R help - Feb 2008 - overall comparison of an ordered categorical in a multiple regression

I entered the following:

formula<-nst~age+soc+inc+reg+imp

pnstlm<-lm(formula,nst)

summary(pnstlm)

imp and soc are ordered categorical variables but the summary does not give
an output of the overall p-values, just individual comparisons.  I can't
find help for this in the manual.  Is there a command or option change in
the summary to find out the overall p-value?

thank you in advance

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On Thu, 2008-02-21 at 14:25 +0000, Angelo Passalacqua
wrote:
> I entered the following:
> 
> formula<-nst~age+soc+inc+reg+imp
> 
> pnstlm<-lm(formula,nst)
> 
> summary(pnstlm)
> 
> imp and soc are ordered categorical variables but the summary does not give
> an output of the overall p-values, just individual comparisons.  I
can't
> find help for this in the manual.  Is there a command or option change in
> the summary to find out the overall p-value?
I think you want:

anova(pnstlm)

to get single measures for imp and soc

G

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"Angelo Passalacqua" <apassala at gmail.com> wrote in
news:d484d1550802210625i6b49868ct325d5623c6e6c9f2 at mail.gmail.com: 
> I entered the following:
> 
> formula<-nst~age+soc+inc+reg+imp
> 
> pnstlm<-lm(formula,nst)
> 
> summary(pnstlm)
> 
> imp and soc are ordered categorical variables but the summary does
> not give an output of the overall p-values, just individual
> comparisons.  I can't find help for this in the manual.  Is there a
> command or option change in the summary to find out the overall
> p-value? 
Simpson's suggestion is far simpler than my solution, and probably 
answers your question more accurately, but here is another way of looking 
at regression with ordered independent variables:

If the levels of the factor are ordered as you expect them (and even if 
the factor is not of class "ordered factor"), rather than some default
alpha ordering, then you could try:

pnstlm2 <- lm(nst ~ age +
                 as.numeric(soc) + 
                 inc + 
                 reg + 
                 as.numeric(imp), data=nst)

They won't be centered, but in this simple model, that will only change 
the intercept. The coefficients will be the change in nst$nst per single 
factor increase in "soc" or "imp". The difference in
deviance between
your first model and the one you have now will be a test of joint 
linearity of "imp" and "soc" in relation to "nst".
(You probably want to
construct them individually.)

(Personally, I think it is a bad practice to create a variable with the 
same name as a dataframe.)

-- 
David Winsemius