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| 74 | <a href="a_timecorrelate.html"><<prev file</a> | next file >> <a href="c_timecorrelate.html" target="_TOP">view single page</a> | <a href="./../../index.html?format=raw" target="_TOP">view frames</a> summary: fields | <a href="#routine_summary">routine</a> details: <a href="#routine_details">routine</a> |
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| 79 | <div id="container"> |
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| 80 | |
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| 81 | <h1 class="directory"><a href="directory-overview.html?format=raw">ToBeReviewed/STATISTICS/</a></h1> |
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| 82 | <h2 class="pro_file">c_timecorrelate.pro</h2> |
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| 83 | |
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| 84 | <div id="file_attr"> |
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| 85 | <dl> |
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| 86 | </dl> |
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| 87 | </div> |
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| 88 | |
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| 89 | <div id="file_comments"></div> |
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| 90 | |
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| 91 | |
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| 92 | |
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| 93 | |
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| 94 | |
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| 95 | |
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| 96 | |
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| 97 | |
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| 98 | <div id="routine_summary"> |
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| 99 | <h2>Routine summary</h2> |
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| 100 | |
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| 101 | <dl> |
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| 102 | |
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| 103 | <dt><p><a href="#_TimeCross_Cov"><span class="result">result = </span>TimeCross_Cov(<span class="result">Xd, Yd, M, nT, Ndim</span>, Double=<span class="result">Double</span>, ZERO2NAN=<span class="result">ZERO2NAN</span>)</a></p><dt> |
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| 104 | <dd> NAME: C_TIMECORRELATE PURPOSE: This function computes the "time cross correlation" Pxy(L) or the "time cross covariance" between 2 arrays (this is some kind of c_correlate but for multidimenstionals arrays) as a function of the lag (L).</dd> |
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| 105 | |
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| 106 | <dt><p><a href="#_C_Timecorrelate"><span class="result">result = </span>C_Timecorrelate(<span class="result">X, Y, Lag</span>, Covariance=<span class="result">Covariance</span>, Double=<span class="result">Double</span>)</a></p><dt> |
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| 107 | <dd></dd> |
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| 108 | |
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| 109 | </dl> |
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| 110 | </div> |
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| 111 | |
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| 112 | |
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| 113 | <div id="routine_details"> |
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| 114 | |
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| 115 | |
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| 116 | <div class="routine_details" id="_TimeCross_Cov"> |
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| 117 | |
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| 118 | <h2><a class="top" href="#container">top</a>TimeCross_Cov </h2> |
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| 119 | |
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| 120 | <p class="header"> |
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| 121 | <span class="result">result = </span>TimeCross_Cov(<span class="result"><a href="#_TimeCross_Cov_param_Xd">Xd</a>, <a href="#_TimeCross_Cov_param_Yd">Yd</a>, <a href="#_TimeCross_Cov_param_M">M</a>, <a href="#_TimeCross_Cov_param_nT">nT</a>, <a href="#_TimeCross_Cov_param_Ndim">Ndim</a></span>, <a href="#_TimeCross_Cov_keyword_Double">Double</a>=<span class="result">Double</span>, <a href="#_TimeCross_Cov_keyword_ZERO2NAN">ZERO2NAN</a>=<span class="result">ZERO2NAN</span>)</p> |
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| 122 | |
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| 123 | <div class="comments"> |
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| 124 | NAME: |
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| 125 | C_TIMECORRELATE |
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| 126 | |
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| 127 | PURPOSE: |
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| 128 | This function computes the "time cross correlation" Pxy(L) or |
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| 129 | the "time cross covariance" between 2 arrays (this is some |
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| 130 | kind of c_correlate but for multidimenstionals arrays) as a |
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| 131 | function of the lag (L). |
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| 132 | |
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| 133 | CATEGORY: |
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| 134 | Statistics. |
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| 135 | |
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| 136 | CALLING SEQUENCE: |
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| 137 | Result = c_timecorrelate(X, Y, Lag) |
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| 138 | |
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| 139 | INPUTS: |
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| 140 | X: an Array which last dimension is the time dimension of |
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| 141 | size n, float or double. |
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| 142 | |
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| 143 | Y: an Array which last dimension is the time dimension of |
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| 144 | size n, float or double. |
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| 145 | |
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| 146 | LAG: A scalar or n-element vector, in the interval [-(n-2), (n-2)], |
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| 147 | of type integer that specifies the absolute distance(s) between |
