[R] Variance Inflation Factor VIC() with a matrix
John Fox
jfox at mcmaster.ca
Thu Sep 20 17:52:31 CEST 2012
Dear Martin,
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
> On Behalf Of Martin H. Schmidt
> Sent: Thursday, September 20, 2012 8:52 AM
> To: r-help at r-project.org
> Subject: [R] Variance Inflation Factor VIC() with a matrix
>
> Hi everyone,
>
> Running the vif() function from the car package like
>
> ----------------------------------------------------
> > reg2 <- lm(CARsPur~Delay_max10+LawChange+MarketTrend_20d+MultiTrade,
> data=data.frame(VarVecPur))
> > vif(reg2)
> Delay_max10 LawChange MarketTrend_20d MultiTrade
> 1.010572 1.009874 1.004278 1.003351
> ----------------------------------------------------
>
> gives a useful result. But using the right-hand variables as a matrix in
> the following way doesn't work with the vif() function:
>
> ----------------------------------------------------
> > reg <- lm(CARsPur~VarVecPur)
> > summary(reg)
>
> Call:
> lm(formula = CARsPur ~ VarVecPur)
>
> Residuals:
> Min 1Q Median 3Q Max
> -0.72885 -0.06461 0.00493 0.06873 0.74936
>
> Coefficients:
> Estimate Std. Error t value Pr(>|t|)
> (Intercept) -0.037860 0.006175 -6.131 9.25e-10 ***
> VarVecPurDelay_max10 0.003661 0.001593 2.298 0.0216 *
> VarVecPurLawChange 0.004679 0.006185 0.757 0.4493
> VarVecPurMarketTrend_20d 0.019015 0.001409 13.493 < 2e-16 ***
> VarVecPurMultiTrade -0.005081 0.003129 -1.624 0.1045
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Residual standard error: 0.1229 on 6272 degrees of freedom
> Multiple R-squared: 0.03021, Adjusted R-squared: 0.02959
> F-statistic: 48.84 on 4 and 6272 DF, p-value: < 2.2e-16
>
> > vif(reg)
> Error in vif.lm(reg) : model contains fewer than 2 terms
>
> ----------------------------------------------------
> Is there a solution or a way to work around?
Not with vif() in the car package, which wants to compute generalized variance inflation factors (GVIFs) for multi-df terms in the model. Single-df VIFs are pretty simple, so you could just write your own function. Alternatively, there are other packages on CRAN, such as DAAG, that compute VIFs, so you might try one of these.
I hope this helps,
John
-----------------------------------------------
John Fox
Senator McMaster Professor of Social Statistics
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
>
> Thank you very much in advanced.
>
>
>
> --
> Kind Regards,
>
> Martin H. Schmidt
> Humboldt University Berlin
>
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