Trouble with a correlation analysis code

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Alexandra Brian
Alexandra Brian am 28 Jan. 2017
Bearbeitet: John Chilleri am 1 Feb. 2017
I'm writing a code to run a correlation analysis between two variables under certain conditions. I want to see if there is a correlation between the second column and the first if the second's value is greater than 0. I wrote the following code, but MATLAB continues to print the error, "Index exceeds matrix dimensions".
clear all
indicators = xlsread('largedata1.xlsx');
X = xlsread('largedata1.xlsx', 1, 'A:A');
rows = indicators(:,2)>0; %Logical vector with the rows which satisfy all conditions.
if any(rows) % True if there is at least 1 row which meats the condition.
md1 = fitlm(indicators(rows, [2]), X(rows)); % Fit with the rows which satisfy condition.
end
Can you please help?
Best, A
  2 Kommentare
Image Analyst
Image Analyst am 28 Jan. 2017
Can you attach 'largedata.xlsx' so people can try your code?
Alexandra Brian
Alexandra Brian am 29 Jan. 2017
I added it! Thanks for the suggestion.

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John Chilleri
John Chilleri am 28 Jan. 2017
Bearbeitet: John Chilleri am 1 Feb. 2017
Hello,
I downloaded your data and ran your code and it didn't encounter any errors for me (rows was a logical vector so rows==1 actually isn't needed like I suggested - I was testing with a double vector).
clear all
indicators = xlsread('largedata1.xlsx');
X = xlsread('largedata1.xlsx', 1, 'A:A');
rows = indicators(:,2)>0; %Logical vector with the rows which satisfy all conditions.
if any(rows) % True if there is at least 1 row which meats the condition.
md1 = fitlm(indicators(rows, [2]), X(rows)); % Fit with the rows which satisfy condition.
end
>> md1
md1 =
Linear regression model:
y ~ 1 + x1
Estimated Coefficients:
Estimate SE tStat pValue
________ _________ ______ __________
(Intercept) 0.056209 0.0049663 11.318 4.6459e-28
x1 0.18966 0.10075 1.8825 0.060056
Number of observations: 1022, Error degrees of freedom: 1020
Root Mean Squared Error: 0.12
R-squared: 0.00346, Adjusted R-Squared 0.00249
F-statistic vs. constant model: 3.54, p-value = 0.0601
  3 Kommentare
Alexandra Brian
Alexandra Brian am 31 Jan. 2017
Thanks John!
John Chilleri
John Chilleri am 1 Feb. 2017
Changed solution, but it doesn't help you it would seem.

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