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| 148 | indexed elements of X. |
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| 149 | |
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| 150 | KEYWORD PARAMETERS: |
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| 151 | COVARIANCE: If set to a non-zero value, the sample cross |
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| 152 | covariance is computed. |
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| 153 | |
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| 154 | DOUBLE: If set to a non-zero value, computations are done in |
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| 155 | double precision arithmetic. |
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| 156 | |
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| 157 | EXAMPLE |
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| 158 | Define two n-element sample populations. |
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| 159 | 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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| 160 | y = [2.31, 2.76, 3.02, 3.13, 3.72, 3.88, 3.97, 4.39, 4.34, 3.95] |
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| 161 | |
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| 162 | Compute the cross correlation of X and Y for LAG = -5, 0, 1, 5, 6, 7 |
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| 163 | lag = [-5, 0, 1, 5, 6, 7] |
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| 164 | result = c_timecorrelate(x, y, lag) |
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| 165 | |
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| 166 | The result should be: |
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| 167 | [-0.428246, 0.914755, 0.674547, -0.405140, -0.403100, -0.339685] |
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| 168 | |
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| 169 | PROCEDURE: |
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| 170 | |
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| 171 | |
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| 172 | FOR L>=0 |
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| 173 | |
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| 174 | n-L-1 |
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| 175 | sigma (X[k]-Xmean)(Y[k+L]-Ymean) |
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| 176 | k=0 |
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| 177 | correlation(X,Y,L)=------------------------------------------------------ |
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| 178 | n-1 n-1 |
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| 179 | sqrt( (sigma (X[k]-Xmean)^2)*(sigma (Y[k]-Ymean)^2)) |
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| 180 | k=0 k=0 |
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| 181 | |
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| 182 | |
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| 183 | |
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| 184 | n-L-1 |
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| 185 | sigma (X[k]-Xmean)(Y[k+L]-Ymean) |
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| 186 | k=0 |
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| 187 | covariance(X,Y,L)=------------------------------------------------------ |
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| 188 | n |
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| 189 | |
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| 190 | FOR L<0 |
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| 191 | |
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| 192 | |
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| 193 | n-L-1 |
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| 194 | sigma (X[k+L]-Xmean)(Y[k]-Ymean) |
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| 195 | k=0 |
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| 196 | correlation(X,Y,L)=------------------------------------------------------ |
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| 197 | n-1 n-1 |
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| 198 | sqrt( (sigma (X[k]-Xmean)^2)*(sigma (Y[k]-Ymean)^2)) |
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| 199 | k=0 k=0 |
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| 200 | |
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| 201 | |
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| 202 | |
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| 203 | n-L-1 |
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| 204 | sigma (X[k+L]-Xmean)(Y[k]-Ymean) |
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| 205 | k=0 |
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| 206 | covariance(X,Y,L)=------------------------------------------------------ |
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| 207 | n |
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| 208 | |
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| 209 | Where Xmean and Ymean are the time means of the sample populations |
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| 210 | x=(x[t=0],x[t=1],...,x[t=n-1]) and y=(y[t=0],y[t=1],...,y[t=n-1]), |
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| 211 | respectively. |
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| 212 | |
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| 213 | |
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| 214 | |
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| 215 | REFERENCE: |
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| 216 | INTRODUCTION TO STATISTICAL TIME SERIES |
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| 217 | Wayne A. Fuller |
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| 218 | ISBN 0-471-28715-6 |
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| 219 | |
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| 220 | MODIFICATION HISTORY:</div> |
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| 221 | |
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| 222 | |
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| 223 | |
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| 224 | |
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| 225 | <h3>Parameters</h3> |
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| 226 | |
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| 227 | |
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| 228 | <h4 id="_TimeCross_Cov_param_Xd">Xd |
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| 236 | |
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| 237 | </h4> |
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| 238 | |
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| 239 | <div class="comments"></div> |
