Re: [R] Why does Bootstrap work for one of similar models but not for the other?
In future, when posting data in your message use dput() or
textConnection() so that helpeRs can more easily load them.
I was not able to replicate your results. Here's what I got:
Bootstrap Statistics :
originalbiasstd. error
t1* 0.99975370 0.0044205644 0.04110232
t2* -0.06091574 -0.0078646847 0.05778133
t3* 0.27506204 0.0006121326 0.05296862
t4* -0.03040424 0.0002096330 0.02826951
> sessionInfo()
R version 2.11.0 (2010-04-22)
i386-pc-mingw32
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] datasets splines grid tcltk stats graphics grDevices
[8] utils methods base
other attached packages:
[1] boot_1.2-42 TinnR_1.0.3 R2HTML_1.59-1 Hmisc_3.7-0
[5] survival_2.35-8 svSocket_0.9-48 lattice_0.18-3 MASS_7.3-5
What does
getOption("digits")
give you just before you run each bootstrap?
Bert Gunter
Genentech nonclinical Statistics
On Thu, Aug 19, 2010 at 12:50 AM, Reiko Akiyama wrote:
> Dear all,
>
> Could anyone help me figure out why bootstrap works for one of similar
> models but not for the other and how I can solve it?
>
> I am using R 2.11.1 in Windows and would like to get confidence intervals
> for my models A and B by bootstrapping. However, bootstrap gives expected
> output for the model A but not for B, which I found was puzzling because the
> structure of the models is similar as I describe below. I had another person
> running the models in another computer and the same thing happens so this
> does not seem to be computer-specific. I could not find a clue for a
> solution in the R archive or in the R book (at least to the extent I
> understood).
>
> Here are the properties of the models A and B and what happens when I run
> bootstrap.
>
> modelA: rA~stA1+ stA2+stA3
> model B: rB~stB1+stB2+stB3
> The variables for the models A and B are in the same dataset called ?data?.
> The sample size is 32 for both models and the value range and distribution
> of the variables in the two models are similar. (Variables from both models
> are at the end of this enquiry.)
>
> [bootstrap of the model A]
>>
>> A.fun<-function(data,indices)coefficients(lm(rA~stA1+
>> stA2+stA3,data=data[indices,]))
>> bootA<-boot(data,A.fun,1000);bootA
>
> ORDINARY NONPARAMETRIC BOOTSTRAP
> Call:
> boot(data = data, statistic = A.fun, R = 1000)
> Bootstrap Statistics :
> original bias std. error
> t1* 1.00016501 -0.004350842 0.05309877
> t2* 0.02343475 0.008501989 0.07638795
> t3* -0.01602954 -0.004980400 0.07806805
> t4* 0.03601194 -0.005417404 0.08510128
>
> [bootstrap of the model B]
>>
>>
>> B.fun<-function(data,indices)coefficients(lm(rB~stB1+stB2+stB3),data=data[indices,])
>> bootB<-boot(data,B.fun,1000);bootB
>
> ORDINARY NONPARAMETRIC BOOTSTRAP
> Call:
> boot(data = data, statistic = B.fun, R = 1000)
> Bootstrap Statistics :
> original bias std. error
> t1* 0.99975370 0 0
> t2* -0.06091574 0 0
> t3* 0.27506203 0 0
> t4* -0.03040424 0 0
>
> What am I missing here?
> I highly appreciate any comments and suggestions.
