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  <title>Description of predict</title>
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  <meta name="description" content="Interpolate a fit produced by locfit().">
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<h1>predict
</h1>

<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../../../up.png"></a></h2>
<div class="box"><strong>Interpolate a fit produced by locfit().</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 [y, se] = predict(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"> Interpolate a fit produced by locfit().

 predict(fit)    produces the fitted values at locfit's selected points.
 predict(fit,x)  interpolates the fits to points specified by x.

 Input arguments:
   fit   The locfit() fit.
   x     Points to interpolate at. May be a matrix with d columns,
         or cell with d components (each a vector). In the former
         case, a fitted value is computed for each row of x.
         In the latter, the components of x are interpreted as
         grid margins.
         Can also specify 'data' (evaluate at data points);
         or 'fitp' (extract the fitted points).
  'band',value
         Type of standard errors to compute. Default is 'band','n', for none.
         Other choices are 'band','g' (use a global s to estimate the resiudal
         standard deviation, so standard errors are s*||l(x)||);
         'band','l' (use a local s(x), so std. errors are s(x)*||l(x)||);
         'band','p' (prediction errors, so s*sqrt(1+||l(x)||^2).
  'direct'
         Compute the local fit directly (rather than using local
         regression, at each point specified by the x argument.
  'kappa',vector
         Vector of constants for simultaneous confidence bands,
         computed by the kappa0() function.
  'level',value
         Coverage probability for confidence intervals and bands.
         Default is 0.95.

  Output is a vector of fitted values (if 'band','n'), or a cell
  with fitted value, standard error vectors, and matrix of lower
  and upper confidence limits.

  Note that for local likelihood fits, back-transformation is
  not performed, so that (e.g.) for Poisson regression with the
  log-link, the output estimates the log-mean, and its standard errors.
  Likewise, for density estimation, the output is log(density).

