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683	<a href="a_timecorrelate.html?format=raw"><<prev file</a> \| next file >>    <a href="c_timecorrelate.html?format=raw" 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>
684
685	</div>
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688	<div id="container">
689
690	<h1 class="directory"><a href="directory-overview.html?format=raw">ToBeReviewed/STATISTICS/</a></h1>
691	<h2 class="pro_file">c_timecorrelate.pro</h2>
692
693	<div id="file_attr">
694	<dl>
695	</dl>
696	</div>
697
698	<div id="file_comments">
699	This function computes the "time cross correlation" Pxy(L) or
700	the "time cross covariance" between 2 arrays (this is some
701	kind of c_correlate but for multidimenstionals arrays) as a
702	function of the lag (L).
703	</div>
704
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710
711
712	<div id="routine_summary">
713	<h2>Routine summary</h2>
714
715	<dl>
716
717	<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>
718	<dd></dd>
719
720	<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>
721	<dd></dd>
722
723	</dl>
724	</div>
725
726
727	<div id="routine_details">
728
729
730	<div class="routine_details" id="_TimeCross_Cov">
731
732	<h2><a class="top" href="#container">top</a>TimeCross_Cov </h2>
733
734	<p class="header">
735	<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>
736
737	<div class="comments"></div>
738
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741
742	<h3>Parameters</h3>
743
744
745	<h4 id="_TimeCross_Cov_param_Xd">Xd
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754	</h4>
755
756	<div class="comments"></div>
757
758	<h4 id="_TimeCross_Cov_param_Yd">Yd
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767	</h4>
768
769	<div class="comments"></div>
770
771	<h4 id="_TimeCross_Cov_param_M">M
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780	</h4>
781
782	<div class="comments"></div>
783
784	<h4 id="_TimeCross_Cov_param_nT">nT
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793	</h4>
794
795	<div class="comments"></div>
796
797	<h4 id="_TimeCross_Cov_param_Ndim">Ndim
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806	</h4>
807
808	<div class="comments"></div>
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815	<h3>Keywords</h3>
816
817	<h4 id="_TimeCross_Cov_keyword_Double">Double
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825
826	</h4>
827
828	<div class="comments"></div>
829
830	<h4 id="_TimeCross_Cov_keyword_ZERO2NAN">ZERO2NAN
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838
839	</h4>
840
841	<div class="comments"></div>
842
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868	</div>
869
870
871	<div class="routine_details" id="_C_Timecorrelate">
872
873	<h2><a class="top" href="#container">top</a>C_Timecorrelate <span class="categories">
874	Statistics.
875	</span></h2>
876
877	<p class="header">
878	<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>
879
880	<div class="comments"></div>
881
882
883
884
885	<h3>Parameters</h3>
886
887
888	<h4 id="_C_Timecorrelate_param_X">X
889	<span class="attr">in</span>
890
891
892	<span class="attr">required</span>
893
894
895
896
897	</h4>
898
899	<div class="comments">
900	An Array which last dimension is the time dimension of
901	size n, float or double.
902	</div>
903
904	<h4 id="_C_Timecorrelate_param_Y">Y
905	<span class="attr">in</span>
906
907
908	<span class="attr">required</span>
909
910
911
912
913	</h4>
914
915	<div class="comments">
916	An Array which last dimension is the time dimension of
917	size n, float or double.
918	</div>
919
920	<h4 id="_C_Timecorrelate_param_Lag">Lag
921	<span class="attr">in</span>
922
923
924	<span class="attr">required</span>
925
926
927
928
929	</h4>
930
931	<div class="comments">
932	A scalar or n-element vector, in the interval [-(n-2), (n-2)],
933	of type integer that specifies the absolute distance(s) between
934	indexed elements of X.
935	</div>
936
937
938
939
940
941
942	<h3>Keywords</h3>
943
944	<h4 id="_C_Timecorrelate_keyword_Covariance">Covariance
945
946
947
948
949
950
951
952
953	</h4>
954
955	<div class="comments">
956	If set to a non-zero value, the sample cross
957	covariance is computed.
958	</div>
959
960	<h4 id="_C_Timecorrelate_keyword_Double">Double
961
962
963
964
965
966
967
968
969	</h4>
970
971	<div class="comments">
972	If set to a non-zero value, computations are done in
973	double precision arithmetic.
974	</div>
975
976
977
978	<h3>Examples</h3><div class="preformat">
979
980	Define two n-element sample populations.
981	x = [3.73, 3.67, 3.77, 3.83, 4.67, 5.87, 6.70, 6.97, 6.40, 5.57]
982	y = [2.31, 2.76, 3.02, 3.13, 3.72, 3.88, 3.97, 4.39, 4.34, 3.95]
983
984	Compute the cross correlation of X and Y for LAG = -5, 0, 1, 5, 6, 7
985	lag = [-5, 0, 1, 5, 6, 7]
986	result = c_timecorrelate(x, y, lag)
987
988	The result should be:
989	[-0.428246, 0.914755, 0.674547, -0.405140, -0.403100, -0.339685]
990	</div>
991	<h3>Version history</h3>
992
993	<h4>Version</h4><div class="preformat">
994	$Id: c_timecorrelate.pro 150 2006-08-09 10:12:54Z navarro $
995	</div>
996	<h4>History</h4><div class="preformat"></div>
997
998
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1014
1015	</div>
1016
1017	</div>
1018
1019
1020
1021	<div id="tagline">Produced by IDLdoc 2.0.</div>
1022
1023	</div>
1024
1025	</body>
1026	</html>

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