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74	<a href="a_correlate2d.html"><<prev file</a> \| <a href="c_timecorrelate.html">next file >></a>    <a href="a_timecorrelate.html" target="_TOP">view single page</a> \| <a href="./../../index.html" target="_TOP">view frames</a>    summary: fields \| <a href="#routine_summary">routine</a>    details: <a href="#routine_details">routine</a>
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81	<h1 class="directory"><a href="directory-overview.html">ToBeReviewed/STATISTICS/</a></h1>
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85	<dl>
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90
91
92
93	Same function as A_CORRELATE but accept array (until 4
94	dimension) for input and do the autocorrelation or the
95	autocovariance along the time dimension which must be the last
96	one of the input array.
97
98	This function computes the autocorrelation Px(L) or autocovariance
99	Rx(L) of a sample population X as a function of the lag (L).
100	</div>
101
102
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108
109	<div id="routine_summary">
110	<h2>Routine summary</h2>
111
112	<dl>
113
114	<dt><p><a href="#_timeauto_cov"><span class="result">result = </span>timeauto_cov(<span class="result">X, M, nT</span>, Double=<span class="result">Double</span>, zero2nan=<span class="result">zero2nan</span>)</a></p><dt>
115	<dd></dd>
116
117	<dt><p><a href="#_a_timecorrelate"><span class="result">result = </span>a_timecorrelate(<span class="result">X, Lag</span>, COVARIANCE=<span class="result">COVARIANCE</span>, DOUBLE=<span class="result">DOUBLE</span>)</a></p><dt>
118	<dd></dd>
119
120	</dl>
121	</div>
122
123
124	<div id="routine_details">
125
126
127	<div class="routine_details" id="_timeauto_cov">
128
129	<h2><a class="top" href="#container">top</a>timeauto_cov <span class="categories">
130	Statistics
131	</span></h2>
132
133	<p class="header">
134	<span class="result">result = </span>timeauto_cov(<span class="result"><a href="#_timeauto_cov_param_X">X</a>, <a href="#_timeauto_cov_param_M">M</a>, <a href="#_timeauto_cov_param_nT">nT</a></span>, <a href="#_timeauto_cov_keyword_Double">Double</a>=<span class="result">Double</span>, <a href="#_timeauto_cov_keyword_zero2nan">zero2nan</a>=<span class="result">zero2nan</span>)</p>
135
136	<div class="comments"></div>
137
138
139
140
141	<h3>Parameters</h3>
142
143
144	<h4 id="_timeauto_cov_param_X">X
145	<span class="attr">in</span>
146
147
148	<span class="attr">required</span>
149
150	<span class="attr">type:</span> <span class="value">array</span>
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152
153	</h4>
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155	<div class="comments">
156	An Array which last dimension is the time dimension so
157	size n.
158	</div>
159
160	<h4 id="_timeauto_cov_param_M">M
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169	</h4>
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171	<div class="comments">
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173	</div>
174
175	<h4 id="_timeauto_cov_param_nT">nT
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184	</h4>
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186	<div class="comments">
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188	</div>
189
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194
195	<h3>Keywords</h3>
196
197	<h4 id="_timeauto_cov_keyword_Double">Double
198
199
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206	</h4>
207
208	<div class="comments">
209	If set to a non-zero value, computations are done in
210	double precision arithmetic.
