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| 3 | <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> |
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| 4 | <head> |
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| 5 | <title>a_timecorrelate.pro (SAXO Documentation Assistant)</title> |
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| 6 | </head> |
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| 45 | <table border="0" cellpadding="0" cellspacing="0" width="98%" bgcolor="#F0F0FF" valign="bottom"> |
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| 46 | <tr> |
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| 47 | <td width="10%"> |
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| 48 | <a href="a_correlate2d.html"><img src="./../../prev.gif" border="0" alt="Previous"></a></td> |
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| 49 | <td width="80%" align="center" valign="center"> |
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| 50 | <font size=-1><i>SAXO Documentation Assistant</i>: <a href="./../../home.html">Overview</a></font></td> |
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| 51 | <td width="10%" align="right"> |
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| 52 | <a href="c_timecorrelate.html"><img src="./../../next.gif" border="0" alt="Next"></a></td> |
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| 53 | </tr> |
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| 54 | </table> |
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| 55 | |
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| 56 | |
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| 57 | <h1><font size="-2">ToBeReviewed/STATISTICS/</font></h1> |
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| 58 | <h2>a_timecorrelate.pro</h2> |
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| 59 | |
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| 60 | <dl> |
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| 61 | </dl> |
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| 62 | |
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| 63 | |
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| 64 | |
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| 65 | |
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| 66 | |
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| 67 | Same function as A_CORRELATE but accept array (until 4 |
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| 68 | dimension) for input and do the autocorrelation or the |
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| 69 | autocovariance along the time dimension which must be the last |
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| 70 | one of the input array. |
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| 71 | |
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| 72 | This function computes the autocorrelation Px(L) or autocovariance |
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| 73 | Rx(L) of a sample population X as a function of the lag (L). |
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| 74 | |
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| 75 | |
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| 76 | |
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| 77 | <h2>Routine summary</h2> |
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| 78 | |
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| 79 | <dl> |
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| 80 | |
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| 81 | <dt><a href="#_timeauto_cov"><i>result = </i>timeauto_cov(<i>X, M, nT</i>, Double=<i>Double</i>, zero2nan=<i>zero2nan</i>)</a><dt> |
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| 82 | <dd><font size="-1"></font></dd> |
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| 83 | |
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| 84 | <dt><a href="#_a_timecorrelate"><i>result = </i>a_timecorrelate(<i>X, Lag</i>, COVARIANCE=<i>COVARIANCE</i>, DOUBLE=<i>DOUBLE</i>)</a><dt> |
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| 85 | <dd><font size="-1"></font></dd> |
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| 86 | |
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| 87 | </dl> |
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| 88 | |
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| 89 | <p> </p> |
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| 90 | |
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| 91 | |
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| 92 | |
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| 93 | <a name="#_timeauto_cov"></a> |
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| 94 | |
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| 95 | <h2>timeauto_cov <font size="-1" color="#006633"> |
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| 96 | Statistics |
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| 97 | </font></h2> |
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| 98 | |
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| 99 | <p><font face="Courier"><i>result = </i>timeauto_cov(<i><a href="#_timeauto_cov_keyword_X">X</a>, <a href="#_timeauto_cov_keyword_M">M</a>, <a href="#_timeauto_cov_keyword_nT">nT</a></i>, <a href="#_timeauto_cov_keyword_Double">Double</a>=<i>Double</i>, <a href="#_timeauto_cov_keyword_zero2nan">zero2nan</a>=<i>zero2nan</i>)</font></p> |
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| 100 | |
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| 101 | |
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| 102 | |
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| 103 | |
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| 104 | |
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| 105 | |
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| 106 | <h3>Parameters</h3> |
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| 107 | |
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| 108 | |
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| 109 | <a name="#_timeauto_cov_keyword_X"></a> |
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| 110 | <h4>X |
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| 111 | <font size="-1" color="#006633">in</font> |
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| 112 | |
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| 113 | |
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| 114 | <font size="-1" color="#006633">required</font> |
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| 115 | |
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| 116 | <font size="-1" color="#006633">type:</font> <font size="-1" color="#006633"><i>array</i></font> |
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| 117 | |
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| 118 | |
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| 119 | </h4> |
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| 120 | |
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| 121 | |
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| 122 | An Array which last dimension is the time dimension so |
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| 123 | size n. |
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| 124 | |
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| 125 | |
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| 126 | |
