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<h1>locfit
</h1>

<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../../../up.png"></a></h2>
<div class="box"><strong>Smoothing noisy data using Local Regression and Likelihood.</strong></div>

<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../../../up.png"></a></h2>
<div class="box"><strong>function fit=locfit(varargin) </strong></div>

<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../../../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Smoothing noisy data using Local Regression and Likelihood.

 arguments still to add: dc maxit

  Usage: fit = locfit(x,y)   % local regression fit of x and y.
         fit = locfit(x)     % density estimation of x.

  Smoothing with locfit is a two-step procedure. The locfit()
  function evaluates the local regression smooth at a set of points
  (can be specified through an evaluation structure). Then, use
  the predict() function to interpolate this fit to other points.

  Additional arguments to locfit() are specified as 'name',value pairs, e.g.:
  locfit( x, 'alpha',[0.7,1.5] , 'family','rate' , 'ev','grid' , 'mg',100 ); 


  Data-related inputs:

    x is a vector or matrix of the independent (or predictor) variables.
      Rows of x represent subjects, columns represent variables.
      Generally, local regression would be used with 1-4 independent
      variables. In higher dimensions, the curse-of-dimensionality,
      as well as the difficulty of visualizing higher dimensional
      surfaces, may limit usefulness.

    y is the column vector of the dependent (or response) variable.
      For density families, 'y' is omitted. 
 NOTE: x and y are the first two arguments. All other arguments require
        the 'name',value notation.

    'weights' Prior weights for observations (reciprocal of variance, or
           sample size). 
    'cens' Censoring indicators for hazard rate or censored regression.
           The coding is '1' (or 'TRUE') for a censored observation, and
           '0' (or 'FALSE') for uncensored observations. 
    'base' Baseline parameter estimate. If a baseline is provided,
           the local regression model is fitted as
                        Y_i = b_i + m(x_i) + epsilon_i,
           with Locfit estimating the m(x) term. For regression models,
           this effectively subtracts b_i from Y_i. The advantage of the
           'base' formulation is that it extends to likelihood
           regression models. 
    'scale' A scale to apply to each variable. This is especially
           important for multivariate fitting, where variables may be
           measured in non-comparable units. It is also used to specify
           the frequency for variables with the 'a' (angular) style.
     'sty' Character string (length d) of styles for each predictor variable.
           n denotes `normal'; a denotes angular (or periodic); l and r
           denotes one-sided left and right; c is conditionally parametric.
 

  Smoothing Parameters and Bandwidths:
  The bandwidth (or more accurately, half-width) of the smoothing window
  controls the amount of smoothing. Locfit allows specification of constant
  (fixed), nearest neighbor, certain locally adaptive variable bandwidths,
  and combinations of these. Also related to the smoothing parameter
  are the local polynmial degree and weight function.

    'nn' 'Nearest neighbor' smoothing parameter. Specifying 'nn',0.5
         means that the width of each smoothing neighborhood is chosen
         to cover 50% of the data.

     'h' A constant (or fixed) bandwidth parameter. For example, 'h',2
         means that the smoothing windows have constant half-width
         (or radius) 2. Note that h is applied after scaling.

   'pen' penalty parameter for adaptive smoothing. Needs to be used
         with care.

  'alpha' The old way of specifying smoothing parameters, as used in
         my book. alpha is equivalent to the vector [nn,h,pen].
         If multiple componenents are non-zero, the largest corresponding
         bandwidth is used. The default (if none of alpha,nn,h,pen
         are provided) is [0.7 0 0].

   'deg' Degree of local polynomial. Default: 2 (local quadratic).
         Degrees 0 to 3 are supported by almost all parts of the
         Locfit code. Higher degrees may work in some cases. 
 
  'kern' Weight function, default = 'tcub'. Other choices are
         'rect', 'trwt', 'tria', 'epan', 'bisq' and 'gauss'.
         Choices may be restricted when derivatives are
         required; e.g. for confidence bands and some bandwidth
         selectors. 
 
    'kt' Kernel type, 'sph' (default); 'prod'. In multivariate
         problems, 'prod' uses a simplified product model which
         speeds up computations. 
 
  'acri' Criterion for adaptive bandwidth selection.
 

  Derivative Estimation.
  Generally I recommend caution when using derivative estimation
  (and especially higher order derivative estimation) -- can you
  really estimate derivatives from noisy data? Any derivative
  estimate is inherently more dependent on an assumed smoothness
  (expressed through the bandwidth) than the data. Warnings aside...

  'deriv' Derivative estimation. 'deriv',1 specifies the first derivative
         (or more correctly, an estimate of the local slope is returned.
         'deriv',[1 1] specifies the second derivative. For bivariate fits
         'deriv',2 specifies the first partial derivative wrt x2.
         'deriv',[1 2] is mixed second-order derivative.
 
  Fitting family.
  'family' is used to specify the local likelihood family.
         Regression-type families are 'gaussian', 'binomial',
           'poisson', 'gamma' and 'geom'. If the family is preceded
           by a q (e.g. 'qgauss', or 'qpois') then quasi-likelihood is
           used; in particular, a dispersion estimate is computed.
           Preceding by an 'r' makes an attempt at robust (outlier-resistant)
           estimation. Combining q and r (e.g. 'family','qrpois') may
           work, if you're lucky.
         Density estimation-type families are 'dens', 'rate' and 'hazard'
           (hazard or failure rate). Note that `dens' scales the output
           to be a statistical density estimate (i.e. scaled to integrate
           to 1). 'rate' estimates the rate or intensity function (events
           per unit time, or events per unit area), which may be called
           density in some fields.
         The default family is 'qgauss' if a response (y argument) has been
         provided, and 'dens' if no response is given.
    'link' Link function for local likelihood fitting. Depending on the
           family, choices may be 'ident', 'log', 'logit',
           'inverse', 'sqrt' and 'arcsin'. 
 
