MATLAB: How to select 2nd largest number (or largest if no 2nd largest)

basic

I need to find the second largest number in a vector but the number of elements from 4 to 1 so sometimes there is no second largest number (only the largest number) and in this case I can use the largest number.

I started with b=sort(a) c=(b(:,end-1)

but obviously I get an error when there is only 1 element.

Sorry if I am missing the obvious here!

Best Answer

Is it possible to have repeated values? If so, what do you define as the "second largest" value? Eg secondmax([1 2 3 3]) = 2? or 3?

y = [x(1),unique(x)];
z = y(end-1);

gives the first option. Replacing unique with sort gives the second.

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MATLAB: Sort in ascending order for first column then descending order for the second column.

A=[1 1 1; 3 2 1; 1 3 1]
B=sortrows(A,[1 -2])

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...

Best Answer

Related Solutions

MATLAB: Sort in ascending order for first column then descending order for the second column.

MATLAB: Polynomial 2nd degree

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