[R] Fwd: Strange results : bootrstrp CIs

[varin sacha]

Dear R-experts,

I really thank you all for your responses.

Best,



Le dimanche 14 janvier 2024 à 10:22:12 UTC+1, Duncan Murdoch <murdoch.duncan using gmail.com> a écrit : 





On 13/01/2024 8:58 p.m., Rolf Turner wrote:
> On Sat, 13 Jan 2024 17:59:16 -0500
> Duncan Murdoch <murdoch.duncan using gmail.com> wrote:
> 
> <SNIP>
> 
>> My guess is that one of the bootstrap samples had a different
>> selection of countries, so factor(Country) had different levels, and
>> that would really mess things up.
>>
>> You'll need to decide how to handle that:  If you are trying to
>> estimate the coefficient for Italy in a sample that contains no data
>> from Italy, what should the coefficient be?
> 
> Perhaps NA?  Ben Bolker conjectured that boot() might be able to handle
> this.  Getting the NAs into the coefficients is a bit of a fag, but.  I
> tried:

My question was really intended as a statistical question.  From a 
statistical perspective, if I have a sampling scheme that sometimes 
generates sample size 0, should my CI be (-Inf, Inf) for high enough 
confidence level?

A Bayesian might say that inference should be entirely based on the 
prior in the case of no relevant data.  You could get similar numerical 
results by adding some fake data to every bootstrap sample, e.g. a 
single weighted observation for each country at your prior mean for that 
country, with weight chosen to match the strength of the prior.  But 
Bayesian methods don't give confidence intervals, they give credible 
intervals, and those aren't the same thing even if they are sometimes 
numerically similar.


Duncan Murdoch


> func <- function(data, idx) {
> clyde <- coef(lm(Score~ Time + factor(Country),data=data))
> ccc <- coef(lm(Score~ Time + factor(Country),data=data[idx,]))
> urk <- rep(NA,length(clyde))
> names(urk) <-names(clyde)
> urk[names(ccc)] <- ccc
> urk
> }
> 
> It produced a result:
> 
>>> set.seed(42)
>>> B= boot(e, func, R=1000)
>> B
>>
>> ORDINARY NONPARAMETRIC BOOTSTRAP
>>
>>
>> Call:
>> boot(data = e, statistic = func, R = 1000)
>>
>>
>> Bootstrap Statistics :
>>        original    bias    std. error
>> t1*  609.62500  3.6204052    95.39452
>> t2*  -54.81250 -1.6624704    36.32911
>> t3*  -41.33333 -2.7337992  100.72113
>> t4*  -96.00000 -1.0995718    99.78864
>> t5* -126.00000 -0.6548886    63.47076
>> t6*  -26.33333 -1.6516683    87.80483
>> t7*  -15.66667 -0.8391170    91.72467
>> t8*  -21.66667 -5.4544013    83.69211
>> t9*  18.33333 -0.7711001    85.57278
> 
> However I have no idea if the result is correct, or even meaningful. I
> have no idea what I'm doing.  Just hammering and hoping. 😊️
> 
> <SNIP>
> 
> cheers,
> 
> Rolf
>

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