  Evaluation structures.
    By default, locfit chooses a set of points, depending on the data
    and smoothing parameters, to evaluate at. This is controlled by
    the evaluation structure.
      'ev' Specify the evaluation structure. Default is 'tree'.
           Other choices include 'phull' (triangulation), 'grid' (a grid
           of points), 'data' (each data point), 'crossval' (data,
           but use leave-one-out cross validation), 'none' (no evaluation
           points, effectively producing the global parametric fit).
           Alternatively, a vector/matrix of evaluation points may be
           provided. 
           (kd trees not currently supported in mlocfit)
     'll' and 'ur' -- row vectors specifying the upper and lower limits
           for the bounding box used by the evaluation structure.
           They default to the data range. 
     'mg' For the 'grid' evaluation structure, 'mg' specifies the
           number of points on each margin. Default 10. Can be either a
           single number or vector. 
    'cut' Refinement parameter for adaptive partitions. Default 0.8;
           smaller values result in more refined partitions. 
    'maxk' Controls space assignment for evaluation structures. For the
           adaptive evaluation structures, it is impossible to be sure
           in advance how many vertices will be generated. If you get
           warnings about `Insufficient vertex space', Locfit's default
           assigment can be increased by increasing 'maxk'. The default
           is 'maxk','100'. 

    'xlim' For density estimation, Locfit allows the density to be
           supported on a bounded interval (or rectangle, in more than
           one dimension). The format should be [ll;ul] (ie, matrix with
           two rows, d columns) where ll is the lower left corner of
           the rectangle, and ur is the upper right corner.
           One-sided bounds, such as [0,infty), are not supported, but can be
           effectively specified by specifying a very large upper
           bound. 
 
      'module' either 'name' or {'name','/path/to/module',parameters}.
 
  Density Estimation
      'renorm',1  will attempt to renormalize the local likelihood
           density estimate so that it integrates to 1. The llde
           (specified by 'family','dens') is scaled to estimate the
           density, but since the estimation is pointwise, there is
           no guarantee that the resulting density integrates exactly
           to 1. Renormalization attempts to achieve this.

  The output of locfit() is a Matlab structure:

 fit.data.x (n*d)
 fit.data.y (n*1)
 fit.data.weights (n*1 or 1*1)
 fit.data.censor (n*1 or 1*1)
 fit.data.baseline (n*1 or 1*1)
 fit.data.style (string length d)
 fit.data.scales (1*d)
 fit.data.xlim (2*d)

 fit.evaluation_structure.type (string)
 fit.evaluation_structure.module.name (string)
 fit.evaluation_structure.module.directory (string)
 fit.evaluation_structure.module.parameters (string)
 fit.evaluation_structure.lower_left (numeric 1*d)
 fit.evaluation_structure.upper_right (numeric 1*d)
 fit.evaluation_structure.grid (numeric 1*d)
 fit.evaluation_structure.cut (numeric 1*d)
 fit.evaluation_structure.maxk
 fit.evaluation_structure.derivative

 fit.smoothing_parameters.alpha = (nn h pen) vector
 fit.smoothing_parameters.adaptive_criterion (string)
 fit.smoothing_parameters.degree (numeric)
 fit.smoothing_parameters.family (string)
 fit.smoothing_parameters.link (string)
 fit.smoothing_parameters.kernel (string)
 fit.smoothing_parameters.kernel_type (string)
 fit.smoothing_parameters.deren 
 fit.smoothing_parameters.deit
 fit.smoothing_parameters.demint
 fit.smoothing_parameters.debug

 fit.fit_points.evaluation_points (d*nv matrix)
 fit.fit_points.fitted_values (matrix, nv rows, many columns)
 fit.fit_points.evaluation_vectors.cell
 fit.fit_points.evaluation_vectors.splitvar
 fit.fit_points.evaluation_vectors.lo
 fit.fit_points.evaluation_vectors.hi
 fit.fit_points.fit_limits (d*2 matrix)
 fit.fit_points.family_link (numeric values)
 fit.fit_points.kappa (likelihood, degrees of freedom, etc)

 fit.parametric_component


  The OLD format:

    fit{1} = data.
    fit{2} = evaluation structure.
    fit{3} = smoothing parameter structure.
    fit{4}{1} = fit points matrix.
    fit{4}{2} = matrix of fitted values etc.
           Note that these are not back-transformed, and may have the
           parametric component removed.
           (exact content varies according to module).
    fit{4}{3} = various details of the evaluation points.
    fit{4}{4} = fit limits.
    fit{4}{5} = family,link.
    fit{5} = parametric component values.</pre></div>

