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<html xmlns:mwsh="http://www.mathworks.com/namespace/mcode/v1/syntaxhighlight.dtd"> <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8"> <!--This HTML is auto-generated from an M-file.To make changes, update the M-file and republish this document. --> <title>jknifedemo</title> <meta name="generator" content="MATLAB 7.0.4"> <meta name="date" content="2007-04-15"> <meta name="m-file" content="jknifedemo"><style>body { background-color: white; margin:10px;}h1 { color: #990000; font-size: x-large;}h2 { color: #990000; font-size: medium;}p.footer { text-align: right; font-size: xx-small; font-weight: lighter; font-style: italic; color: gray;}pre.codeinput { margin-left: 30px;}span.keyword {color: #0000FF}span.comment {color: #228B22}span.string {color: #A020F0}span.untermstring {color: #B20000}span.syscmd {color: #B28C00}pre.showbuttons { margin-left: 30px; border: solid black 2px; padding: 4px; background: #EBEFF3;}pre.codeoutput { color: gray; font-style: italic;}pre.error { color: red;}/* Make the text shrink to fit narrow windows, but not stretch too far in wide windows. On Gecko-based browsers, the shrink-to-fit doesn't work. */ p,h1,h2,div { /* for MATLAB's browser */ width: 600px; /* for Mozilla, but the "width" tag overrides it anyway */ max-width: 600px; /* for IE */ width:expression(document.body.clientWidth > 620 ? "600px": "auto" );} </style></head> <body><pre class="codeinput"><span class="comment">% JKNIFEDEMO Demonstration of JKNIFE and JSTATS</span><span class="comment">% EXAMPLE : jknife_demo (Oh, the FEX code metrics..)</span><span class="comment">% NOTES : Function HISTF needs to be downloaded from FEX</span><span class="comment">% AUTHOR : Dimitri Shvorob, dimitri.shvorob@vanderbilt.edu, 4/15/07</span><span class="comment">% Simple regression</span>n = 100;x = rand(n,1);i = ones(n,1);a = 3;b = 5;y = a + b*x + randn(n,1);<span class="comment">% Jackknife calculations</span>disp(<span class="string">'A. "fun" with vector output (OLS estimates of regression intercept and slope)'</span>)ls = jknife(<span class="string">'olsvector'</span>,[y i x]);disp(<span class="string">'Mean and variance estimates'</span>)[m,v] = jstats(ls) <span class="comment">%#ok</span>disp(<span class="string">'B. "fun" with structure output (OLS estimates of regression intercept and slope)'</span>)ls = jknife(<span class="string">'olsstructure'</span>,[y i x]);disp(<span class="string">'Mean and variance estimates'</span>)[m,v] = jstats(ls,<span class="string">'b'</span>) <span class="comment">%#ok</span><span class="comment">% Jackknife distributions of OLS estimates</span>ab = [ls.b]; <span class="comment">% note: may want to exclude actual-sample value</span>subplot(3,1,1)scatter(x,y,2)title([<span class="string">'Random sample: \ity = '</span> num2str(a) <span class="string">' + '</span> num2str(b) <span class="string">'x + \epsilon, \rmwhere \itx ~ U\rm[\it0,1\rm]\it,\epsilon ~ N\rm[\it0,1\rm], \iti = 1,..,100'</span>]);subplot(3,1,2)opt.dx = .01;opt.xmin = a - .5;opt.xmax = a + .5;opt.xmrk = a;histf(ab(1,:),opt)title([<span class="string">'OLS intercept estimate: jackknife distribution. (True value = \it'</span> num2str(a) <span class="string">'\rm)'</span>])subplot(3,1,3)opt.xmrk = b;opt.xmin = b - .5;opt.xmax = b + .5;histf(ab(2,:),opt)title([<span class="string">'OLS slope estimate: jackknife distribution. (True value = \it'</span> num2str(b) <span class="string">'\rm)'</span>])</pre><pre class="codeoutput">A. "fun" with vector output (OLS estimates of regression intercept and slope)Mean and variance estimatesm = 3.1155 4.6971v = 0.0449 0.1239B. "fun" with structure output (OLS estimates of regression intercept and slope)Mean and variance estimatesm = 3.1155 4.6971v = 0.0449 0.1239</pre><img vspace="5" hspace="5" src="jknifedemo_01.png"> <p class="footer"><br> Published with MATLAB® 7.0.4<br></p> <!--##### SOURCE BEGIN #####% JKNIFEDEMO Demonstration of JKNIFE and JSTATS
% EXAMPLE : jknife_demo (Oh, the FEX code metrics..)
% NOTES : Function HISTF needs to be downloaded from FEX
% AUTHOR : Dimitri Shvorob, dimitri.shvorob@vanderbilt.edu, 4/15/07
% Simple regression
n = 100;
x = rand(n,1);
i = ones(n,1);
a = 3;
b = 5;
y = a + b*x + randn(n,1);
% Jackknife calculations
disp('A. "fun" with vector output (OLS estimates of regression intercept and slope)')
ls = jknife('olsvector',[y i x]);
disp('Mean and variance estimates')
[m,v] = jstats(ls) %#ok
disp('B. "fun" with structure output (OLS estimates of regression intercept and slope)')
ls = jknife('olsstructure',[y i x]);
disp('Mean and variance estimates')
[m,v] = jstats(ls,'b') %#ok
% Jackknife distributions of OLS estimates
ab = [ls.b]; % note: may want to exclude actual-sample value
subplot(3,1,1)
scatter(x,y,2)
title(['Random sample: \ity = ' num2str(a) ' + ' num2str(b) 'x + \epsilon, \rmwhere \itx ~ U\rm[\it0,1\rm]\it,\epsilon ~ N\rm[\it0,1\rm], \iti = 1,..,100']);
subplot(3,1,2)
opt.dx = .01;
opt.xmin = a - .5;
opt.xmax = a + .5;
opt.xmrk = a;
histf(ab(1,:),opt)
title(['OLS intercept estimate: jackknife distribution. (True value = \it' num2str(a) '\rm)'])
subplot(3,1,3)
opt.xmrk = b;
opt.xmin = b - .5;
opt.xmax = b + .5;
histf(ab(2,:),opt)
title(['OLS slope estimate: jackknife distribution. (True value = \it' num2str(b) '\rm)'])
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