Re: [R] Question on approximations of full logistic regression model

From: <khosoda_at_med.kobe-u.ac.jp>
Date: Wed, 18 May 2011 21:54:04 +0900

Thank you for your advice, Tim.

I am reading your paper and other materials in your website. I could not find R package of your bootknife method. Is there any R package for this procedure?

(11/05/17 14:13), Tim Hesterberg wrote:
> My usual rule is that whatever gives the widest confidence intervals
> in a particular problem is most accurate for that problem :-)
>
> Bootstrap percentile intervals tend to be too narrow.
> Consider the case of the sample mean; the usual formula CI is
> xbar +- t_alpha sqrt( (1/(n-1)) sum((x_i - xbar)^2)) / sqrt(n)
> The bootstrap percentile interval for symmetric data is roughly
> xbar +- z_alpha sqrt( (1/(n )) sum((x_i - xbar)^2)) / sqrt(n)
> It is narrower than the formula CI because
> * z quantiles rather than t quantiles
> * standard error uses divisor of n rather than (n-1)
>
> In stratified sampling, the narrowness factor depends on the
> stratum sizes, not the overall n.
> In regression, estimates for some quantities may be based on a small
> subset of the data (e.g. coefficients related to rare factor levels).
>
> This doesn't mean we should give up on the bootstrap.
> There are remedies for the bootstrap biases, see e.g.
> Hesterberg, Tim C. (2004), Unbiasing the Bootstrap-Bootknife Sampling
> vs. Smoothing, Proceedings of the Section on Statistics and the
> Environment, American Statistical Association, 2924-2930.
> http://home.comcast.net/~timhesterberg/articles/JSM04-bootknife.pdf
>
> And other methods have their own biases, particularly in nonlinear
> applications such as logistic regression.
>
> Tim Hesterberg
>

