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  <title>Description of kappa0</title>
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<h1>kappa0
</h1>

<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../../../up.png"></a></h2>
<div class="box"><strong>Compute the constants for `tube-formula' based simultaneous</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 kap=kappa0(x,y,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"> Compute the constants for `tube-formula' based simultaneous
 confidence bands.

 Works for regression models only. Density estimation problems
 should be converted to counts, and use poisson regression
 'family','poisson'.

 Essentially, this is a front-end to locfit, and so all optional
 arguments to locfit (eg, smoothing parameters) can be provided.

 To compute (or plot) the confidence bands, provide the output
 of the kappa0() function as the 'kappa' argument to a
 predict() or lfband() call.


 Example:

 load ethanol;
 fit = locfit(E,NOx,'alpha',0.5)
 kap = kappa0(E,NOx,'alpha',0.5)  % give same arguments!
 lfplot(fit)
 lfband(fit,'kappa',kap)     % plot the simultaneous bands
 z = predict(fit,[0.6 0.7 0.8]','kappa',kap,'band','g')
 z{3}                        % evaluate the bands.</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)">
<li><a href="locfit.html" class="code" title="function fit=locfit(varargin)">locfit</a>	Smoothing noisy data using Local Regression and Likelihood.</li></ul>
This function is called by:
<ul style="list-style-image:url(../../../matlabicon.gif)">
<li><a href="scb.html" class="code" title="function z=scb(x,y,varargin)">scb</a>	Simultaneous Confidence Bands</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 kap=kappa0(x,y,varargin)</a>
0002 
0003 <span class="comment">% Compute the constants for `tube-formula' based simultaneous</span>
0004 <span class="comment">% confidence bands.</span>
0005 <span class="comment">%</span>
0006 <span class="comment">% Works for regression models only. Density estimation problems</span>
0007 <span class="comment">% should be converted to counts, and use poisson regression</span>
0008 <span class="comment">% 'family','poisson'.</span>
0009 <span class="comment">%</span>
0010 <span class="comment">% Essentially, this is a front-end to locfit, and so all optional</span>
0011 <span class="comment">% arguments to locfit (eg, smoothing parameters) can be provided.</span>
0012 <span class="comment">%</span>
0013 <span class="comment">% To compute (or plot) the confidence bands, provide the output</span>
0014 <span class="comment">% of the kappa0() function as the 'kappa' argument to a</span>
0015 <span class="comment">% predict() or lfband() call.</span>
0016 <span class="comment">%</span>
0017 <span class="comment">%</span>
0018 <span class="comment">% Example:</span>
0019 <span class="comment">%</span>
0020 <span class="comment">% load ethanol;</span>
0021 <span class="comment">% fit = locfit(E,NOx,'alpha',0.5)</span>
0022 <span class="comment">% kap = kappa0(E,NOx,'alpha',0.5)  % give same arguments!</span>
0023 <span class="comment">% lfplot(fit)</span>
0024 <span class="comment">% lfband(fit,'kappa',kap)     % plot the simultaneous bands</span>
0025 <span class="comment">% z = predict(fit,[0.6 0.7 0.8]','kappa',kap,'band','g')</span>
0026 <span class="comment">% z{3}                        % evaluate the bands.</span>
0027 
0028 fit = <a href="locfit.html" class="code" title="function fit=locfit(varargin)">locfit</a>(x,y,<span class="string">'module'</span>,<span class="string">'kappa'</span>,<span class="string">'ev'</span>,<span class="string">'grid'</span>,<span class="string">'mg'</span>,20,varargin{:});
0029 z = fit.fit_points.kappa;
0030 d = size(fit.data.x,2);
0031 kap = z(1:(d+1));
0032 
0033 <span class="keyword">return</span>;</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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