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| 127 | <a name="#_timeauto_cov_keyword_M"></a> |
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| 128 | <h4>M |
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| 129 | |
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| 130 | |
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| 131 | |
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| 132 | |
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| 133 | |
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| 135 | |
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| 136 | |
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| 137 | </h4> |
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| 138 | |
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| 139 | |
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| 140 | |
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| 141 | |
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| 142 | |
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| 143 | |
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| 144 | <a name="#_timeauto_cov_keyword_nT"></a> |
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| 145 | <h4>nT |
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| 146 | |
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| 147 | |
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| 148 | |
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| 149 | |
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| 150 | |
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| 151 | |
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| 152 | |
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| 153 | |
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| 154 | </h4> |
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| 155 | |
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| 156 | |
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| 157 | |
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| 159 | |
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| 162 | |
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| 163 | <h3>Keywords</h3> |
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| 164 | |
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| 165 | |
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| 166 | <a name="#_timeauto_cov_keyword_Double"></a> |
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| 167 | <h4>Double |
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| 168 | |
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| 169 | |
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| 170 | |
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| 171 | |
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| 174 | |
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| 175 | |
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| 176 | </h4> |
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| 177 | |
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| 178 | |
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| 179 | If set to a non-zero value, computations are done in |
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| 180 | double precision arithmetic. |
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| 181 | |
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| 182 | |
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| 183 | <a name="#_timeauto_cov_keyword_zero2nan"></a> |
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| 184 | <h4>zero2nan |
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| 185 | |
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| 186 | |
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| 187 | |
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| 188 | |
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| 189 | |
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| 191 | |
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| 192 | |
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| 193 | </h4> |
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| 194 | |
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| 195 | |
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| 197 | |
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| 198 | |
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| 199 | |
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| 200 | |
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| 201 | <h3>Examples</h3> |
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| 202 | |
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| 203 | |
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| 204 | <h3>Version history</h3> |
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| 205 | |
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| 206 | <h4>Version</h4> |
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[168] | 207 | $Id: a_timecorrelate.pro 163 2006-08-29 12:59:46Z navarro $ |
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[163] | 208 | |
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| 209 | <h4>History</h4> |
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| 210 | |
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| 227 | |
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| 228 | <font size="-3"><p> </p></font> |
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| 229 | <hr size="1" color="#CCCCCC"/> |
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| 230 | |
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| 231 | <a name="#_a_timecorrelate"></a> |
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| 232 | |
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| 233 | <h2>a_timecorrelate <font size="-1" color="#006633"> |
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| 234 | Statistics |
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| 235 | </font></h2> |
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| 236 | |
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| 237 | <p><font face="Courier"><i>result = </i>a_timecorrelate(<i><a href="#_a_timecorrelate_keyword_X">X</a>, <a href="#_a_timecorrelate_keyword_Lag">Lag</a></i>, <a href="#_a_timecorrelate_keyword_COVARIANCE">COVARIANCE</a>=<i>COVARIANCE</i>, <a href="#_a_timecorrelate_keyword_DOUBLE">DOUBLE</a>=<i>DOUBLE</i>)</font></p> |
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| 238 | |
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| 241 | |
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| 242 | |
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| 243 | |
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| 244 | <h3>Parameters</h3> |
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| 245 | |
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| 246 | |
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| 247 | <a name="#_a_timecorrelate_keyword_X"></a> |
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| 248 | <h4>X |
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| 249 | <font size="-1" color="#006633">in</font> |
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| 250 | |
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| 251 | |
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| 252 | <font size="-1" color="#006633">required</font> |
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| 253 | |
