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# lcvplot

## SYNOPSIS function g=lcvplot(alpha,varargin)

## DESCRIPTION ``` Computes and plots the Likelihood Cross-Validation score (LCV)
for local fits with different smoothing parameters.

The first argument to lcvplot(), alpha, should be a matrix with one
or two columns (first column = nearest neighbor component, second
column = constant component). Each row of this matrix is, in turn,
passed as the 'alpha' argument to lcv() (and locfit()). The results
are stored in a matrix, and LCV score ploted against the degrees of
freedom.```

## CROSS-REFERENCE INFORMATION This function calls: This function is called by:

## SOURCE CODE ```0001 function g=lcvplot(alpha,varargin)
0002 %
0003 % Computes and plots the Likelihood Cross-Validation score (LCV)
0004 % for local fits with different smoothing parameters.
0005 %
0006 % The first argument to lcvplot(), alpha, should be a matrix with one
0007 % or two columns (first column = nearest neighbor component, second
0008 % column = constant component). Each row of this matrix is, in turn,
0009 % passed as the 'alpha' argument to lcv() (and locfit()). The results
0010 % are stored in a matrix, and LCV score ploted against the degrees of
0011 % freedom.
0012
0013 k = size(alpha,1);
0014 z = zeros(k,4);
0015
0016 for i=1:k
0017   z(i,:) = lcv(varargin{:},'alpha',alpha(i,:));
0018 end;
0019
0020 plot(z(:,3),z(:,4));
0021 xlabel('Fitted DF');
0022 ylabel('LCV');
0023
0024 g = [alpha z];
0025 return;```

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