<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../../../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../../../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../../../matlabicon.gif)">
<li><a href="../../../chronux_2_10/locfit/Book/fig10_1.html" class="code" title="">fig10_1</a>	Local Regression and Likelihood, Figure 10.1.</li><li><a href="../../../chronux_2_10/locfit/Book/fig11_1.html" class="code" title="">fig11_1</a>	Local Regression and Likelihood, Figure 11.1</li><li><a href="../../../chronux_2_10/locfit/Book/fig11_4.html" class="code" title="">fig11_4</a>	Local Regression and Likelihood, Figure 11.4.</li><li><a href="../../../chronux_2_10/locfit/Book/fig11_5.html" class="code" title="">fig11_5</a>	Local Regression and Likelihood, Figure 11.5.</li><li><a href="../../../chronux_2_10/locfit/Book/fig13_1.html" class="code" title="">fig13_1</a>	Local Regression and Likelihood, Figure 13.1</li><li><a href="../../../chronux_2_10/locfit/Book/fig1_1.html" class="code" title="">fig1_1</a>	Local Regression and Likelihood, Figure 1.1.</li><li><a href="../../../chronux_2_10/locfit/Book/fig2_2.html" class="code" title="">fig2_2</a>	Local Regression and Likelihood, Figure 2.2.</li><li><a href="../../../chronux_2_10/locfit/Book/fig2_3.html" class="code" title="">fig2_3</a>	Local Regression and Likelihood, Figure 2.3.</li><li><a href="../../../chronux_2_10/locfit/Book/fig2_4.html" class="code" title="">fig2_4</a>	Local Regression and Likelihood, Figure 2.4.</li><li><a href="../../../chronux_2_10/locfit/Book/fig2_5.html" class="code" title="">fig2_5</a>	Local Regression and Likelihood, Figure 2.5.</li><li><a href="../../../chronux_2_10/locfit/Book/fig2_6.html" class="code" title="">fig2_6</a>	Local Regression and Likelihood, Figure 2.6.</li><li><a href="../../../chronux_2_10/locfit/Book/fig3_1.html" class="code" title="">fig3_1</a>	Local Regression and Likelihood, Figure 3.1.</li><li><a href="../../../chronux_2_10/locfit/Book/fig4_1.html" class="code" title="">fig4_1</a>	Local Regression and Likelihood, Figure 4.1.</li><li><a href="../../../chronux_2_10/locfit/Book/fig4_2.html" class="code" title="">fig4_2</a>	Local Regression and Likelihood, Figure 4.2.</li><li><a href="../../../chronux_2_10/locfit/Book/fig4_3.html" class="code" title="">fig4_3</a>	Local Regression and Likelihood, Figure 4.3.</li><li><a href="../../../chronux_2_10/locfit/Book/fig5_1.html" class="code" title="">fig5_1</a>	Local Regression and Likelihood, Figure 5.1.</li><li><a href="../../../chronux_2_10/locfit/Book/fig5_2.html" class="code" title="">fig5_2</a>	Local Regression and Likelihood, Figure 5.2.</li><li><a href="../../../chronux_2_10/locfit/Book/fig5_3.html" class="code" title="">fig5_3</a>	Local Regression and Likelihood, Figure 5.3.</li><li><a href="../../../chronux_2_10/locfit/Book/fig5_4.html" class="code" title="">fig5_4</a>	Local Regression and Likelihood, Figure 5.4.</li><li><a href="../../../chronux_2_10/locfit/Book/fig5_5.html" class="code" title="">fig5_5</a>	Local Regression and Likelihood, Figure 5.5.</li><li><a href="../../../chronux_2_10/locfit/Book/fig5_6.html" class="code" title="">fig5_6</a>	Local Regression and Likelihood, Figure 5.6.</li><li><a href="../../../chronux_2_10/locfit/Book/fig6_1.html" class="code" title="">fig6_1</a>	Local Regression and Likelihood, Figure 6.1.</li><li><a href="../../../chronux_2_10/locfit/Book/fig6_2.html" class="code" title="">fig6_2</a>	Local Regression and Likelihood, Figure 6.2.</li><li><a href="../../../chronux_2_10/locfit/Book/fig6_3.html" class="code" title="">fig6_3</a>	Local Regression and Likelihood, Figure 6.3.</li><li><a href="../../../chronux_2_10/locfit/Book/fig6_4.html" class="code" title="">fig6_4</a>	Local Regression and Likelihood, Figure 6.4.</li><li><a href="../../../chronux_2_10/locfit/Book/fig6_5.html" class="code" title="">fig6_5</a>	Local Regression and Likelihood, Figure 6.5.</li><li><a href="../../../chronux_2_10/locfit/Book/fig6_6.html" class="code" title="">fig6_6</a>	Local Regression and Likelihood, Figure 6.6.</li><li><a href="../../../chronux_2_10/locfit/Book/fig6_7.html" class="code" title="">fig6_7</a>	Local Regression and Likelihood, Figure 6.6.</li><li><a href="../../../chronux_2_10/locfit/Book/fig7_1.html" class="code" title="">fig7_1</a>	Local Regression and Likelihood, Figure 7.1.</li><li><a href="../../../chronux_2_10/locfit/Book/fig7_2.html" class="code" title="">fig7_2</a>	Local Regression and Likelihood, Figure 7.2.</li><li><a href="../../../chronux_2_10/locfit/Book/fig7_3.html" class="code" title="">fig7_3</a>	Local Regression and Likelihood, Figure 7.3.</li><li><a href="../../../chronux_2_10/locfit/Book/fig7_4.html" class="code" title="">fig7_4</a>	Local Regression and Likelihood, Figure 7.4.</li><li><a href="../../../chronux_2_10/locfit/Book/fig7_5.html" class="code" title="">fig7_5</a>	Local Regression and Likelihood, Figure 7.5.</li><li><a href="../../../chronux_2_10/locfit/Book/fig7_6.html" class="code" title="">fig7_6</a>	Local Regression and Likelihood, Figure 7.6.</li><li><a href="../../../chronux_2_10/locfit/Book/fig8_1.html" class="code" title="">fig8_1</a>	Local Regression and Likelihood, Figure 8.1.</li><li><a href="../../../chronux_2_10/locfit/Book/fig8_2.html" class="code" title="">fig8_2</a>	Local Regression and Likelihood, Figure 8.2.</li><li><a href="../../../chronux_2_10/locfit/Book/fig8_3.html" class="code" title="">fig8_3</a>	Local Regression and Likelihood, Figure 8.3.</li><li><a href="../../../chronux_2_10/locfit/Book/fig9_2.html" class="code" title="">fig9_2</a>	Local Regression and Likelihood, Figure 9.2.</li><li><a href="../../../chronux_2_10/locfit/Neuro/lfex1.html" class="code" title="">lfex1</a>	Local Estimation a spike firing rate (in spikes per unit time).</li><li><a href="../../../chronux_2_10/locfit/Neuro/lfex2.html" class="code" title="">lfex2</a>	Model the success probability of successive trials of a monkey</li><li><a href="aic.html" class="code" title="function g=aic(varargin)">aic</a>	</li><li><a href="gcv.html" class="code" title="function g=gcv(varargin)">gcv</a>	</li><li><a href="hatmatrix.html" class="code" title="function z=hatmatrix(varargin)">hatmatrix</a>	</li><li><a href="kappa0.html" class="code" title="function kap=kappa0(x,y,varargin)">kappa0</a>	Compute the constants for `tube-formula' based simultaneous</li><li><a href="lcv.html" class="code" title="function g=lcv(varargin)">lcv</a>	</li><li><a href="lf_censor.html" class="code" title="function fit = lf_censor(x,y,cens,varargin)">lf_censor</a>	</li><li><a href="lfgui.html" class="code" title="function varargout = lfgui(varargin)">lfgui</a>	LFGUI M-file for lfgui.fig</li><li><a href="lfsmooth.html" class="code" title="function yhat=lfsmooth(varargin)">lfsmooth</a>	</li><li><a href="locfit_all.html" class="code" title="function out=locfit_all(varargin)">locfit_all</a>	Smoothing noisy data using Local Regression and Likelihood.</li><li><a href="scb.html" class="code" title="function z=scb(x,y,varargin)">scb</a>	Simultaneous Confidence Bands</li><li><a href="smooth_lf.html" class="code" title="function z=smooth_lf(x,y,varargin)">smooth_lf</a>	must (unlike R smooth.lf() function) give x and y.</li><li><a href="../../../chronux_2_10/spectral_analysis/helper/den_jack.html" class="code" title="function [m,ll,ul,llj,ulj]=den_jack(X,family,varargin)">den_jack</a>	Function to compute smooth estimates of the mean of x using locfit,</li></ul>
<!-- crossreference -->