>> Thank you for your reply, Prof. Harrell.
>>
>> I agree with you. Dropping only one variable does not actually help a lot.
>>
>> I have one more question.
>> During analysis of this model I found that the confidence
>> intervals (CIs) of some coefficients provided by bootstrapping (bootcov
>> function in rms package) was narrower than CIs provided by usual
>> variance-covariance matrix and CIs of other coefficients wider.  My data
>> has no cluster structure. I am wondering which CIs are better.
>> I guess bootstrapping one, but is it right?
>>
>> I would appreciate your help in advance.
>> --
>> KH
>>
>>
>>
>> (11/05/16 12:25), Frank Harrell wrote:
>>> I think you are doing this correctly except for one thing.  The validation
>>> and other inferential calculations should be done on the full model.  Use
>>> the approximate model to get a simpler nomogram but not to get standard
>>> errors.  With only dropping one variable you might consider just running the
>>> nomogram on the entire model.
>>> Frank
>>>
>>>
>>> KH wrote:
>>>>
>>>> Hi,
>>>> I am trying to construct a logistic regression model from my data (104
>>>> patients and 25 events). I build a full model consisting of five
>>>> predictors with the use of penalization by rms package (lrm, pentrace
>>>> etc) because of events per variable issue. Then, I tried to approximate
>>>> the full model by step-down technique predicting L from all of the
>>>> componet variables using ordinary least squares (ols in rms package) as
>>>> the followings. I would like to know whether I am doing right or not.
>>>>
>>>>> library(rms)
>>>>> plogit<- predict(full.model)
>>>>> full.ols<- ols(plogit ~ stenosis+x1+x2+ClinicalScore+procedure, sigma=1)
>>>>> fastbw(full.ols, aics=1e10)
>>>>
>>>>    Deleted       Chi-Sq d.f. P      Residual d.f. P      AIC    R2
>>>>    stenosis       1.41  1    0.2354   1.41   1    0.2354  -0.59 0.991
>>>>    x2            16.78  1    0.0000  18.19   2    0.0001  14.19 0.882
>>>>    procedure     26.12  1    0.0000  44.31   3    0.0000  38.31 0.711
>>>>    ClinicalScore 25.75  1    0.0000  70.06   4    0.0000  62.06 0.544
>>>>    x1            83.42  1    0.0000 153.49   5    0.0000 143.49 0.000
>>>>
>>>> Then, fitted an approximation to the full model using most imprtant
>>>> variable (R^2 for predictions from the reduced model against the
>>>> original Y drops below 0.95), that is, dropping "stenosis".
>>>>
>>>>> full.ols.approx<- ols(plogit ~ x1+x2+ClinicalScore+procedure)
>>>>> full.ols.approx$stats
>>>>             n  Model L.R.        d.f.          R2           g       Sigma
>>>> 104.0000000 487.9006640   4.0000000   0.9908257   1.3341718   0.1192622
>>>>
>>>> This approximate model had R^2 against the full model of 0.99.
>>>> Therefore, I updated the original full logistic model dropping
>>>> "stenosis" as predictor.
>>>>
>>>>> full.approx.lrm<- update(full.model, ~ . -stenosis)
>>>>
>>>>> validate(full.model, bw=F, B=1000)
>>>>             index.orig training    test optimism index.corrected    n
>>>> Dxy           0.6425   0.7017  0.6131   0.0887          0.5539 1000
>>>> R2            0.3270   0.3716  0.3335   0.0382          0.2888 1000
>>>> Intercept     0.0000   0.0000  0.0821  -0.0821          0.0821 1000
>>>> Slope         1.0000   1.0000  1.0548  -0.0548          1.0548 1000
>>>> Emax          0.0000   0.0000  0.0263   0.0263          0.0263 1000
>>>>
>>>>> validate(full.approx.lrm, bw=F, B=1000)
>>>>             index.orig training    test optimism index.corrected    n
>>>> Dxy           0.6446   0.6891  0.6265   0.0626          0.5820 1000
>>>> R2            0.3245   0.3592  0.3428   0.0164          0.3081 1000
>>>> Intercept     0.0000   0.0000  0.1281  -0.1281          0.1281 1000
>>>> Slope         1.0000   1.0000  1.1104  -0.1104          1.1104 1000
>>>> Emax          0.0000   0.0000  0.0444   0.0444          0.0444 1000
>>>>
>>>> Validatin revealed this approximation was not bad.
>>>> Then, I made a nomogram.
>>>>
>>>>> full.approx.lrm.nom<- nomogram(full.approx.lrm,
>>>> fun.at=c(0.05,0.1,0.2,0.4,0.6,0.8,0.9,0.95), fun=plogis)
>>>>> plot(full.approx.lrm.nom)
>>>>
>>>> Another nomogram using ols model,
>>>>
>>>>> full.ols.approx.nom<- nomogram(full.ols.approx,
>>>> fun.at=c(0.05,0.1,0.2,0.4,0.6,0.8,0.9,0.95), fun=plogis)
>>>>> plot(full.ols.approx.nom)
>>>>
>>>> These two nomograms are very similar but a little bit different.
>>>>
>>>> My questions are;
>>>>
>>>> 1. Am I doing right?
>>>>
>>>> 2. Which nomogram is correct
>>>>
>>>> I would appreciate your help in advance.
>>>>
>>>> --
>>>> KH
>>>>
>>>> ______________________________________________
>>>> R-help_at_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.
>>>>
>>>
>>>
>>> -----
>>> Frank Harrell
>>> Department of Biostatistics, Vanderbilt University
>>> --
>>> View this message in context: http://r.789695.n4.nabble.com/Question-on-approximations-of-full-logistic-regression-model-tp3524294p3525372.html
>>> Sent from the R help mailing list archive at Nabble.com.
>>>
>>> ______________________________________________
>>> R-help_at_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.
>>
>>
>>      E-mail address
>>          Office: khosoda_at_med.kobe-u.ac.jp
>> 	Home  : khosoda_at_venus.dti.ne.jp
>>
>>
>

______________________________________________
R-help_at_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. Received on Wed 18 May 2011 - 12:59:34 GMT

This quarter's messages: by month, or sorted: [ by date ] [ by thread ] [ by subject ] [ by author ]

All messages

Archive maintained by Robert King, hosted by the discipline of statistics at the University of Newcastle, Australia.
Archive generated by hypermail 2.2.0, at Wed 18 May 2011 - 13:20:07 GMT.

Mailing list information is available at https://stat.ethz.ch/mailman/listinfo/r-help. Please read the posting guide before posting to the list.

list of date sections of archive