  Author: Catherine Loader.</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/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/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/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_6.html" class="code" title="">fig6_6</a>	Local Regression and Likelihood, Figure 6.6.</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/fig9_2.html" class="code" title="">fig9_2</a>	Local Regression and Likelihood, Figure 9.2.</li><li><a href="fitted.html" class="code" title="function y = fitted(fit)">fitted</a>	Fitted values from a locfit object.</li><li><a href="lfband.html" class="code" title="function lfband(fit,varargin)">lfband</a>	adds confidence bands around the plot of a locfit() fit.</li><li><a href="lfplot.html" class="code" title="function lfplot(varargin)">lfplot</a>	Plot (for one or two dimensions) a locfit() fit.</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="residuals.html" class="code" title="function y = residuals(fit,type)">residuals</a>	Residuals (or a few other things) from a locfit() fit.</li><li><a href="scb.html" class="code" title="function z=scb(x,y,varargin)">scb</a>	Simultaneous Confidence Bands</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 [y, se] = predict(varargin)</a>
0002 
0003 <span class="comment">% Interpolate a fit produced by locfit().</span>
0004 <span class="comment">%</span>
0005 <span class="comment">% predict(fit)    produces the fitted values at locfit's selected points.</span>
0006 <span class="comment">% predict(fit,x)  interpolates the fits to points specified by x.</span>
0007 <span class="comment">%</span>
0008 <span class="comment">% Input arguments:</span>
0009 <span class="comment">%   fit   The locfit() fit.</span>
0010 <span class="comment">%   x     Points to interpolate at. May be a matrix with d columns,</span>
0011 <span class="comment">%         or cell with d components (each a vector). In the former</span>
0012 <span class="comment">%         case, a fitted value is computed for each row of x.</span>
0013 <span class="comment">%         In the latter, the components of x are interpreted as</span>
0014 <span class="comment">%         grid margins.</span>
0015 <span class="comment">%         Can also specify 'data' (evaluate at data points);</span>
0016 <span class="comment">%         or 'fitp' (extract the fitted points).</span>
0017 <span class="comment">%  'band',value</span>
0018 <span class="comment">%         Type of standard errors to compute. Default is 'band','n', for none.</span>
0019 <span class="comment">%         Other choices are 'band','g' (use a global s to estimate the resiudal</span>
0020 <span class="comment">%         standard deviation, so standard errors are s*||l(x)||);</span>
0021 <span class="comment">%         'band','l' (use a local s(x), so std. errors are s(x)*||l(x)||);</span>
0022 <span class="comment">%         'band','p' (prediction errors, so s*sqrt(1+||l(x)||^2).</span>
0023 <span class="comment">%  'direct'</span>
0024 <span class="comment">%         Compute the local fit directly (rather than using local</span>
0025 <span class="comment">%         regression, at each point specified by the x argument.</span>
0026 <span class="comment">%  'kappa',vector</span>
0027 <span class="comment">%         Vector of constants for simultaneous confidence bands,</span>
0028 <span class="comment">%         computed by the kappa0() function.</span>
0029 <span class="comment">%  'level',value</span>
0030 <span class="comment">%         Coverage probability for confidence intervals and bands.</span>
0031 <span class="comment">%         Default is 0.95.</span>
0032 <span class="comment">%</span>
0033 <span class="comment">%  Output is a vector of fitted values (if 'band','n'), or a cell</span>
0034 <span class="comment">%  with fitted value, standard error vectors, and matrix of lower</span>
0035 <span class="comment">%  and upper confidence limits.</span>
0036 <span class="comment">%</span>
0037 <span class="comment">%  Note that for local likelihood fits, back-transformation is</span>
0038 <span class="comment">%  not performed, so that (e.g.) for Poisson regression with the</span>
0039 <span class="comment">%  log-link, the output estimates the log-mean, and its standard errors.</span>
0040 <span class="comment">%  Likewise, for density estimation, the output is log(density).</span>
0041 <span class="comment">%</span>
0042 <span class="comment">%  Author: Catherine Loader.</span>
0043 
0044 <span class="keyword">if</span> (nargin&lt;1)
0045     error(<span class="string">'predict requires fit argument'</span>);
0046 <span class="keyword">end</span>;
0047 
0048 fit = varargin{1};
0049 
0050 <span class="keyword">if</span> (nargin==1) x = <span class="string">'fitp'</span>; <span class="keyword">else</span> x = varargin{2}; <span class="keyword">end</span>;
0051 
0052 band = <span class="string">'n'</span>;
0053 what = <span class="string">'coef'</span>;
0054 rest = <span class="string">'none'</span>;
0055 dir  = 0;
0056 level = 0.95;
0057 d = size(fit.data.x,2);
0058 kap = [zeros(1,d) 1];
0059 
0060 na = 3;
0061 <span class="keyword">while</span> na &lt;= nargin
0062   inc = 0;
0063   <span class="keyword">if</span> strcmp(varargin{na},<span class="string">'band'</span>)
0064     band = varargin{na+1};
0065     inc = 2;
0066   <span class="keyword">end</span>;
0067   <span class="keyword">if</span> strcmp(varargin{na},<span class="string">'what'</span>)
0068     what = varargin{na+1};
0069     inc = 2;
0070   <span class="keyword">end</span>;
0071   <span class="keyword">if</span> strcmp(varargin{na},<span class="string">'restyp'</span>)
0072     rest = varargin{na+1};
0073     inc = 2;
0074   <span class="keyword">end</span>;
0075   <span class="keyword">if</span> strcmp(varargin{na},<span class="string">'direct'</span>)
0076     dir = 1;
0077     inc = 1;
0078   <span class="keyword">end</span>;
0079   <span class="keyword">if</span> strcmp(varargin{na},<span class="string">'kappa'</span>)
0080     kap = varargin{na+1};
0081     inc = 2;
0082   <span class="keyword">end</span>;
0083   <span class="keyword">if</span> strcmp(varargin{na},<span class="string">'level'</span>)
0084     level = varargin{na+1};
0085     inc = 2;
0086   <span class="keyword">end</span>;
0087   <span class="keyword">if</span> (inc == 0)
0088     disp(varargin{na});
0089     error(<span class="string">'Unknown argument'</span>);
0090   <span class="keyword">end</span>;
0091   na = na+inc;
0092 <span class="keyword">end</span>;
0093 
0094 [y se cb] = mexpp(x,fit,band,what,rest,dir,kap,level);
0095 <span class="keyword">if</span> (band==<span class="string">'n'</span>)
0096     y = y;
0097 <span class="keyword">else</span>
0098     y = {y se cb};
0099 <span class="keyword">end</span>;
0100 
0101 <span class="keyword">return</span>;</pre></div>
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