q222.m

高速公路路面质量改进方案 采用matlab编程 达到了稳定点

P=data1;
Y=data;
Plen=length(P);
Pvalue=0;
Pvalue1=0;
Pvalue2=0;
Pvalue3=0;
Pvalue4=0;
for i=1:Plen
    mark = 0;
    for j=1:9
        if(P(i,j)==0)
            mark=1;
        end
    end    
    if(mark==0)%全部是非0数据
        Pvalue=Pvalue+1;
        PP(Pvalue,:)=P(i,:);
        %%加上常数项
        if(Y(i,1))
            Pvalue1=Pvalue1+1;
            P1(Pvalue1,:)=[1 P(i,:)  Y(i,1)];
        end
        if(Y(i,2))
            Pvalue2=Pvalue2+1;
            P2(Pvalue2,:)=[1 P(i,:)  Y(i,2)];
        end
        if(Y(i,3))
            Pvalue3=Pvalue3+1;
            P3(Pvalue3,:)=[1 P(i,:)  Y(i,3)];
        end
        if(Y(i,4))
            Pvalue4=Pvalue4+1;
            P4(Pvalue4,:)=[1 P(i,:)  Y(i,4)];
        end
        
    end
end
%处理得出各个指标和相关参数的非零关系P1，P2，P3，P4
%b = regress(P1(:,10),P1(:,1:9));
%[b,bint,r,rint,stats] = regress(P1(:,10),P1(:,1:9));
[b1,bint1,r1,rint1,stats1] = regress(P1(:,11),P1(:,1:10));
%stepwise(P1(:,1:10),P1(:,11));

rcoplot(r1,rint1);
[b2,bint2,r2,rint2,stats2] = regress(P2(:,11),P2(:,1:10));
%stepwise(P2(:,1:10),P2(:,11));

rcoplot(r2,rint2);
[b3,bint3,r3,rint3,stats3] = regress(P3(:,11),P3(:,1:10));
%stepwise(P3(:,1:10),P3(:,11));

rcoplot(r3,rint3);
[b4,bint4,r4,rint4,stats4] = regress(P4(:,11),P4(:,1:10));
%stepwise(P4(:,1:10),P4(:,11));

rcoplot(r4,rint4);

[PC,score,latent,tsquare]=princomp(PP);
%PP(1,:)=PP(1,:)*1000;
%covx = cov(PP);
%[PC1,score1,latent1,tsquare1]=princomp(PP);
%[COEFF, LATENT] = PCACOV(PP);

clc
%计算主分量与四个标准的关系
%Pvalue=0;
Pvalue1=0;
Pvalue2=0;
Pvalue3=0;
Pvalue4=0;
PC_2=PC(:,1:2);
PPP=PP*PC_2;%两个主分量
Plen=length(PPP);
for i=1:Plen
    
       if(Y(i,1))
            Pvalue1=Pvalue1+1;
            P_1(Pvalue1,:)=[PPP(i,:) 1 Y(i,1)];
        end
        if(Y(i,2))
            Pvalue2=Pvalue2+1;
            P_2(Pvalue2,:)=[PPP(i,:) 1 Y(i,2)];
        end
        if(Y(i,3))
            Pvalue3=Pvalue3+1;
            P_3(Pvalue3,:)=[PPP(i,:) 1 Y(i,3)];
        end
        if(Y(i,4))
            Pvalue4=Pvalue4+1;
            P_4(Pvalue4,:)=[PPP(i,:) 1 Y(i,4)];
        end
end
plot3(PPP,Y(:,1));
[b1,bint1,r1,rint1,stats1] = regress(P_1(:,4),P_1(:,1:3));

%rcoplot(r1,rint1);
[b2,bint2,r2,rint2,stats2] = regress(P_2(:,4),P_2(:,1:3));
%rcoplot(r2,rint2);
[b3,bint3,r3,rint3,stats3] = regress(P_3(:,4),P_3(:,1:3));
%rcoplot(r3,rint3);
[b4,bint4,r4,rint4,stats4] = regress(P_4(:,4),P_4(:,1:3));
%rcoplot(r4,rint4);



%Pcov=cov(PP)