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clear all clc load('data.mat'); %Ô¤ÏȱàдÊý¾ÝÎļþdata.mat,²¢±£´æµ½µ±Ç°¹¤×÷·¾¶Ï X=data(:,1:5); y=data(:,6:8); % SIMPLSËã·¨½øÐÐ N=5; [XL,yl,XS,YS,beta,PCTVAR,MSE,stats] = plsregress(X,y,N); % °´ÕÕÖØ×éÖ®ºóµÄ³É·Ö¶ÔÔʼ·½²îµÄ½âÊÍÁ¦¶È figure(1) plot(1:N,cumsum(100*PCTVAR(2, ),'-bo');xlabel('Number of PLS components'); ylabel('Percent Variance Explained in y'); % ²Ð²îͼ figure(2) yfit = [ones(size(X,1),1) X]*beta; residuals = y-yfit; corrcoef(y,yfit); stem(residuals) xlabel('Observation'); ylabel('Residual'); % ÄâºÏͼ£¨y yfit£© figure(3) plot(y,yfit,'o') % ¼ÆËãÄâºÏÓŶȣ¬ÒÔR^2±íʾ y1=y(:,1); y2=y(:,2); y3=y(:,3); yfit1=yfit(:,1); yfit2=yfit(:,2); yfit3=yfit(:,3); TSS1 = sum((y1-mean(y1)).^2); RSS1 = sum((y1-yfit1).^2); Rsquared1 = 1-RSS1/TSS1 TSS2 = sum((y2-mean(y2)).^2); RSS2 = sum((y2-yfit2).^2); Rsquared2 = 1-RSS2/TSS2 TSS3 = sum((y3-mean(y3)).^2); RSS3 = sum((y3-yfit3).^2); Rsquared3 = 1-RSS3/TSS3 myRSS=[Rsquared1,Rsquared2,Rsquared3] % È¨ÖØ·Ö²¼Í¼ figure(4) plot(1:N,stats.W,'o-'); legend({'c1','c2','c3','c4','c5','c6','c7'},'Location','NW') xlabel('Predictor'); ylabel('Weight'); ÇëÎÊ[XL,yl,XS,YS,beta,PCTVAR,MSE,stats] = plsregress(X,y,N);Öи÷²ÎÊýÊÇָʲô£¿legend({'c1','c2','c3','c4','c5','c6','c7'},'Location','NW')È¨ÖØ·Ö²¼Í¼ÊÇÖ¸Ê²Ã´È¨ÖØ£¿Èç¹ûÒª¼ÓÒ»¸öÔ¤²âÄ£ÐÍÒªÔõô¼Ó£¿ |
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