[R] how to compute Bonferroni, Tukey's, Sheffe 95%-condence intervals for coefficients B1, B2, B3 in linear regression?

[Greg Snow]

Super,

Are you just interested in having the final intervals computed for
you?  Or are you trying to compute them yourself so that you can learn
more about what they do?  Or something else?

If the first is the case then you can just use the multicomp package
as you have mentioned.  David was assuming that this was your approach
and wanted to know why that was not good enough, what you did with
multicomp and why you were not satisfied with the results.  If you are
happy using multicomp and just are not seeing a piece that you are
expecting, then show us what you have tried,  what the results are,
what you expect the results to be, and how the last 2 differ.  Then we
can better help you.

If your goal is to learn, then re-inventing the wheel can be a good
thing, but make it clear that learning is the important part, not just
getting an answer.  Also show us what you have done so far, what
references you are using for the formulas, and where you are stuck.

If your goal is something else, then give us more details.

On Wed, May 4, 2016 at 10:44 PM, super <desolator88 at 163.com> wrote:
>
>
> Tks for you attention, i want to know Bonferroni, Tukey's, Sheffe 95%-condence intervals for coefficients in linear regression, for example,
> fit <- lm(y ~ x1 + x2)
> confint(fit) would give b0,b1,b2 95%CIs, but i want to get Bonferroni, Tukey's, Sheffe 95%-condence intervals for these coefficients. Do anyone happen to know it?
>
>
>
>
>
>
>
> At 2016-05-05 03:55:45, "David Winsemius" <dwinsemius at comcast.net> wrote:
>>
>>> On May 4, 2016, at 7:45 AM, super <desolator88 at 163.com> wrote:
>>>
>>>
>>> Dear experts,
>>>    I have a problem in compute Bonferroni,Tukey's,Sheffe 95%-condence intervals for coefficients B1,B2,B3 in linear regression using R? how can i do it? I only know how to compute these three cofindence intervals in multicomparsion by using multcomp package, and i am search a lot for how to comupte the three CIs for linear regression coefficients but without any useful information, so, plz help me ~
>>
>>Your question does not detail where the 'confint' function in pkg:multcop is letting you down. After the first few lines of the first example I type:
>>
>>confint(wht)
>>
>>#---------------
>>And get:
>>
>>        Simultaneous Confidence Intervals
>>
>>Multiple Comparisons of Means: Tukey Contrasts
>>
>>
>>Fit: aov(formula = breaks ~ wool + tension, data = warpbreaks)
>>
>>Quantile = 2.4155
>>95% family-wise confidence level
>>
>>
>>Linear Hypotheses:
>>           Estimate lwr      upr
>>M - L == 0 -10.0000 -19.3536  -0.6464
>>H - L == 0 -14.7222 -24.0758  -5.3687
>>H - M == 0  -4.7222 -14.0758   4.6313
>>
>>
>>Subsequent examples on that page use linear regression models as there starting point.
>>
>>--
>>
>>David Winsemius
>>Alameda, CA, USA
>>
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-- 
Gregory (Greg) L. Snow Ph.D.
538280 at gmail.com