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| 240 | |
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| 241 | <h4 id="_TimeCross_Cov_param_Yd">Yd |
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| 249 | |
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| 250 | </h4> |
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| 251 | |
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| 252 | <div class="comments"></div> |
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| 253 | |
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| 254 | <h4 id="_TimeCross_Cov_param_M">M |
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| 262 | |
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| 263 | </h4> |
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| 264 | |
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| 265 | <div class="comments"></div> |
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| 266 | |
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| 267 | <h4 id="_TimeCross_Cov_param_nT">nT |
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| 275 | |
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| 276 | </h4> |
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| 277 | |
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| 278 | <div class="comments"></div> |
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| 279 | |
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| 280 | <h4 id="_TimeCross_Cov_param_Ndim">Ndim |
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| 288 | |
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| 289 | </h4> |
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| 290 | |
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| 291 | <div class="comments"></div> |
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| 292 | |
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| 297 | |
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| 298 | <h3>Keywords</h3> |
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| 299 | |
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| 300 | <h4 id="_TimeCross_Cov_keyword_Double">Double |
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| 309 | </h4> |
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| 310 | |
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| 311 | <div class="comments"></div> |
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| 312 | |
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| 313 | <h4 id="_TimeCross_Cov_keyword_ZERO2NAN">ZERO2NAN |
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| 321 | |
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| 322 | </h4> |
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| 323 | |
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| 324 | <div class="comments"></div> |
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| 351 | </div> |
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| 352 | |
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| 353 | |
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| 354 | <div class="routine_details" id="_C_Timecorrelate"> |
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| 355 | |
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| 356 | <h2><a class="top" href="#container">top</a>C_Timecorrelate </h2> |
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| 357 | |
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| 358 | <p class="header"> |
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| 359 | <span class="result">result = </span>C_Timecorrelate(<span class="result"><a href="#_C_Timecorrelate_param_X">X</a>, <a href="#_C_Timecorrelate_param_Y">Y</a>, <a href="#_C_Timecorrelate_param_Lag">Lag</a></span>, <a href="#_C_Timecorrelate_keyword_Covariance">Covariance</a>=<span class="result">Covariance</span>, <a href="#_C_Timecorrelate_keyword_Double">Double</a>=<span class="result">Double</span>)</p> |
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| 360 | |
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| 361 | <div class="comments"></div> |
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| 362 | |
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| 363 | |
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| 364 | |
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| 365 | |
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| 366 | <h3>Parameters</h3> |
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| 367 | |
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| 368 | |
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| 369 | <h4 id="_C_Timecorrelate_param_X">X |
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| 370 | |
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| 377 | |
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| 378 | </h4> |
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| 379 | |
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| 380 | <div class="comments"></div> |
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| 381 | |
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| 382 | <h4 id="_C_Timecorrelate_param_Y">Y |
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| 391 | </h4> |
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| 392 | |
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| 393 | <div class="comments"></div> |
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| 394 | |
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| 395 | <h4 id="_C_Timecorrelate_param_Lag">Lag |
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| 396 | |
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| 404 | </h4> |
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| 406 | <div class="comments"></div> |
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| 412 | |
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| 413 | <h3>Keywords</h3> |
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| 414 | |
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| 415 | <h4 id="_C_Timecorrelate_keyword_Covariance">Covariance |
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| 424 | </h4> |
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| 425 | |
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| 426 | <div class="comments"></div> |
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| 427 | |
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| 428 | <h4 id="_C_Timecorrelate_keyword_Double">Double |
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| 468 | </div> |
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| 472 | <div id="tagline">Produced by IDLdoc 2.0.</div> |
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| 474 | </div> |
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| 477 | </html> |
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