Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of Ben Ward
> Sent: Thursday, January 06, 2011 6:09 AM
> To: r-help
> Subject: [R] Splitting a Vector
>
> Hi all,
>
> I read in a text book, that you can examine a variable that is colinear
> with others, and giving different ANOVA output and explanatory power
> when ordered differently in the model forula, by modelling that
> explanatory variable, against the others colinear with it. Then, using
> that information to split the vector (explanatory variable) in
> question,
> into two new vectors, one should correspond to the fitted values and
> one
> the residuals of the (I think you could call it nested) model. One
> vector therefore should be aligned with the subspacespace defined by
> the
> other variables colinear with it, and the other will be residual, and
> so
> orthogonal to the subspace of the colinear variables. Then by including
> these two variables in the origional model - the one that showed the
> order dependency, you can see how much explanatory power the othogonal
> part of the order dependent variable has, at different orders, and in
> principle it shouldn't change, but the vector made from the part
> co-aligned with the co-variates, will change as the order changes -
> it's
> explanatory power should decreace in ANOVA is it moves away from being
> the first explanatory variable in the model.
>
> Obviously finding the fitted model values and residual required to
> split
> the vector in two is a simple lm() with the right variables. But how
> would I create two new vectors from this and append them to my
> dataframe? Is there a package or function specially designed with this
> sort of task in mind?
>
> Thanks,
> Ben Ward.
>
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