5
$\begingroup$

I am running multiple linear regression with R.

mod=lm(varP ~ var1 +var2+var3+var4)

The table is:

all:
lm(formula = varP ~ var1 + var2 + var3 + var4)

Residuals:
    Min      1Q  Median      3Q     Max     
-4.9262 -0.6985  0.0472  0.7319  4.3305 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.700823   0.084737   8.271 1.45e-15 ***
var1      1.080172   0.175348   6.160 1.59e-09 ***
var2     -0.057803   0.007777  -7.432 5.25e-13 ***
var3     -9.924772   4.268235  -2.325   0.0205 *  
var4     -0.015104   0.001290 -11.710  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.139 on 460 degrees of freedom
Multiple R-squared:  0.657, Adjusted R-squared:  0.654 
F-statistic: 220.3 on 4 and 460 DF,  p-value: < 2.2e-16

it means that my model explains 65.4% of the variance. But now, I would like to determine the importance of each predictor.

I was using:

lm.sumSquares(mod) 

Is dR-sqr relevant to interpret this importance ?

              SS       dR-sqr pEta-sqr  df        F p-value
(Intercept)   88.73054 0.0510   0.1294   1  68.4015  0.0000
var4         177.88026 0.1022   0.2296   1 137.1262  0.0000
var2          71.65234 0.0412   0.1072   1  55.2361  0.0000
var1          49.22579 0.0283   0.0762   1  37.9477  0.0000
var3           7.01377 0.0040   0.0116   1   5.4069  0.0205

Error (SSE)  596.71237     NA       NA 460       NA      NA    
Total (SST) 1739.76088     NA       NA  NA       NA      NA
$\endgroup$
8
$\begingroup$

If you are using R you can use the caret package which has a built in method to give variable importance. See this link (http://caret.r-forge.r-project.org/varimp.html)

You basically will just have to do

 varImp(mod, scale = FALSE)
$\endgroup$
3
  • $\begingroup$ Thanks. I have tried and I get Overall reghght 11.710091 / regfreq 7.432101 / regtem2 6.160171 / regsreh 2.325263 In which unit are these values ? $\endgroup$ – user52265 Jul 17 '14 at 15:55
  • $\begingroup$ these values are relative to each other so there are no units. They are just percent values. Do scale=TRUE and you will see this more clearly. $\endgroup$ – mike1886 Jul 17 '14 at 16:03
  • 1
    $\begingroup$ does it mean that my variable reghght explains 11.7 % of the variance in the model ? I have tried scale= TRUE, nothing change $\endgroup$ – user52265 Jul 17 '14 at 16:09

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.