MATLAB: Find the min(or max) of the 2nd row of an unknown matrix

min or max

matrix A(m,n)

we need to find min or max of only the 2nd row if we have a 2nd row

Best Answer

[value,index]=max(A(2,:))
[Value,Index]=min(A(2,:))

Related Solutions

MATLAB: Polynomial 2nd degree

Higher order polynomials aren't going to help here...the following code generated the figure:

for i=3:9
  subplot(4,2,i-2);
  [~,ix]=sort(SB(:,i));
  plot(SB(ix,i),SB(ix,1))
  xlabel(sprintf('B%d',i-2))
end

There's virtually no correlation with most of the corollary variables; what there is in pieces breaks down in other areas of every variable.

Just taking a stab; ran one that uses all variables; another with the only two that were staistically significant -- those results are

>> fitlm(SB(:,3:end),SB(:,1))
ans = 
Linear regression model:
    y ~ 1 + x1 + x2 + x3 + x4 + x5 + x6 + x7
Estimated Coefficients:
                   Estimate     SE      tStat    pValue
                   ________    _____    _____    ______
    (Intercept)     63.33      13.62     4.65    0.13  
    x1             -29.51      18.92    -1.56    0.36  
    x2              20.66      15.52     1.33    0.41  
    x3               4.74      14.23     0.33    0.80  
    x4              -4.83      15.69    -0.31    0.81  
    x5              20.96      33.11     0.63    0.64  
    x6              -0.02      21.87    -0.00    1.00  
    x7               1.42      16.83     0.08    0.95  
Number of observations: 9, Error degrees of freedom: 1
Root Mean Squared Error: 2.49
R-squared: 0.934,  Adjusted R-Squared 0.474
F-statistic vs. constant model: 2.03, p-value = 0.495
>> figure
>> LMA=ans;
>> plot(LMA)
>> title('Salinity ~ 1 + B1 +B2 + B3 +B4 + B5 +B6 + B7')
>> LM12=fitlm(SB(:,3:4),SB(:,1))
LM12 = 
Linear regression model:
    y ~ 1 + x1 + x2
Estimated Coefficients:
                   Estimate     SE     tStat    pValue
                   ________    ____    _____    ______
    (Intercept)     67.74      2.13    31.82    0.00  
    x1             -20.86      5.90    -3.53    0.01  
    x2              21.88      3.21     6.82    0.00  
Number of observations: 9, Error degrees of freedom: 6
Root Mean Squared Error: 1.33
R-squared: 0.887,  Adjusted R-Squared 0.85
F-statistic vs. constant model: 23.6, p-value = 0.00144
>> figure
>> plot(LM12)
>> title('Salinity ~ 1 + B1 +B2')
>> ylim([40 100])

The last puts the plots on the same scale; notice the intervals are much tighter with only two predictors. Whether this would be worth a hoot for future predictions is pretty much pure luck I'd guess...

MATLAB: Plotting a 2nd figure

Insert a figure command before the 2nd for loop

     for nn=1:k          
       surf(Vm,Rs,T(:,:,nn)); hold on
       title('')
       xlabel(''), ylabel(''), zlabel('')        
   end 
   figure;
   for nn=1:k    
      surf(H_1,nu_1,T(:,:,nn)); hold on
      title('')
      xlabel(''), ylabel(''), zlabel('')     
   end

Best Answer

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MATLAB: Polynomial 2nd degree

MATLAB: Plotting a 2nd figure

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