I appreciate that you are trying to help me but I don't fully understand your
point. At one point I did say "... the intercept is not significantly
different from zero". I admit I also said "dropping the intercept term"
which in my loose application of terminology means force the intercept to a
value of zero. So yes the intercept exists and it has a value but that value
is not significantly different from zero. This does not make the intercept
non-significant or exclude an intercept in any way. If that was your point
then I stand corrected for my loose use of terminology. If not, then perhaps
you can expand a little more.
Perhaps the following will explain what I'm after. Fitting y ~ x1+x2 for
dataframe d1 gives the following:
> summary(lm(y~x1+x2, data=d1))
Call:
lm(formula = y ~ x1 + x2, data = d1)
Residuals:
Min 1Q Median 3Q Max
-0.165377 -0.034284 0.001215 0.033799 0.127428
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.02074 0.01823 1.137 0.258
x1 0.99515 0.02122 46.891 <2e-16 ***
x2 0.97811 0.02240 43.656 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.05937 on 97 degrees of freedom
Multiple R-squared: 0.9717, Adjusted R-squared: 0.9711
F-statistic: 1665 on 2 and 97 DF, p-value: < 2.2e-16
>From my understanding I would be justified in considering the intercept to
have a value of zero. If I force a fit with zero intercept I get different
coefficients and summary stats as follows:
> summary(lm(y~0+x1+x2, data=d1))
Call:
lm(formula = y ~ 0 + x1 + x2, data = d1)
Residuals:
Min 1Q Median 3Q Max
-0.155509 -0.032272 0.004912 0.032568 0.130603
Coefficients:
Estimate Std. Error t value Pr(>|t|)
x1 1.01297 0.01434 70.64 <2e-16 ***
x2 0.99715 0.01491 66.86 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.05946 on 98 degrees of freedom
Multiple R-squared: 0.997, Adjusted R-squared: 0.9969
F-statistic: 1.62e+04 on 2 and 98 DF, p-value: < 2.2e-16
For my real application theory would suggest the intercept is zero for each
of the thousands of groups in my dataset. Of course I can fit y ~ x1+x2 and
where the summary info suggests the intercept is not significantly different
from zero, refit y ~ -1+x1+x2. I just wondered whether step or some other
function could do that for me in one R expression.
Thanks again.
David Winsemius wrote:
>
> I think you should explain (to yourself primarily) what it means to
> have a non-significant intercept. If you can justify on a theoretic
> basis the exclusion of an intercept, then you may get more assistance.
> However, if you are just naively questing after some mythical concept
> of "significance", people may be less motivated to solve what most
> would consider to be an "insignificant" question.
>
> --
> DW
>
> David Winsemius, MD
> Heritage Laboratories
> West Hartford, CT
>
--
View this message in context:
http://www.nabble.com/How-to-exclude-insignificant-intercepts-using-%22step%22-function-tp24158818p24160969.html
Sent from the R help mailing list archive at Nabble.com.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