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| 254 | <font size="-1" color="#006633">type:</font> <font size="-1" color="#006633"><i>array</i></font> |
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| 255 | |
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| 256 | |
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| 257 | </h4> |
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| 258 | |
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| 259 | |
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| 260 | An Array which last dimension is the time dimension so |
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| 261 | size n. |
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| 262 | |
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| 263 | |
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| 264 | |
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| 265 | <a name="#_a_timecorrelate_keyword_Lag"></a> |
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| 266 | <h4>Lag |
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| 267 | <font size="-1" color="#006633">in</font> |
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| 268 | |
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| 269 | |
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| 270 | <font size="-1" color="#006633">required</font> |
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| 271 | |
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| 272 | <font size="-1" color="#006633">type:</font> <font size="-1" color="#006633"><i>scalar or vector</i></font> |
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| 273 | |
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| 274 | |
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| 275 | </h4> |
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| 276 | |
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| 277 | |
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| 278 | A scalar or n-element vector, in the interval [-(n-2), (n-2)], |
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| 279 | of type integer that specifies the absolute distance(s) between |
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| 280 | indexed elements of X. |
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| 281 | |
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| 283 | |
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| 284 | |
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| 285 | |
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| 286 | <h3>Keywords</h3> |
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| 287 | |
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| 288 | |
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| 289 | <a name="#_a_timecorrelate_keyword_COVARIANCE"></a> |
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| 290 | <h4>COVARIANCE |
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| 291 | |
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| 292 | |
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| 293 | |
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| 294 | |
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| 297 | |
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| 298 | |
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| 299 | </h4> |
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| 300 | |
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| 301 | |
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| 302 | If set to a non-zero value, the sample autocovariance |
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| 303 | is computed. |
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| 304 | |
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| 305 | |
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| 306 | <a name="#_a_timecorrelate_keyword_DOUBLE"></a> |
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| 307 | <h4>DOUBLE |
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| 308 | |
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| 314 | |
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| 315 | |
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| 316 | </h4> |
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| 317 | |
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| 318 | |
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| 319 | If set to a non-zero value, computations are done in |
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| 320 | double precision arithmetic. |
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| 321 | |
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| 322 | |
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| 323 | |
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| 324 | |
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| 325 | <h3>Examples</h3> |
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| 326 | Define an n-element sample population. |
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| 327 | x = [3.73, 3.67, 3.77, 3.83, 4.67, 5.87, 6.70, 6.97, 6.40, 5.57] |
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| 328 | |
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| 329 | Compute the autocorrelation of X for LAG = -3, 0, 1, 3, 4, 8 |
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| 330 | lag = [-3, 0, 1, 3, 4, 8] |
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| 331 | result = a_correlate(x, lag) |
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| 332 | |
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| 333 | The result should be: |
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| 334 | [0.0146185, 1.00000, 0.810879, 0.0146185, -0.325279, -0.151684] |
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| 335 | |
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| 336 | <h3>Version history</h3> |
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| 337 | |
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| 338 | <h4>Version</h4> |
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[168] | 339 | $Id: a_timecorrelate.pro 163 2006-08-29 12:59:46Z navarro $ |
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[163] | 340 | |
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| 341 | <h4>History</h4> |
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| 342 | 24/2/2000 Sebastien Masson (smasson@lodyc.jussieu.fr) |
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| 343 | |
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| 344 | Based on the A_CORRELATE procedure of IDL |
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| 345 | INTRODUCTION TO STATISTICAL TIME SERIES |
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| 346 | Wayne A. Fuller |
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| 347 | ISBN 0-471-28715-6 |
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| 365 | <font size="-3"><p> </p></font> |
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| 366 | <hr size="1" color="#CCCCCC"/> |
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[177] | 371 | <p><font color="gray" size="-3"> Produced by IDLdoc 2.0 on Wed Sep 13 16:32:56 2006.</font></p> |
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[163] | 372 | |
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| 373 | </body> |
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| 374 | </html> |
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