211	</div>
212
213	<h4 id="_timeauto_cov_keyword_zero2nan">zero2nan
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222	</h4>
223
224	<div class="comments">
225
226	</div>
227
228
229
230	<h3>Examples</h3><div class="preformat">
231
232	</div>
233	<h3>Version history</h3>
234
235	<h4>Version</h4><div class="preformat">
236	$Id: a_timecorrelate.pro 163 2006-08-29 12:59:46Z navarro $
237	</div>
238	<h4>History</h4><div class="preformat">
239
240	</div>
241
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256
257	<h3>Statistics</h3>
258	<table class="statistics">
259	<tr><td>McCabe cyclic</td><td> 8</td></tr>
260	<tr><td>McCabe essential</td><td> 1</td></tr>
261	<tr><td>McCabe modular design</td><td> 1</td></tr>
262	</table>
263
264
265	</div>
266
267
268	<div class="routine_details" id="_a_timecorrelate">
269
270	<h2><a class="top" href="#container">top</a>a_timecorrelate <span class="categories">
271	Statistics
272	</span></h2>
273
274	<p class="header">
275	<span class="result">result = </span>a_timecorrelate(<span class="result"><a href="#_a_timecorrelate_param_X">X</a>, <a href="#_a_timecorrelate_param_Lag">Lag</a></span>, <a href="#_a_timecorrelate_keyword_COVARIANCE">COVARIANCE</a>=<span class="result">COVARIANCE</span>, <a href="#_a_timecorrelate_keyword_DOUBLE">DOUBLE</a>=<span class="result">DOUBLE</span>)</p>
276
277	<div class="comments"></div>
278
279
280
281
282	<h3>Parameters</h3>
283
284
285	<h4 id="_a_timecorrelate_param_X">X
286	<span class="attr">in</span>
287
288
289	<span class="attr">required</span>
290
291	<span class="attr">type:</span> <span class="value">array</span>
292
293
294	</h4>
295
296	<div class="comments">
297	An Array which last dimension is the time dimension so
298	size n.
299	</div>
300
301	<h4 id="_a_timecorrelate_param_Lag">Lag
302	<span class="attr">in</span>
303
304
305	<span class="attr">required</span>
306
307	<span class="attr">type:</span> <span class="value">scalar or vector</span>
308
309
310	</h4>
311
312	<div class="comments">
313	A scalar or n-element vector, in the interval [-(n-2), (n-2)],
314	of type integer that specifies the absolute distance(s) between
315	indexed elements of X.
316	</div>
317
318
319
320
321
322
323	<h3>Keywords</h3>
324
325	<h4 id="_a_timecorrelate_keyword_COVARIANCE">COVARIANCE
326
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332
333
334	</h4>
335
336	<div class="comments">
337	If set to a non-zero value, the sample autocovariance
338	is computed.
339	</div>
340
341	<h4 id="_a_timecorrelate_keyword_DOUBLE">DOUBLE
342
343
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350	</h4>
351
352	<div class="comments">
353	If set to a non-zero value, computations are done in
354	double precision arithmetic.
355	</div>
356
357
358
359	<h3>Examples</h3><div class="preformat">
360	Define an n-element sample population.
361	x = [3.73, 3.67, 3.77, 3.83, 4.67, 5.87, 6.70, 6.97, 6.40, 5.57]
362
363	Compute the autocorrelation of X for LAG = -3, 0, 1, 3, 4, 8
364	lag = [-3, 0, 1, 3, 4, 8]
365	result = a_correlate(x, lag)
366
367	The result should be:
368	[0.0146185, 1.00000, 0.810879, 0.0146185, -0.325279, -0.151684]
369	</div>
370	<h3>Version history</h3>
371
372	<h4>Version</h4><div class="preformat">
373	$Id: a_timecorrelate.pro 163 2006-08-29 12:59:46Z navarro $
374	</div>
375	<h4>History</h4><div class="preformat">
376	24/2/2000 Sebastien Masson (smasson@lodyc.jussieu.fr)
377
378	Based on the A_CORRELATE procedure of IDL
379	INTRODUCTION TO STATISTICAL TIME SERIES
380	Wayne A. Fuller
381	ISBN 0-471-28715-6
382	</div>
383
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396
397
398
399	<h3>Statistics</h3>
400	<table class="statistics">
401	<tr><td>McCabe cyclic</td><td> 13</td></tr>
402	<tr><td>McCabe essential</td><td> 1</td></tr>
403	<tr><td>McCabe modular design</td><td> 1</td></tr>
404	</table>
405
406
407	</div>
408
409	</div>
410
411
412
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414
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