MK趋势检验_mk突变检验_mk检验matlab_MK趋势检验_MK_least67x

% Time Series Trend Detection Tests % [ z, sl, lcl, ucl ] = trend( y, dt ) % where z = Mann-Kendall Statistic % sl = Sen's Slope Estimate % lcl = Lower Confidence Limit of sl % ucl = Upper Confidence Limit of sl % y = Time Series of Data % dt = Time Interval of Data %y是待检测数据序列 % function [ z, sl, lcl, ucl ] = mk( y ) %Data=xlsread('C:\Users\Administrator\Desktop\河西分析.xlsx'); %y=Data(:,9); %y=y(y>0); Data=xlsread('海流兔河径流.xlsx'); y=Data(:,2); n = length( y ); dt=1; % calculate statistic s = 0; for k = 1:n-1, for j = k+1:n, s = s + sign( y(j) - y(k) ); end; end; % variance ( assuming no tied groups ) v = ( n * ( n - 1 ) * ( 2 * n + 5 ) ) / 18; % test statistic if s == 0, z = 0; elseif s > 0, z = ( s - 1 ) / sqrt( v ); else z = ( s + 1 ) / sqrt( v ); end; % should calculate Normal value here nor = 1.64; % results disp( [ ' n = ' num2str( n ) ] ); disp( [ ' Mean Value = ' num2str( mean( y ) ) ] ); disp( [ ' Z statistic = ' num2str( z ) ] ); if abs( z ) < nor, disp( ' No significant trend' ); z = 0; elseif z > 0, disp( ' Upward trend detected' ); else disp( ' Downward trend detected' ); end; disp( 'Sens Nonparametric Estimator:' ); % calculate slopes ndash = n * ( n - 1 ) / 2; s = zeros( ndash, 1 ); i = 1; for k = 1:n-1, for j = k+1:n, s(i) = ( y(j) - y(k) ) / ( j - k ) / dt; i = i + 1; end; end; % the estimate sl = median( s ); disp( [ ' Slope Estimate = ' num2str( sl ) ] ); % variance ( assuming no tied groups ) v = ( n * ( n - 1 ) * ( 2 * n + 5 ) ) / 18; m1 = fix( ( ndash - nor * sqrt( v ) ) / 2 ); m2 = fix( ( ndash + nor * sqrt( v ) ) / 2 ); s = sort( s ); lcl = s( m1 ); ucl = s( m2 + 1 ); disp( [ ' Lower Confidence Limit = ' ... num2str( lcl ) ] ); disp( [ ' Upper Confidence Limit = ' ... num2str( ucl ) ] );