1,0.8))
     plot(fit,get.data=TRUE)