>
> Best Wishes,
> Reiko Akiyama
> Uppsala University
> Sweden
>
> [Variables from the model A]
>>
>> rA
>
> [1] 0.7100881 1.0406464 1.1100229 0.6182664 0.7345739 1.0577865 0.6856024
> [8] 0.5264447 1.5793340 1.1793993 0.6488737 1.0214076 1.3589618 1.0528893
> [15] 1.5242409 1.3761019 0.9427032 0.6794809 1.4752693 0.7737512 1.0120797
> [22] 0.8692458 1.2079660 1.0610513 0.8570029 0.9794319 1.0957395 0.8243552
> [29] 0.4162586 1.4079334 1.0692132 1.1059419
>>
>> stA1
>
> [1] -0.9126354 -0.8331680 -1.0239203 -0.3721959 -0.5311308 0.7564474
> [7] -1.1828933 -1.2146727 -0.8172593 -0.9921410 -0.5152602 -0.9285442
> [13] -0.4198840 -0.9444529 -0.4198840 -0.8331680 1.2810163 1.4081718
> [19] 1.7102091 2.3460247 1.3806653 1.3127957 1.2333282 1.4240806
> [25] -0.1337555 -0.1973142 0.2954372 -0.1337555 -0.4039753 -0.3880665
> [31] 0.2795666 -0.2291317
>>
>> stA2
>
> [1] -0.2292617 -0.4917962 -0.6437899 -1.2241293 -0.3398026 -2.0946384
> [7] -1.0721356 -1.2655821 -1.3484877 -1.8873744 -0.7543307 -0.9615948
> [13] -0.3674378 0.4483537 0.8761467 -0.8786892 0.5312593 1.1524988
> [19] 0.3234425 -0.4088906 0.5102565 1.1945044 1.7748438 0.6827002
> [25] 0.6418001 1.1801340 0.4207184 0.8076114 0.9181522 0.6827002
> [31] 0.9037819 0.9181522
>>
>> stA3
>
> [1] 0.86459627 -0.23416149 -2.00372671 0.04161491 0.78881988 -2.50869565
> [7] -0.02608696 -0.84161491 -0.95465839 -0.28012422 0.47080745 0.07577640
> [13] 0.84223602 0.24472050 2.83975155 0.43043478 -0.75652174 -0.92795031
> [19] 0.29192547 -0.78633540 -0.78385093 -0.51242236 0.59627329 0.19068323
> [25] 0.02919255 1.17018634 -0.19440994 0.68385093 1.08881988 -0.28385093
> [31] -0.71118012 1.065
[R] Why does Bootstrap work for one of similar models but not for the other?
Dear all, Could anyone help me figure out why bootstrap works for one of similar models but not for the other and how I can solve it? I am using R 2.11.1 in Windows and would like to get confidence intervals for my models A and B by bootstrapping. However, bootstrap gives expected output for the model A but not for B, which I found was puzzling because the structure of the models is similar as I describe below. I had another person running the models in another computer and the same thing happens so this does not seem to be computer-specific. I could not find a clue for a solution in the R archive or in the R book (at least to the extent I understood). Here are the properties of the models A and B and what happens when I run bootstrap. modelA: rA~stA1+ stA2+stA3 model B: rB~stB1+stB2+stB3 The variables for the models A and B are in the same dataset called ?data?. The sample size is 32 for both models and the value range and distribution of the variables in the two models are similar. (Variables from both models are at the end of this enquiry.) [bootstrap of the model A] A.fun<-function(data,indices)coefficients(lm(rA~stA1+ stA2+stA3,data=data[indices,])) bootA<-boot(data,A.fun,1000);bootA ORDINARY NONPARAMETRIC BOOTSTRAP Call: boot(data = data, statistic = A.fun, R = 1000) Bootstrap Statistics : original biasstd. error t1* 1.00016501 -0.004350842 0.05309877 t2* 0.02343475 0.008501989 0.07638795 t3* -0.01602954 -0.004980400 0.07806805 t4* 0.03601194 -0.005417404 0.08510128 [bootstrap of the model B] B.fun<-function(data,indices)coefficients(lm(rB~stB1+stB2+stB3),data=data[indices,]) bootB<-boot(data,B.fun,1000);bootB ORDINARY NONPARAMETRIC BOOTSTRAP Call: boot(data = data, statistic = B.fun, R = 1000) Bootstrap Statistics : original biasstd. error t1* 0.99975370 0 0 t2* -0.06091574 0 0 t3* 0.27506203 0 0 t4* -0.03040424 0 0 What am I missing here? I highly appreciate any comments and suggestions. Best Wishes, Reiko Akiyama Uppsala University Sweden [Variables from the model A] rA [1] 0.7100881 1.0406464 1.1100229 0.6182664 0.7345739 1.0577865 0.6856024 [8] 0.5264447 1.5793340 1.1793993 0.6488737 1.0214076 1.3589618 1.0528893 [15] 1.5242409 1.3761019 0.9427032 0.6794809 1.4752693 0.7737512 1.0120797 [22] 0.8692458 1.2079660 1.0610513 0.8570029 0.9794319 1.0957395 0.8243552 [29] 0.4162586 1.4079334 1.0692132 1.1059419 stA1 [1] -0.9126354 -0.8331680 -1.0239203 -0.3721959 -0.5311308 0.7564474 [7] -1.1828933 -1.2146727 -0.8172593 -0.9921410 -0.5152602 -0.9285442 [13] -0.4198840 -0.9444529 -0.4198840 -0.8331680 1.2810163 1.4081718 [19] 1.7102091 2.3460247 1.3806653 1.3127957 1.2333282 1.4240806 [25] -0.1337555 -0.1973142 0.2954372 -0.1337555 -0.4039753 -0.3880665 [31] 0.2795666 -0.2291317 stA2 [1] -0.2292617 -0.4917962 -0.6437899 -1.2241293 -0.3398026 -2.0946384 [7] -1.0721356 -1.2655821 -1.3484877 -1.8873744 -0.7543307 -0.9615948 [13] -0.3674378 0.4483537 0.8761467 -0.8786892 0.5312593 1.1524988 [19] 0.3234425 -0.4088906 0.5102565 1.1945044 1.7748438 0.6827002 [25] 0.6418001 1.1801340 0.4207184 0.8076114 0.9181522 0.6827002 [31] 0.9037819 0.9181522 stA3 [1] 0.86459627 -0.23416149 -2.00372671 0.04161491 0.78881988 -2.50869565 [7] -0.02608696 -0.84161491 -0.95465839 -0.28012422 0.47080745 0.07577640 [13] 0.84223602 0.24472050 2.83975155 0.43043478 -0.75652174 -0.92795031 [19] 0.29192547 -0.78633540 -0.78385093 -0.51242236 0.59627329 0.19068323 [25] 0.02919255 1.17018634 -0.19440994 0.68385093 1.08881988 -0.28385093 [31] -0.71118012 1.06583851 [Variables from the model B] rB [1] 1.5385568 1.5885100 1.3587255 0.8991566 1.4086787 0.3097095 0.9191378 [8] 0.3996252 0.7393065 0.6993440 1.2488286 1.4186693 1.4586318 1.8282851 [15] 0.8991566 0.9790816 1.0889785 1.0090535 0.7792690 0.8991566 0.8791753 [22] 0.7892597 0.6294096 0.9690910 1.0689973 0.5994377 0.6793628 0.7293159 [29] 0.9690910 0.7393065 0.7193253 1.7583507 stB1 [1] -0.67898627 -0.94275552 -1.32045796 0.03417996 -1.18276552 2.01872951 [7] -1.75937865 -1.85016395 -0.70319013 -0.89159673 -0.35055299 -0.38890124 [13] -0.81445562 -0.98941255 -0.95548269 -0.63066192 0.52759406 1.27063302 [19] 1.19746568 1.34424498 0.62679931 1.15103096 1.24195520 0.94395043 [25] 0.20232868 0.71085978 0.53654199 0.67470683 0.41377202 0.38428833 [31] 0.58178180 -0.40626910 stB2 [1] 2.18599646 1.64030436 0.29913150 -0.30874645 2.29052340 -2.13238029 [7] -0.78386750 -0.53233418 -0.96552917 -1.00046883 -0.02227346 -0.71399585 [13] 0.42490201 1.27034048 0.09650296 -0.32970563 -0.23188840 -0.24586119 [19] 0.04061179 -0.07118592 -0.49040812 -0.04323265 -0.06419952 -0.18297594 [25] 0.40393513 -0.73495504 -0.53233418 -0.23188840 0.13144263 -0.21092921 [31] -1.24501576 2.29052340 stB3 [1] -0.368 0.3416667 -1.788 -1.813 -0.617 0.