<h2><a name="_source"></a>SOURCE CODE <a href="#_top"><img alt="^" border="0" src="../../../up.png"></a></h2>
<div class="fragment"><pre>0001 <a name="_sub0" href="#_subfunctions" class="code">function fit=locfit(varargin)</a>
0002 
0003 <span class="comment">% Smoothing noisy data using Local Regression and Likelihood.</span>
0004 <span class="comment">%</span>
0005 <span class="comment">% arguments still to add: dc maxit</span>
0006 <span class="comment">%</span>
0007 <span class="comment">%  Usage: fit = locfit(x,y)   % local regression fit of x and y.</span>
0008 <span class="comment">%         fit = locfit(x)     % density estimation of x.</span>
0009 <span class="comment">%</span>
0010 <span class="comment">%  Smoothing with locfit is a two-step procedure. The locfit()</span>
0011 <span class="comment">%  function evaluates the local regression smooth at a set of points</span>
0012 <span class="comment">%  (can be specified through an evaluation structure). Then, use</span>
0013 <span class="comment">%  the predict() function to interpolate this fit to other points.</span>
0014 <span class="comment">%</span>
0015 <span class="comment">%  Additional arguments to locfit() are specified as 'name',value pairs, e.g.:</span>
0016 <span class="comment">%  locfit( x, 'alpha',[0.7,1.5] , 'family','rate' , 'ev','grid' , 'mg',100 );</span>
0017 <span class="comment">%</span>
0018 <span class="comment">%</span>
0019 <span class="comment">%  Data-related inputs:</span>
0020 <span class="comment">%</span>
0021 <span class="comment">%    x is a vector or matrix of the independent (or predictor) variables.</span>
0022 <span class="comment">%      Rows of x represent subjects, columns represent variables.</span>
0023 <span class="comment">%      Generally, local regression would be used with 1-4 independent</span>
0024 <span class="comment">%      variables. In higher dimensions, the curse-of-dimensionality,</span>
0025 <span class="comment">%      as well as the difficulty of visualizing higher dimensional</span>
0026 <span class="comment">%      surfaces, may limit usefulness.</span>
0027 <span class="comment">%</span>
0028 <span class="comment">%    y is the column vector of the dependent (or response) variable.</span>
0029 <span class="comment">%      For density families, 'y' is omitted.</span>
0030 <span class="comment">% NOTE: x and y are the first two arguments. All other arguments require</span>
0031 <span class="comment">%        the 'name',value notation.</span>
0032 <span class="comment">%</span>
0033 <span class="comment">%    'weights' Prior weights for observations (reciprocal of variance, or</span>
0034 <span class="comment">%           sample size).</span>
0035 <span class="comment">%    'cens' Censoring indicators for hazard rate or censored regression.</span>
0036 <span class="comment">%           The coding is '1' (or 'TRUE') for a censored observation, and</span>
0037 <span class="comment">%           '0' (or 'FALSE') for uncensored observations.</span>
0038 <span class="comment">%    'base' Baseline parameter estimate. If a baseline is provided,</span>
0039 <span class="comment">%           the local regression model is fitted as</span>
0040 <span class="comment">%                        Y_i = b_i + m(x_i) + epsilon_i,</span>
0041 <span class="comment">%           with Locfit estimating the m(x) term. For regression models,</span>
0042 <span class="comment">%           this effectively subtracts b_i from Y_i. The advantage of the</span>
0043 <span class="comment">%           'base' formulation is that it extends to likelihood</span>
0044 <span class="comment">%           regression models.</span>
0045 <span class="comment">%    'scale' A scale to apply to each variable. This is especially</span>
0046 <span class="comment">%           important for multivariate fitting, where variables may be</span>
0047 <span class="comment">%           measured in non-comparable units. It is also used to specify</span>
0048 <span class="comment">%           the frequency for variables with the 'a' (angular) style.</span>
0049 <span class="comment">%     'sty' Character string (length d) of styles for each predictor variable.</span>
0050 <span class="comment">%           n denotes `normal'; a denotes angular (or periodic); l and r</span>
0051 <span class="comment">%           denotes one-sided left and right; c is conditionally parametric.</span>
0052 <span class="comment">%</span>
0053 <span class="comment">%</span>
0054 <span class="comment">%  Smoothing Parameters and Bandwidths:</span>
0055 <span class="comment">%  The bandwidth (or more accurately, half-width) of the smoothing window</span>
0056 <span class="comment">%  controls the amount of smoothing. Locfit allows specification of constant</span>
0057 <span class="comment">%  (fixed), nearest neighbor, certain locally adaptive variable bandwidths,</span>
0058 <span class="comment">%  and combinations of these. Also related to the smoothing parameter</span>
0059 <span class="comment">%  are the local polynmial degree and weight function.</span>
0060 <span class="comment">%</span>
0061 <span class="comment">%    'nn' 'Nearest neighbor' smoothing parameter. Specifying 'nn',0.5</span>
0062 <span class="comment">%         means that the width of each smoothing neighborhood is chosen</span>
0063 <span class="comment">%         to cover 50% of the data.</span>
0064 <span class="comment">%</span>
0065 <span class="comment">%     'h' A constant (or fixed) bandwidth parameter. For example, 'h',2</span>
0066 <span class="comment">%         means that the smoothing windows have constant half-width</span>
0067 <span class="comment">%         (or radius) 2. Note that h is applied after scaling.</span>
0068 <span class="comment">%</span>
0069 <span class="comment">%   'pen' penalty parameter for adaptive smoothing. Needs to be used</span>
0070 <span class="comment">%         with care.</span>
0071 <span class="comment">%</span>
0072 <span class="comment">%  'alpha' The old way of specifying smoothing parameters, as used in</span>
0073 <span class="comment">%         my book. alpha is equivalent to the vector [nn,h,pen].</span>
0074 <span class="comment">%         If multiple componenents are non-zero, the largest corresponding</span>
