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In my data I have problems with heteroscedasticity as indicated by the Breusch-Pagan test and the NVC test that are both significant.

Therefore, I would like to follow the method posted by Gavin Simpson here: Regression with Heteroskedasticity Corrected Standard Errors

This seems to work but now I have troubles interpreting the results as they look very different from my original multiple regression results. 

mySummary(model_maineffect, vcovHC)

t test of coefficients:

                             Estimate Std. Error  t value  Pr(>|t|)    
    (Intercept)             -0.5462588  0.0198430 -27.5291 < 2.2e-16 ***
    IV1                      0.0762802  0.0082630   9.2315 < 2.2e-16 ***
    Control1                -0.0062260  0.0071657  -0.8689   0.38493    
    Control2                 0.0277049  0.0066251   4.1818 2.910e-05 ***
    Control3                 0.0199855  0.0104345   1.9153   0.05547 .  
    Control4                -0.4639035  0.0083046 -55.8608 < 2.2e-16 ***
    Control5                 0.6239948  0.0072652  85.8876 < 2.2e-16 ***

    ---
    Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

    Wald test

    Model 1: DV ~ IV1 + Control1 + Control2         + 
        Control3 + Control4 + Control5  
    Model 2: DV ~ 1
      Res.Df Df      F    Pr(>F)    
    1  14120                        
    2  14128 -8 1304.6 < 2.2e-16 ***
    ---
    Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Can I interpret them just in the same way as a multiple regression, i.e., IV1 has a highly significant effect on DV since Pr(>|t|) of IV1 is <0.001. And does it mean that the model is significantly improved since the Pr(>F) is <0.001? How could I report my R-Square in this case?

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    stats.stackexchange.com will be a better place to ask this question. As this is strictly statistics related question. – Jav May 17 '17 at 16:14
  • provide the original code to generate the parameters in `mySummary`. – akash87 May 17 '17 at 18:33
  • @akash87: To generate `mySummary` I followed the code by Gavin Simpson `mySummary <- function(model_maineffect, VCOV) { print(coeftest(model_maineffect, vcov. = VCOV)) print(waldtest(model_maineffect, vcov = VCOV)) }` The model is a standard multiple regression model: `model_maineffect <- lm(DV ~ IV1 + Control1 + Control2 + Control3 + Control4 + Control5)` –  May 18 '17 at 07:21
  • @Jav: Thank you! I will also post my question there –  May 18 '17 at 07:25

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