An anonymous enquirer asks:
: -----Original Message-----
: From: [EMAIL PROTECTED]
: [mailto:[EMAIL PROTECTED] On Behalf Of array chip
: Sent: Saturday, 23 April 2005 2:32 PM
: To: r-help@stat.math.ethz.ch
: Subject: [R] ANOVA with both discreet and continuous variable
:
:
: Hi all,
:
: I have dataset with 2 independent variable, one (x1)
: is continuous, the other (x2) is a categorical
: variable with 2 levels. The dependent variable (y) is
: continuous. When I run linear regression y~x1*x2, I
: found that the p value for the continuous independent
: variable x1 changes when different contrasts was used
: (helmert vs. treatment), while the p values for the
: categorical x2 and interaction are independent of the
: contrasts used. Can anyone explain why?
Because the hypotheses the corresponding test statistics
are testing are invariant with respect to the choice of
contrast matrices you have considered. (This is NOT true
if your factor has more than two levels, by the way.)
: I guess the p
: value for x1 is testing different hypothesis under
: different contrasts?
The tests are for different null hypotheses, yes.
: If the interaction is NOT
: significant, what contrast should I use to test the
: hypothesis that x1 is not significantly related with
: y?
There is no choice of contrast matrix that will give the
test statistic associated with the linear term x1 this
meaning. Your question only specifies a null hypothesis,
a significance test requires a null and an alternative
hypothesis. Both matter. In the context you have set
up below the way I would go about addressing what I
think is your question would be something like:
M0 <- lm(y ~ x2) ## Null hypthesis with no x1
M1 <- lm(y ~ x1*x2) ## outer hypothesis as below
anova(M0, M1)
:
:
: x1<-rnorm(50,9,2)
: x2<-as.factor(as.numeric(runif(50)<0.35))
: y<-rnorm(50,30,5)
:
: options(contrasts=c('contr.treatment','contr.poly'))
: summary(lm(y~x1*x2))
:
: options(contrasts=c('contr.helmert','contr.poly'))
: summary(lm(y~x1*x2))
:
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