0075 <span class="comment">%         bandwidth is used. The default (if none of alpha,nn,h,pen</span>
0076 <span class="comment">%         are provided) is [0.7 0 0].</span>
0077 <span class="comment">%</span>
0078 <span class="comment">%   'deg' Degree of local polynomial. Default: 2 (local quadratic).</span>
0079 <span class="comment">%         Degrees 0 to 3 are supported by almost all parts of the</span>
0080 <span class="comment">%         Locfit code. Higher degrees may work in some cases.</span>
0081 <span class="comment">%</span>
0082 <span class="comment">%  'kern' Weight function, default = 'tcub'. Other choices are</span>
0083 <span class="comment">%         'rect', 'trwt', 'tria', 'epan', 'bisq' and 'gauss'.</span>
0084 <span class="comment">%         Choices may be restricted when derivatives are</span>
0085 <span class="comment">%         required; e.g. for confidence bands and some bandwidth</span>
0086 <span class="comment">%         selectors.</span>
0087 <span class="comment">%</span>
0088 <span class="comment">%    'kt' Kernel type, 'sph' (default); 'prod'. In multivariate</span>
0089 <span class="comment">%         problems, 'prod' uses a simplified product model which</span>
0090 <span class="comment">%         speeds up computations.</span>
0091 <span class="comment">%</span>
0092 <span class="comment">%  'acri' Criterion for adaptive bandwidth selection.</span>
0093 <span class="comment">%</span>
0094 <span class="comment">%</span>
0095 <span class="comment">%  Derivative Estimation.</span>
0096 <span class="comment">%  Generally I recommend caution when using derivative estimation</span>
0097 <span class="comment">%  (and especially higher order derivative estimation) -- can you</span>
0098 <span class="comment">%  really estimate derivatives from noisy data? Any derivative</span>
0099 <span class="comment">%  estimate is inherently more dependent on an assumed smoothness</span>
0100 <span class="comment">%  (expressed through the bandwidth) than the data. Warnings aside...</span>
0101 <span class="comment">%</span>
0102 <span class="comment">%  'deriv' Derivative estimation. 'deriv',1 specifies the first derivative</span>
0103 <span class="comment">%         (or more correctly, an estimate of the local slope is returned.</span>
0104 <span class="comment">%         'deriv',[1 1] specifies the second derivative. For bivariate fits</span>
0105 <span class="comment">%         'deriv',2 specifies the first partial derivative wrt x2.</span>
0106 <span class="comment">%         'deriv',[1 2] is mixed second-order derivative.</span>
0107 <span class="comment">%</span>
0108 <span class="comment">%  Fitting family.</span>
0109 <span class="comment">%  'family' is used to specify the local likelihood family.</span>
0110 <span class="comment">%         Regression-type families are 'gaussian', 'binomial',</span>
0111 <span class="comment">%           'poisson', 'gamma' and 'geom'. If the family is preceded</span>
0112 <span class="comment">%           by a q (e.g. 'qgauss', or 'qpois') then quasi-likelihood is</span>
0113 <span class="comment">%           used; in particular, a dispersion estimate is computed.</span>
0114 <span class="comment">%           Preceding by an 'r' makes an attempt at robust (outlier-resistant)</span>
0115 <span class="comment">%           estimation. Combining q and r (e.g. 'family','qrpois') may</span>
0116 <span class="comment">%           work, if you're lucky.</span>
0117 <span class="comment">%         Density estimation-type families are 'dens', 'rate' and 'hazard'</span>
0118 <span class="comment">%           (hazard or failure rate). Note that `dens' scales the output</span>
0119 <span class="comment">%           to be a statistical density estimate (i.e. scaled to integrate</span>
0120 <span class="comment">%           to 1). 'rate' estimates the rate or intensity function (events</span>
0121 <span class="comment">%           per unit time, or events per unit area), which may be called</span>
0122 <span class="comment">%           density in some fields.</span>
0123 <span class="comment">%         The default family is 'qgauss' if a response (y argument) has been</span>
0124 <span class="comment">%         provided, and 'dens' if no response is given.</span>
0125 <span class="comment">%    'link' Link function for local likelihood fitting. Depending on the</span>
0126 <span class="comment">%           family, choices may be 'ident', 'log', 'logit',</span>
0127 <span class="comment">%           'inverse', 'sqrt' and 'arcsin'.</span>
0128 <span class="comment">%</span>
0129 <span class="comment">%  Evaluation structures.</span>
0130 <span class="comment">%    By default, locfit chooses a set of points, depending on the data</span>
0131 <span class="comment">%    and smoothing parameters, to evaluate at. This is controlled by</span>
0132 <span class="comment">%    the evaluation structure.</span>
0133 <span class="comment">%      'ev' Specify the evaluation structure. Default is 'tree'.</span>
0134 <span class="comment">%           Other choices include 'phull' (triangulation), 'grid' (a grid</span>
0135 <span class="comment">%           of points), 'data' (each data point), 'crossval' (data,</span>
0136 <span class="comment">%           but use leave-one-out cross validation), 'none' (no evaluation</span>
0137 <span class="comment">%           points, effectively producing the global parametric fit).</span>
0138 <span class="comment">%           Alternatively, a vector/matrix of evaluation points may be</span>
0139 <span class="comment">%           provided.</span>
0140 <span class="comment">%           (kd trees not currently supported in mlocfit)</span>
0141 <span class="comment">%     'll' and 'ur' -- row vectors specifying the upper and lower limits</span>
0142 <span class="comment">%           for the bounding box used by the evaluation structure.</span>
0143 <span class="comment">%           They default to the data range.</span>
0144 <span class="comment">%     'mg' For the 'grid' evaluation structure, 'mg' specifies the</span>
0145 <span class="comment">%           number of points on each margin. Default 10. Can be either a</span>
