On Thu, 29 Sep 2005, Christian Hennig wrote:
>> ?confint
>>
>
> Thank you to all of you.
>
> As far as I see this is not mentioned on the lm help page (though I
> presumably don't have the recent version), which I would
> suggest...
and I would suggest that you study a good book on the subject.
Sorry, I forgot confint and I made a mistake in my suggestion which
should be:
cbind(estimate = coef(lm.D9),
lower = coef(lm.D9) - 1.96 * sqrt(diag(vcov(lm.D9))),
upper = coef(lm.D9) + 1.96 * sqrt(diag(vcov(lm.D9
Best,
Renaud
Christian Hennig a écrit :
> Hi list,
>
> is ther
Why not use vcov() and the normal approximation ?
> ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
> trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
> group <- gl(2,10,20, labels=c("Ctl","Trt"))
> weight <- c(ctl, trt)
> lm.D9 <- lm(weight ~ group)
>
> cbind(estimat
> ?confint
>
Thank you to all of you.
As far as I see this is not mentioned on the lm help page (though I
presumably don't have the recent version), which I would
suggest...
Best,
Christian
On Thu, 29 Sep 2005, Chuck Cleland wrote:
> ?confint
>
> For example:
>
> > ctl <- c(4.17,5.58,5.18,6.1
On Thu, 29 Sep 2005, Christian Hennig wrote:
> Hi list,
>
> is there any direct way to obtain confidence intervals for the regression
> slope from lm, predict.lm or the like?
There is a confint method: e.g.,
R> fm <- lm(dist ~ speed, data = cars)
R> confint(fm, parm = "speed")
2.5 % 9
?confint
> -Oprindelig meddelelse-
> Fra: [EMAIL PROTECTED]
> [mailto:[EMAIL PROTECTED] På vegne af Christian Hennig
> Sendt: 29. september 2005 13:19
> Til: r-help-request Mailing List
> Emne: [R] Regression slope confidence interval
>
> Hi list,
>
&
?confint
For example:
> ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
> trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
> group <- gl(2,10,20, labels=c("Ctl","Trt"))
> weight <- c(ctl, trt)
> lm(weight ~ group)
Call:
lm(formula = weight ~ group)
Coe
Hi list,
is there any direct way to obtain confidence intervals for the regression
slope from lm, predict.lm or the like?
(If not, is there any reason? This is also missing in some other statistics
softwares, and I thought this would be quite a standard application.)
I know that it's easy to imple
On Tue, 2004-07-20 at 17:02, Avril Coghlan wrote:
Hello,
I'm a newcomer to R so please
forgive me if this is a silly question.
It's that I have a linear regression:
fm <- lm (x ~ y)
and I want to test whether the
slope of the regression is significantly
less than 1. How can I do this in R?
Another
see also the contributed document by John Verzani, Simple R, page 87f.
> Adaikalavan Ramasamy wrote:
>
> > I would try to construct the confidence intervals and
> compare them to
> > the value that you want
> >
> >>x <- rnorm(20)
> >>y <- 2*x + rnorm(20)
> >>summary( m1 <- lm(y~x) )
> >
> >
>
Adaikalavan Ramasamy wrote:
I would try to construct the confidence intervals and compare them to
the value that you want
x <- rnorm(20)
y <- 2*x + rnorm(20)
summary( m1 <- lm(y~x) )
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1418 0.1294 1.0950.288
x
At 06:44 PM 7/20/2004 +0100, Adaikalavan Ramasamy wrote:
>I would try to construct the confidence intervals and compare them to
>the value that you want
>> x <- rnorm(20)
>> y <- 2*x + rnorm(20)
>> summary( m1 <- lm(y~x) )
>
>
>Coefficients:
>Estimate Std. Error t value Pr(>|t|)
>(Inter
I would try to construct the confidence intervals and compare them to
the value that you want
> x <- rnorm(20)
> y <- 2*x + rnorm(20)
> summary( m1 <- lm(y~x) )
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1418 0.1294 1.0950.288
x 2.2058
Hello,
I'm a newcomer to R so please
forgive me if this is a silly question.
It's that I have a linear regression:
fm <- lm (x ~ y)
and I want to test whether the
slope of the regression is significantly
less than 1. How can I do this in R?
I'm also interested in comparing the
slopes of two re
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