LinearModel.fit - are the regression coefficient standardized?

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Katha
Katha 2013-2-21
Hello at all, I have a multiple linear regression done with LinearModel.fit and got estimate values (b) for the independent data X. I want to compare the regressions coefficients (estimated values) with each other to estimate the influence on the responce in y.
Here my questions: Are the estimated values comparable to each other or how can I do this? I don't have an vector of all b for different X. Only one value for one X(i). So I can't standardize them with zscore. Is there a command to get the standardized values or an other trick? Of course I could standardize all data at the beginning, but the results are realy confusing, so I hope anybody has a better solution for me.
Thank you very much. Greets

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Tom Lane
Tom Lane 2013-2-22
I don't quite understand what you want. The coefficient estimates as shown are not standardized. The so-called "beta coefficients" are the coefficients for standardized X values, and indeed the easiest way to get them would be to standardize X beforehand, such as by using zscore(X).
Here's another idea. If you try the following, you may conclude that the two coefficients are not comparable because one multiples a predictor with values in the 1000s, and the other multiplies a predictor with values close to 1:
load carsmall
lm = LinearModel.fit([Weight Cylinders],MPG)
There is a plot intended to show the effects of these predictors on a common scale:
plotEffects(lm)
This plot shows the estimated effect on the response of changing each predictor from its minimum value to its maximum value.

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