0146 <span class="comment">%           single number or vector.</span>
0147 <span class="comment">%    'cut' Refinement parameter for adaptive partitions. Default 0.8;</span>
0148 <span class="comment">%           smaller values result in more refined partitions.</span>
0149 <span class="comment">%    'maxk' Controls space assignment for evaluation structures. For the</span>
0150 <span class="comment">%           adaptive evaluation structures, it is impossible to be sure</span>
0151 <span class="comment">%           in advance how many vertices will be generated. If you get</span>
0152 <span class="comment">%           warnings about `Insufficient vertex space', Locfit's default</span>
0153 <span class="comment">%           assigment can be increased by increasing 'maxk'. The default</span>
0154 <span class="comment">%           is 'maxk','100'.</span>
0155 <span class="comment">%</span>
0156 <span class="comment">%    'xlim' For density estimation, Locfit allows the density to be</span>
0157 <span class="comment">%           supported on a bounded interval (or rectangle, in more than</span>
0158 <span class="comment">%           one dimension). The format should be [ll;ul] (ie, matrix with</span>
0159 <span class="comment">%           two rows, d columns) where ll is the lower left corner of</span>
0160 <span class="comment">%           the rectangle, and ur is the upper right corner.</span>
0161 <span class="comment">%           One-sided bounds, such as [0,infty), are not supported, but can be</span>
0162 <span class="comment">%           effectively specified by specifying a very large upper</span>
0163 <span class="comment">%           bound.</span>
0164 <span class="comment">%</span>
0165 <span class="comment">%      'module' either 'name' or {'name','/path/to/module',parameters}.</span>
0166 <span class="comment">%</span>
0167 <span class="comment">%  Density Estimation</span>
0168 <span class="comment">%      'renorm',1  will attempt to renormalize the local likelihood</span>
0169 <span class="comment">%           density estimate so that it integrates to 1. The llde</span>
0170 <span class="comment">%           (specified by 'family','dens') is scaled to estimate the</span>
0171 <span class="comment">%           density, but since the estimation is pointwise, there is</span>
0172 <span class="comment">%           no guarantee that the resulting density integrates exactly</span>
0173 <span class="comment">%           to 1. Renormalization attempts to achieve this.</span>
0174 <span class="comment">%</span>
0175 <span class="comment">%  The output of locfit() is a Matlab structure:</span>
0176 <span class="comment">%</span>
0177 <span class="comment">% fit.data.x (n*d)</span>
0178 <span class="comment">% fit.data.y (n*1)</span>
0179 <span class="comment">% fit.data.weights (n*1 or 1*1)</span>
0180 <span class="comment">% fit.data.censor (n*1 or 1*1)</span>
0181 <span class="comment">% fit.data.baseline (n*1 or 1*1)</span>
0182 <span class="comment">% fit.data.style (string length d)</span>
0183 <span class="comment">% fit.data.scales (1*d)</span>
0184 <span class="comment">% fit.data.xlim (2*d)</span>
0185 <span class="comment">%</span>
0186 <span class="comment">% fit.evaluation_structure.type (string)</span>
0187 <span class="comment">% fit.evaluation_structure.module.name (string)</span>
0188 <span class="comment">% fit.evaluation_structure.module.directory (string)</span>
0189 <span class="comment">% fit.evaluation_structure.module.parameters (string)</span>
0190 <span class="comment">% fit.evaluation_structure.lower_left (numeric 1*d)</span>
0191 <span class="comment">% fit.evaluation_structure.upper_right (numeric 1*d)</span>
0192 <span class="comment">% fit.evaluation_structure.grid (numeric 1*d)</span>
0193 <span class="comment">% fit.evaluation_structure.cut (numeric 1*d)</span>
0194 <span class="comment">% fit.evaluation_structure.maxk</span>
0195 <span class="comment">% fit.evaluation_structure.derivative</span>
0196 <span class="comment">%</span>
0197 <span class="comment">% fit.smoothing_parameters.alpha = (nn h pen) vector</span>
0198 <span class="comment">% fit.smoothing_parameters.adaptive_criterion (string)</span>
0199 <span class="comment">% fit.smoothing_parameters.degree (numeric)</span>
0200 <span class="comment">% fit.smoothing_parameters.family (string)</span>
0201 <span class="comment">% fit.smoothing_parameters.link (string)</span>
0202 <span class="comment">% fit.smoothing_parameters.kernel (string)</span>
0203 <span class="comment">% fit.smoothing_parameters.kernel_type (string)</span>
0204 <span class="comment">% fit.smoothing_parameters.deren</span>
0205 <span class="comment">% fit.smoothing_parameters.deit</span>
0206 <span class="comment">% fit.smoothing_parameters.demint</span>
0207 <span class="comment">% fit.smoothing_parameters.debug</span>
0208 <span class="comment">%</span>
0209 <span class="comment">% fit.fit_points.evaluation_points (d*nv matrix)</span>
0210 <span class="comment">% fit.fit_points.fitted_values (matrix, nv rows, many columns)</span>
0211 <span class="comment">% fit.fit_points.evaluation_vectors.cell</span>
0212 <span class="comment">% fit.fit_points.evaluation_vectors.splitvar</span>
0213 <span class="comment">% fit.fit_points.evaluation_vectors.lo</span>
0214 <span class="comment">% fit.fit_points.evaluation_vectors.hi</span>
0215 <span class="comment">% fit.fit_points.fit_limits (d*2 matrix)</span>
0216 <span class="comment">% fit.fit_points.family_link (numeric values)</span>
0217 <span class="comment">% fit.fit_points.kappa (likelihood, degrees of freedom, etc)</span>
0218 <span class="comment">%</span>
0219 <span class="comment">% fit.parametric_component</span>
0220 <span class="comment">%</span>
0221 <span class="comment">%</span>
0222 <span class="comment">%  The OLD format:</span>
0223 <span class="comment">%</span>
0224 <span class="comment">%    fit{1} = data.</span>
0225 <span class="comment">%    fit{2} = evaluation structure.</span>
0226 <span class="comment">%    fit{3} = smoothing parameter structure.</span>
0227 <span class="comment">%    fit{4}{1} = fit points matrix.</span>
0228 <span class="comment">%    fit{4}{2} = matrix of fitted values etc.</span>
