[R] Variance Inflation Factor VIC() with a matrix

[Michael Friendly]

You've stumbled across the answer to your question --

while lm() supports y~X formulas without a data=argument
and y~ X1+X2+X3 formulas with one, you can't depend on
all contributed functions to do the same.

As John pointed out, the advantage of car::vif over other
implementations is that it correctly handles the cases
of factors, polynomial terms, etc. for which generalized
VIF is more useful, and this is most easily accommodated
with the formula interface.

The matrix interface takes less typing, but sometimes
leaves you wondering later what you actually had in VarVecPur.

-Michael


On 9/20/2012 8:52 AM, Martin H. Schmidt wrote:
> 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?
>
> Thank you very much in advanced.
>
>
>


-- 
Michael Friendly     Email: friendly AT yorku DOT ca
Professor, Psychology Dept.
York University      Voice: 416 736-2100 x66249 Fax: 416 736-5814
4700 Keele Street    Web:   http://www.datavis.ca
Toronto, ONT  M3J 1P3 CANADA