[R] COnfidence intervals for estimates of linear model
Hello,
I would like to calculate the 95% confidence intervals for the estimates
of a linear model and I just wanted to check that I am doing it correct.
Is it just:
Estimate + 1.95996*Std.Error to Estimate - 1.95996*Std.Error
or is there another approach that doesn't assume a normal distrbution?
Thanks. Apologies for my naiivity
Dan
--
**
Daniel Brewer, Ph.D.
Institute of Cancer Research
Molecular Carcinogenesis
Email: daniel.bre...@icr.ac.uk
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Re: [R] COnfidence intervals for estimates of linear model
Dear Daniel,
Have a look at ?predict.lm The interval argument gives you the
information that you need.
HTH,
Thierry
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie Kwaliteitszorg
Gaverstraat 4
9500 Geraardsbergen
Belgium
Research Institute for Nature and Forest
team Biometrics Quality Assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
thierry.onkel...@inbo.be
www.inbo.be
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say what the experiment died of.
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~ Roger Brinner
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ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
-Oorspronkelijk bericht-
Van: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
Namens Daniel Brewer
Verzonden: woensdag 23 december 2009 11:28
Aan: r-h...@stat.math.ethz.ch
Onderwerp: [R] COnfidence intervals for estimates of linear model
Hello,
I would like to calculate the 95% confidence intervals for the estimates
of a linear model and I just wanted to check that I am doing it correct.
Is it just:
Estimate + 1.95996*Std.Error to Estimate - 1.95996*Std.Error
or is there another approach that doesn't assume a normal distrbution?
Thanks. Apologies for my naiivity
Dan
--
**
Daniel Brewer, Ph.D.
Institute of Cancer Research
Molecular Carcinogenesis
Email: daniel.bre...@icr.ac.uk
**
The Institute of Cancer Research: Royal Cancer Hospital, a charitable
Company Limited by Guarantee, Registered in England under Company No.
534147 with its Registered Office at 123 Old Brompton Road, London SW7
3RP.
This e-mail message is confidential and for use by the a...{{dropped:2}}
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Re: [R] COnfidence intervals for estimates of linear model
Using 1.95996 is appropriate when you know the population standard deviation or
the sample size is approximately infinity. Otherwise you should use the
t-distribution, the qt function is useful for that.
Or if you want intervals for the coefficients look at the confint function, if
you want intervals for predictions, then look at predict.lm as has been pointed
out (estimates could be interpreted either way).
Hope this helps,
--
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.s...@imail.org
801.408.8111
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
project.org] On Behalf Of Daniel Brewer
Sent: Wednesday, December 23, 2009 3:28 AM
To: r-h...@stat.math.ethz.ch
Subject: [R] COnfidence intervals for estimates of linear model
Hello,
I would like to calculate the 95% confidence intervals for the
estimates
of a linear model and I just wanted to check that I am doing it
correct.
Is it just:
Estimate + 1.95996*Std.Error to Estimate - 1.95996*Std.Error
or is there another approach that doesn't assume a normal distrbution?
Thanks. Apologies for my naiivity
Dan
--
**
Daniel Brewer, Ph.D.
Institute of Cancer Research
Molecular Carcinogenesis
Email: daniel.bre...@icr.ac.uk
**
The Institute of Cancer Research: Royal Cancer Hospital, a charitable
Company Limited by Guarantee, Registered in England under Company No.
534147 with its Registered Office at 123 Old Brompton Road, London SW7
3RP.
This e-mail message is confidential and for use by the
a...{{dropped:2}}
__
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.
__
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.
