Changeset 151 for trunk/SRC/Documentation/idldoc_html_output/ToBeReviewed/STATISTICS/a_timecorrelate.html
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- 08/09/06 12:21:11 (18 years ago)
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trunk/SRC/Documentation/idldoc_html_output/ToBeReviewed/STATISTICS/a_timecorrelate.html
r138 r151 696 696 </div> 697 697 698 <div id="file_comments"></div> 698 <div id="file_comments"> 699 Same function as A_CORRELATE but accept array (until 4 700 dimension) for input and do the autocorrelation or the 701 autocovariance along the time dimension which must be the last 702 one of the input array. 703 704 This function computes the autocorrelation Px(L) or autocovariance 705 Rx(L) of a sample population X as a function of the lag (L). 706 </div> 699 707 700 708 … … 711 719 712 720 <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> 713 <dd> NAME: A_TIMECORRELATE PURPOSE: Same function as A_CORRELATE but accept array (until 4 dimension) for input and do the autocorrelation or the autocovariance along the time dimension which must be the last one of the input array.</dd>721 <dd></dd> 714 722 715 723 <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> … … 730 738 <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> 731 739 740 <div class="comments"></div> 741 742 743 744 745 <h3>Parameters</h3> 746 747 748 <h4 id="_TimeAuto_Cov_param_X">X 749 750 751 752 753 754 755 756 757 </h4> 758 759 <div class="comments"></div> 760 761 <h4 id="_TimeAuto_Cov_param_M">M 762 763 764 765 766 767 768 769 770 </h4> 771 772 <div class="comments"></div> 773 774 <h4 id="_TimeAuto_Cov_param_nT">nT 775 776 777 778 779 780 781 782 783 </h4> 784 785 <div class="comments"></div> 786 787 788 789 790 791 792 <h3>Keywords</h3> 793 794 <h4 id="_TimeAuto_Cov_keyword_Double">Double 795 796 797 798 799 800 801 802 803 </h4> 804 805 <div class="comments"></div> 806 807 <h4 id="_TimeAuto_Cov_keyword_zero2nan">zero2nan 808 809 810 811 812 813 814 815 816 </h4> 817 818 <div class="comments"></div> 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 </div> 846 847 848 <div class="routine_details" id="_A_TimeCorrelate"> 849 850 <h2><a class="top" href="#container">top</a>A_TimeCorrelate <span class="categories"> 851 Statistics. 852 </span></h2> 853 854 <p class="header"> 855 <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> 856 857 <div class="comments"></div> 858 859 860 861 862 <h3>Parameters</h3> 863 864 865 <h4 id="_A_TimeCorrelate_param_X">X 866 <span class="attr">in</span> 867 868 869 <span class="attr">required</span> 870 871 872 873 874 </h4> 875 732 876 <div class="comments"> 733 NAME: 734 A_TIMECORRELATE 735 736 PURPOSE: 737 Same function as A_CORRELATE but accept array (until 4 738 dimension) for input and do the autocorrelation or the 739 autocovariance along the time dimension which must be the last 740 one of the input array. 741 742 This function computes the autocorrelation Px(L) or autocovariance 743 Rx(L) of a sample population X as a function of the lag (L). 744 745 CATEGORY: 746 Statistics. 747 748 CALLING SEQUENCE: 749 Result = a_timecorrelate(X, Lag) 750 751 INPUTS: 752 X: an Array which last dimension is the time dimension os 753 size n. 754 755 LAG: A scalar or n-element vector, in the interval [-(n-2), (n-2)], 756 of type integer that specifies the absolute distance(s) between 757 indexed elements of X. 758 759 KEYWORD PARAMETERS: 760 COVARIANCE: If set to a non-zero value, the sample autocovariance 761 is computed. 762 763 DOUBLE: If set to a non-zero value, computations are done in 764 double precision arithmetic. 