0229 <span class="comment">%           Note that these are not back-transformed, and may have the</span>
0230 <span class="comment">%           parametric component removed.</span>
0231 <span class="comment">%           (exact content varies according to module).</span>
0232 <span class="comment">%    fit{4}{3} = various details of the evaluation points.</span>
0233 <span class="comment">%    fit{4}{4} = fit limits.</span>
0234 <span class="comment">%    fit{4}{5} = family,link.</span>
0235 <span class="comment">%    fit{5} = parametric component values.</span>
0236 <span class="comment">%</span>
0237 
0238 
0239 
0240 <span class="comment">% Minimal input validation</span>
0241 <span class="keyword">if</span> nargin &lt; 1
0242    error( <span class="string">'At least one input argument required'</span> );
0243 <span class="keyword">end</span>
0244 
0245 xdata = double(varargin{1});
0246 d = size(xdata,2);
0247 n = size(xdata,1);
0248 <span class="keyword">if</span> ((nargin&gt;1) &amp;&amp; (~ischar(varargin{2})))
0249   ydata = double(varargin{2});
0250   <span class="keyword">if</span> (any(size(ydata) ~= [n 1])); error(<span class="string">'y must be n*1 column vector'</span>); <span class="keyword">end</span>;
0251   family = <span class="string">'qgauss'</span>;
0252   na = 3;
0253 <span class="keyword">else</span>
0254   ydata = 0;
0255   family = <span class="string">'density'</span>;
0256   na = 2;
0257 <span class="keyword">end</span>;
0258 <span class="keyword">if</span> mod(nargin-na,2)==0
0259   error( <span class="string">'All arguments other than x, y must be name,value pairs'</span> );
0260 <span class="keyword">end</span>
0261 
0262 
0263 wdata = ones(n,1);
0264 cdata = 0;
0265 base  = 0;
0266 style = <span class="string">'n'</span>;
0267 scale = 1;
0268 xl = zeros(2,d);
0269 
0270 alpha = [0 0 0];
0271 deg = 2;
0272 link = <span class="string">'default'</span>;
0273 acri = <span class="string">'none'</span>;
0274 kern = <span class="string">'tcub'</span>;
0275 kt = <span class="string">'sph'</span>;
0276 deren = 0;
0277 deit  = <span class="string">'default'</span>;
0278 demint= 20;
0279 debug = 0;
0280 
0281 ev = <span class="string">'tree'</span>;
0282 ll = zeros(1,d);
0283 ur = zeros(1,d);
0284 mg = 10;
0285 maxk = 100;
0286 deriv=0;
0287 cut = 0.8;
0288 mdl = struct(<span class="string">'name'</span>,<span class="string">'std'</span>, <span class="string">'directory'</span>,<span class="string">''</span>, <span class="string">'parameters'</span>,0 );
0289 
0290 <span class="keyword">while</span> na &lt; length(varargin)
0291     inc = 0;
0292     <span class="keyword">if</span> (varargin{na}==<span class="string">'y'</span>)
0293         ydata = double(varargin{na+1});
0294         family = <span class="string">'qgauss'</span>;
0295         inc = 2;
0296         <span class="keyword">if</span> (any(size(ydata) ~= [n 1])); error(<span class="string">'y must be n*1 column vector'</span>); <span class="keyword">end</span>;
0297     <span class="keyword">end</span>
0298     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'weights'</span>))
0299         wdata = double(varargin{na+1});
0300         inc = 2;
0301         <span class="keyword">if</span> (any(size(wdata) ~= [n 1])); error(<span class="string">'weights must be n*1 column vector'</span>); <span class="keyword">end</span>;
0302     <span class="keyword">end</span>
0303     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'cens'</span>))
0304         cdata = double(varargin{na+1});
0305         inc = 2;
0306         <span class="keyword">if</span> (any(size(cdata) ~= [n 1])); error(<span class="string">'cens must be n*1 column vector'</span>); <span class="keyword">end</span>;
0307     <span class="keyword">end</span>
0308     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'base'</span>)) <span class="comment">% numeric vector, n*1 or 1*1.</span>
0309         base = double(varargin{na+1});
0310         <span class="keyword">if</span> (length(base)==1); base = base*ones(n,1); <span class="keyword">end</span>;
0311         inc = 2;
0312     <span class="keyword">end</span>
0313     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'style'</span>)) <span class="comment">% character string of length d.</span>
0314         style = varargin{na+1};
0315         inc = 2;
0316     <span class="keyword">end</span>;
0317     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'scale'</span>)) <span class="comment">% row vector, length 1 or d.</span>
0318         scale = varargin{na+1};
0319         <span class="keyword">if</span> (scale==0)
0320           scale = zeros(1,d);
0321           <span class="keyword">for</span> i=1:d
0322             scale(i) = sqrt(var(xdata(:,i)));
0323           <span class="keyword">end</span>;
0324         <span class="keyword">end</span>;
0325         inc = 2;
0326     <span class="keyword">end</span>;
0327     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'xlim'</span>)) <span class="comment">% 2*d numeric matrix.</span>
0328         xl = varargin{na+1};
0329         inc = 2;
0330     <span class="keyword">end</span>
0331     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'alpha'</span>)) <span class="comment">% row vector of length 1, 2 or 3.</span>
0332         alpha = [varargin{na+1} 0 0 0];
0333         alpha = alpha(1:3);
0334         inc = 2;
0335     <span class="keyword">end</span>
0336     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'nn'</span>)) <span class="comment">% scalar</span>
0337         alpha(1) = varargin{na+1};
0338         inc = 2;
0339     <span class="keyword">end</span>
0340     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'h'</span>)) <span class="comment">% scalar</span>
0341         alpha(2) = varargin{na+1};
0342         inc = 2;
0343     <span class="keyword">end</span>;
0344     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'pen'</span>)) <span class="comment">% scalar</span>
0345         alpha(3) = varargin{na+1};
0346         inc = 2;
0347     <span class="keyword">end</span>;
0348     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'acri'</span>)) <span class="comment">% string</span>
0349         acri = varargin{na+1};
