[R] Bootstrapping confidence intervals

2011-05-02 Thread khosoda
Hi,
Sorry for repeated question.

I performed logistic regression using lrm and penalized it with pentrace
function. I wanted to get confidence intervals of odds ratio of each
predictor and summary(MyModel) gave them. I also tried to get
bootstrapping standard errors in the logistic regression. bootcov
function in rms package provided them. Then, I found that the confidence
intervals provided by bootstrapping (bootcov) was narrower than CIs
provided by usual variance-covariance matrix in the followings.

My data has no cluster structure.
I am wondering which confidence interval is better. I guess
bootstrapping one, but is it right?

I would appreciate anybody's help in advance.

> summary(MyModel, stenosis=c(70, 80), x1=c(1.5, 2.0), x2=c(1.5, 2.0))
 Effects  Response : outcome

 Factor  Low  High Diff. Effect S.E. Lower 0.95 Upper 0.95
 stenosis70.0 80   10.0  -0.11  0.24 -0.59  0.37
  Odds Ratio 70.0 80   10.0   0.90NA  0.56  1.45
 x1   1.5  20.5   1.21  0.37  0.49  1.94
  Odds Ratio  1.5  20.5   3.36NA  1.63  6.95
 x2   1.5  20.5  -0.29  0.19 -0.65  0.08
  Odds Ratio  1.5  20.5   0.75NA  0.52  1.08
 ClinicalScore3.0  52.0   0.61  0.38 -0.14  1.36
  Odds Ratio  3.0  52.0   1.84NA  0.87  3.89
 procedure - CA:CE2.0  1 NA   0.83  0.46 -0.07  1.72
  Odds Ratio  2.0  1 NA   2.28NA  0.93  5.59

> summary(MyModel.boot, stenosis=c(70, 80), x1=c(1.5, 2.0), x2=c(1.5, 2.0))
 Effects  Response : outcome

 Factor  Low  High Diff. Effect S.E. Lower 0.95 Upper 0.95
 stenosis70.0 80   10.0  -0.11  0.28 -0.65  0.43
  Odds Ratio 70.0 80   10.0   0.90NA  0.52  1.54
 x1   1.5  20.5   1.21  0.29  0.65  1.77
  Odds Ratio  1.5  20.5   3.36NA  1.92  5.89
 x2   1.5  20.5  -0.29  0.16 -0.59  0.02
  Odds Ratio  1.5  20.5   0.75NA  0.55  1.02
 ClinicalScore3.0  52.0   0.61  0.45 -0.28  1.50
  Odds Ratio  3.0  52.0   1.84NA  0.76  4.47
 procedure - CAS:CEA  2.0  1 NA   0.83  0.38  0.07  1.58
  Odds Ratio  2.0  1 NA   2.28NA  1.08  4.85

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Re: [R] Bootstrapping confidence intervals

2009-10-21 Thread Ista Zahn
John Fox has a nice explanation here:
http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-bootstrapping.pdf

-Ista

On Wed, Oct 21, 2009 at 6:38 AM, Charlotta Rylander  wrote:
> Hello,
>
> We are a group of PhD students working in the field of toxicology. Several
> of us have small data sets with N=10-15. Our research is mainly about the
> association between an exposure and an effect, so preferrably we would like
> to use linear regression models. However, most of the time our data do not
> fulfill the model assumptions for linear models ( no normality of y-varible
> achieved even after log transformation). We have been told that we can use
> bootstrapping to derive a confidence interval for the original parameter
> estimate ( Beta 1)  from the linear regression model and if the confidence
> interval do not include 0, we can "trust" the result from the original
> linear model ( of couse only if a scatter plot of the variables looks ok).
> What is your opinion about this method? Is that ok?  I have problems
> understanding how it is possible to resample several times from an already
> poor distribution ( that do not fulfill the model assumptions for linear
> models) to achieve a confidence interval that "validates" the use of these
> linear models? I would really appriciate a simple explanation about this!
>
> Many thanks,
>
> Charlotta Rylander
>
>        [[alternative HTML version deleted]]
>
> __
> 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.
>



-- 
Ista Zahn
Graduate student
University of Rochester
Department of Clinical and Social Psychology
http://yourpsyche.org

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[R] Bootstrapping confidence intervals

2009-10-21 Thread Charlotta Rylander
Hello,
 
We are a group of PhD students working in the field of toxicology. Several
of us have small data sets with N=10-15. Our research is mainly about the
association between an exposure and an effect, so preferrably we would like
to use linear regression models. However, most of the time our data do not
fulfill the model assumptions for linear models ( no normality of y-varible
achieved even after log transformation). We have been told that we can use
bootstrapping to derive a confidence interval for the original parameter
estimate ( Beta 1)  from the linear regression model and if the confidence
interval do not include 0, we can "trust" the result from the original
linear model ( of couse only if a scatter plot of the variables looks ok).
What is your opinion about this method? Is that ok?  I have problems
understanding how it is possible to resample several times from an already
poor distribution ( that do not fulfill the model assumptions for linear
models) to achieve a confidence interval that "validates" the use of these
linear models? I would really appriciate a simple explanation about this!
 
Many thanks,
 
Charlotta Rylander

[[alternative HTML version deleted]]

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and provide commented, minimal, self-contained, reproducible code.