765 766 EXAMPLE 877 An Array which last dimension is the time dimension os 878 size n. 879 </div> 880 881 <h4 id="_A_TimeCorrelate_param_Lag">Lag 882 <span class="attr">in</span> 883 884 885 <span class="attr">required</span> 886 887 888 889 890 </h4> 891 892 <div class="comments"> 893 A scalar or n-element vector, in the interval [-(n-2), (n-2)], 894 of type integer that specifies the absolute distance(s) between 895 indexed elements of X. 896 </div> 897 898 899 900 901 902 903 <h3>Keywords</h3> 904 905 <h4 id="_A_TimeCorrelate_keyword_COVARIANCE">COVARIANCE 906 907 908 909 910 911 912 913 914 </h4> 915 916 <div class="comments"> 917 If set to a non-zero value, the sample autocovariance 918 is computed. 919 </div> 920 921 <h4 id="_A_TimeCorrelate_keyword_DOUBLE">DOUBLE 922 923 924 925 926 927 928 929 930 </h4> 931 932 <div class="comments"> 933 If set to a non-zero value, computations are done in 934 double precision arithmetic. 935 </div> 936 937 938 939 <h3>Examples</h3><div class="preformat"> 767 940 Define an n-element sample population. 768 941 x = [3.73, 3.67, 3.77, 3.83, 4.67, 5.87, 6.70, 6.97, 6.40, 5.57] … … 774 947 The result should be: 775 948 [0.0146185, 1.00000, 0.810879, 0.0146185, -0.325279, -0.151684] 776 777 PROCEDURE: 778 779 780 n-L-1 781 sigma (X[k]-Xmean)(X[k+L]-Xmean) 782 k=0 783 correlation(X,L)=---------------------------------------- 784 n-1 785 sigma (X[k]-Xmean)^2 786 k=0 787 788 789 790 n-L-1 791 sigma (X[k]-Xmean)(X[k+L]-Xmean) 792 k=0 793 covariance(X,L)=------------------------------------------- 794 n 795 796 Where Xmean is the Time mean of the sample population 797 x=(x[t=0],x[t=1],...,x[t=n-1]) 798 799 800 REFERENCE: 801 INTRODUCTION TO STATISTICAL TIME SERIES 802 Wayne A. Fuller 803 ISBN 0-471-28715-6 804 805 MODIFICATION HISTORY:</div> 806 807 808 809 810 <h3>Parameters</h3> 811 812 813 <h4 id="_TimeAuto_Cov_param_X">X 814 815 816 817 818 819 820 821 822 </h4> 823 824 <div class="comments"></div> 825 826 <h4 id="_TimeAuto_Cov_param_M">M 827 828 829 830 831 832 833 834 835 </h4> 836 837 <div class="comments"></div> 838 839 <h4 id="_TimeAuto_Cov_param_nT">nT 840 841 842 843 844 845 846 847 848 </h4> 849 850 <div class="comments"></div> 851 852 853 854 855 856 857 <h3>Keywords</h3> 858 859 <h4 id="_TimeAuto_Cov_keyword_Double">Double 860 861 862 863 864 865 866 867 868 </h4> 869 870 <div class="comments"></div> 871 872 <h4 id="_TimeAuto_Cov_keyword_zero2nan">zero2nan 873 874 875 876 877 878 879 880 881 </h4> 882 883 <div class="comments"></div> 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 </div> 911 912 913 <div class="routine_details" id="_A_TimeCorrelate"> 914 915 <h2><a class="top" href="#container">top</a>A_TimeCorrelate </h2> 916 917 <p class="header"> 918 <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> 919 920 <div class="comments"></div> 921 922 923 924 925 <h3>Parameters</h3> 926 927 928 <h4 id="_A_TimeCorrelate_param_X">X 929 930 931 932 933 934 935 936 937 </h4> 938 939 <div class="comments"></div> 940 941 <h4 id="_A_TimeCorrelate_param_Lag">Lag 942 943 944 945 946 947 948 949 950 </h4> 951 952 <div class="comments"></div> 953 954 955 956 957 958 959 <h3>Keywords</h3> 960 961 <h4 id="_A_TimeCorrelate_keyword_COVARIANCE">COVARIANCE 962 963 964 965 966 967 968 969 970 </h4> 971 972 <div class="comments"></div> 973 974 <h4 id="_A_TimeCorrelate_keyword_DOUBLE">DOUBLE 975 976 977 978 979 980 981 982 983 </h4> 984 985 <div class="comments"></div> 986 987 988 989 990 991 992 993 949 </div> 950 <h3>Version history</h3> 951 952 <h4>Version</h4><div class="preformat"> 953 $Id: a_timecorrelate.pro 150 2006-08-09 10:12:54Z navarro $ 954 </div> 955 <h4>History</h4><div class="preformat"></div> 994 956 995 957
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