0350         inc = 2;
0351     <span class="keyword">end</span>
0352     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'deg'</span>)) <span class="comment">% positive integer.</span>
0353         deg = varargin{na+1};
0354         inc = 2;
0355     <span class="keyword">end</span>;
0356     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'family'</span>)) <span class="comment">% character string.</span>
0357         family = varargin{na+1};
0358         inc = 2;
0359     <span class="keyword">end</span>;
0360     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'link'</span>)) <span class="comment">% character string.</span>
0361         link = varargin{na+1};
0362         inc = 2;
0363     <span class="keyword">end</span>;
0364     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'kern'</span>)) <span class="comment">% character string.</span>
0365         kern = varargin{na+1};
0366         inc = 2;
0367     <span class="keyword">end</span>;
0368     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'kt'</span>)) <span class="comment">% character string.</span>
0369         kt = varargin{na+1};
0370         inc = 2;
0371     <span class="keyword">end</span>;
0372     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'ev'</span>)) <span class="comment">% char. string, or matrix with d columns.</span>
0373         ev = varargin{na+1};
0374         <span class="keyword">if</span> (isnumeric(ev)); ev = ev'; <span class="keyword">end</span>;
0375         inc = 2;
0376     <span class="keyword">end</span>;
0377     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'ll'</span>)) <span class="comment">% row vector of length d.</span>
0378         ll = varargin{na+1};
0379         inc = 2;
0380     <span class="keyword">end</span>;
0381     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'ur'</span>)) <span class="comment">% row vector of length d.</span>
0382         ur = varargin{na+1};
0383         inc = 2;
0384     <span class="keyword">end</span>;
0385     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'mg'</span>)) <span class="comment">% row vector of length d.</span>
0386         mg = varargin{na+1};
0387         inc = 2;
0388     <span class="keyword">end</span>;
0389     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'cut'</span>)) <span class="comment">% positive scalar.</span>
0390         cut = varargin{na+1};
0391         inc = 2;
0392     <span class="keyword">end</span>;
0393     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'module'</span>)) <span class="comment">% string.</span>
0394         mdl = struct(<span class="string">'name'</span>,varargin{na+1}, <span class="string">'directory'</span>,<span class="string">''</span>, <span class="string">'parameters'</span>,0 );
0395         inc = 2;
0396     <span class="keyword">end</span>;
0397     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'maxk'</span>)) <span class="comment">% positive integer.</span>
0398         maxk = varargin{na+1};
0399         inc = 2;
0400     <span class="keyword">end</span>;
0401     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'deriv'</span>)) <span class="comment">% numeric row vector, up to deg elements.</span>
0402         deriv = varargin{na+1};
0403         inc = 2;
0404     <span class="keyword">end</span>;
0405     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'renorm'</span>)) <span class="comment">% density renormalization.</span>
0406         deren = varargin{na+1};
0407         inc = 2;
0408     <span class="keyword">end</span>;
0409     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'itype'</span>)) <span class="comment">% density - integration type.</span>
0410         deit = varargin{na+1};
0411         inc = 2;
0412     <span class="keyword">end</span>;
0413     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'mint'</span>)) <span class="comment">% density - # of integration points.</span>
0414         demint = varargin{na+1};
0415         inc = 2;
0416     <span class="keyword">end</span>;
0417     <span class="keyword">if</span> (strcmp(varargin{na},<span class="string">'debug'</span>)) <span class="comment">% debug level.</span>
0418         debug = varargin{na+1};
0419         inc = 2;
0420     <span class="keyword">end</span>;
0421     <span class="keyword">if</span> (inc==0)
0422       disp(varargin{na});
0423       error(<span class="string">'Unknown Input Argument.'</span>);
0424     <span class="keyword">end</span>;
0425     na=na+inc;
0426 <span class="keyword">end</span>
0427 
0428 
0429 fit.data.x = xdata;
0430 fit.data.y = ydata;
0431 fit.data.weights = wdata;
0432 fit.data.censor = cdata;
0433 fit.data.baseline = base;
0434 fit.data.style = style;
0435 fit.data.scales = scale;
0436 fit.data.xlim = xl;
0437 
0438 fit.evaluation_structure.type = ev;
0439 fit.evaluation_structure.module = mdl;
0440 fit.evaluation_structure.lower_left = ll;
0441 fit.evaluation_structure.upper_right = ur;
0442 fit.evaluation_structure.grid = mg;
0443 fit.evaluation_structure.cut = cut;
0444 fit.evaluation_structure.maxk = maxk;
0445 fit.evaluation_structure.derivative = deriv;
0446 
0447 <span class="keyword">if</span> (alpha==0); alpha = [0.7 0 0]; <span class="keyword">end</span>;
0448 
0449 fit.smoothing_parameters.alpha = alpha;
0450 fit.smoothing_parameters.adaptive_criterion = acri;
0451 fit.smoothing_parameters.degree = deg;
0452 fit.smoothing_parameters.family = family;
0453 fit.smoothing_parameters.link = link;
0454 fit.smoothing_parameters.kernel = kern;
0455 fit.smoothing_parameters.kernel_type = kt;
0456 fit.smoothing_parameters.deren = deren;
0457 fit.smoothing_parameters.deit = deit;
0458 fit.smoothing_parameters.demint = demint;
0459 fit.smoothing_parameters.debug = debug;
0460 
0461 [fpc pcomp] = mexlf(fit.data,fit.evaluation_structure,fit.smoothing_parameters);
0462 fit.fit_points = fpc;
0463 fit.parametric_component = pcomp;
0464 
0465 <span class="keyword">return</span>
0466 
0467 
0468</pre></div>
<hr><address>Generated on Fri 12-Aug-2011 11:36:15 by <strong><a href="http://www.artefact.tk/software/matlab/m2html/" target="_parent">m2html</a></strong> &copy; 2005</address>
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