Multiple Linear Regression using fitlm function
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ANURAG DEEPAK
on 18 Jan 2020
Answered: Image Analyst
on 18 Jan 2020
Hello Sir, why i am not getting the intercept for the other variables?
>> lm=fitlm(X,TAG)
Warning: Regression design matrix is rank deficient to within machine precision.
> In classreg.regr.CompactTermsRegression/checkDesignRank (line 35)
In LinearModel.fit (line 1237)
In fitlm (line 121)
lm =
Linear regression model:
y ~ 1 + x1 + x2 + x3 + x4 + x5 + x6 + x7
Estimated Coefficients:
Estimate SE tStat pValue
__________ __ _____ ______
(Intercept) 0 0 NaN NaN
x1 0 0 NaN NaN
x2 0.00037516 0 Inf NaN
x3 0.00021467 0 Inf NaN
x4 -0.16078 0 -Inf NaN
x5 0.68268 0 Inf NaN
x6 -0.0013354 0 -Inf NaN
x7 0 0 NaN NaN
Number of observations: 5, Error degrees of freedom: 0
R-squared: 1, Adjusted R-Squared: NaN
F-statistic vs. constant model: NaN, p-value = NaN
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Accepted Answer
Star Strider
on 18 Jan 2020
In a linear regression of any sort, there is only one intercept.
2 Comments
Star Strider
on 18 Jan 2020
If you want to ssee what the intercepts of the individual variables are, you need to regress them individually. However, the entire idea of a multiple linear regression is to regress the various predictor variables together, to get a unified idea of how they all interact. Note that they do not have to be individual predictors, and can be interaction terms (predictors multiplised together) that distorts the idea of an intercept.
More Answers (1)
Image Analyst
on 18 Jan 2020
Why not do
coefficients = y \ x;
where y and x are tall matrices that have your observation values listed? (Or maybe it's y/x - I don't know which off the top of my head.)
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