Thanks Michael,
Yes, it was this I wanted. And it worked just fine in my do-file as
well. During the process I have realized that I might perhaps also
need to keep some variables together in groups (so that it is not
totally scrambled, but randomly resorted but keeping two groups of
variables together). For this Austin Nichols suggestion seems to work,
so I think I have a solution for that as well.
Many thanks,
Erik Ingelsson
Quoting Michael Blasnik <[email protected]>:
It seems like you want something similar to -permute- except you want
to permute all of the variables, not just one. Here's a short program
that will scramble all of the variables in your dataset. You can usel
this within your own program and perform a monte carlo simulation
(check out -simulate-).
program define scramble
version 9.2
syntax [varlist]
tempvar hold order rand
gen long `order'=_n
foreach var of local varlist {
gen `rand'=uniform()
gen `hold'=`var'
sort `rand'
replace `var'=`hold'[`order']
drop `rand' `hold'
}
end
Michael Blasnik
----- Original Message ----- From: "Erik Ingelsson"
<[email protected]>
To: <[email protected]>
Sent: Thursday, November 30, 2006 1:31 PM
Subject: st: Construct Null Datasets through Bootstrap Resampling
Dear Statalist users,
I am trying to construct null data sets through bootstrap
resampling, to be able to account for multiple testing in genetic
analyses. I would like to sample my genotypes and phenotypes
randomly with replacement (without keeping linked them together as
the original observations in my dataset), and then run
regressions on these samples to evaluate a distribution of minimum
probability values. Thereby, I will obtain empirical p-values by
comparing the nominal p-values with the distribution of
probability from the null data sets. I have seen this implemented
in SAS, but I hope that it could be done also in STATA.
In my ?trail-and-error? approach, I have come so far, that I have
learned how to use bootstrap sampling to get a new dataset with
p-values from a set of regressions. However, these simulations are
still using my original observations (although creating new
samples), while I would like the observations to be randomly
created from the available variables (not keeping them together as
in the original dataset). Below is the code for what I have done
so far. Variables linj001-linj004 are the genotypes, thus the
important independent variables; the other variables are
covariates to adjust for; stset and all definitions, etc are done
above. In reality, I will have much more regressions to include in
the simulation, but this is just for learning how to do it.
---
*Program with the commands to be run in all bootstrap samples*
capture program drop myboot
program myboot, rclass
stcox linj001 whoht70 adadiab70 ami70 vit70 z972 z290 z085 zekg_lvh
return scalar p1 = 2*(1-normal(abs(b[linj001]/_se[linj001])))
stcox linj002 whoht70 adadiab70 ami70 vit70 z972 z290 z085 zekg_lvh
return scalar p2 = 2*(1-normal(abs(b[linj002]/_se[linj002])))
stcox linj003 whoht70 adadiab70 ami70 vit70 z972 z290 z085 zekg_lvh
return scalar p3 = 2*(1-normal(abs(b[linj003]/_se[linj003])))
stcox linj004 whoht70 adadiab70 ami70 vit70 z972 z290 z085 zekg_lvh
return scalar p4 = 2*(1-normal(abs(b[linj004]/_se[linj004])))
end
*Run the program in the original sample*
myboot
ret list
*Bootstrapping in 10000 samples*
bootstrap "myboot" p1=r(p1) p2=r(p2) p3=r(p3) p4=r(p4), reps(10000)
saving(C:\bootstrapsample) replace
---
This leaves me with a dataset (C:\bootstrapsample) which consists
of the p-values from the 4 regressions derived from 10000
simulations. However, this is not exactly what I need, since the
variables are still ?connected? in the original observations (and
then randomly chosen for my simulated sets). I would like to get
simulations with all variables scrambled, so that new observations
with all variables scrambled are created in a number of bootstrap
simulations, and then used for regressions. The present macro can
give me 10000 simulated p-values for the regressions, based on
samples with replacement, but these simulations are reusing the
actual 2000 observations from the original dataset. Now I would
like to create a ?null dataset?, in which I instead of sampling
from the observations in the real dataset, I would like Stata to
randomly ?make up? observations from the existing variables and
values, so that I have 2000 fake observations (with random
selection of all variables) to base the regressions on, in 10000
simulations.
I have read the help files, manual, Statalist, searched at
Internet, even asked Technical Support (which helped me to come
this far, but not the last part). I am using Stata 8.2 for
Windows. Is there a way to do this? Did I explain what I want to
do properly? Is there anyone who can help me with this?
Thanks a lot in advance,
Erik Ingelsson
---
Erik Ingelsson, MD, PhD
Current affiliation (until June 30, 2007):
Framingham Heart Study
73 Mt. Wayte Avenue, Suite 2
Framingham, MA 01702-5827
Phone: 508-935-3453
Fax: 508-626-1262
Cell: 508-202-8493
Permanent affiliation:
Uppsala University, Department of Public Health and Caring Sciences, Uppsala
Science Park, SE-751 85 Uppsala, SWEDEN.
Fax: +46-18-611 79 76
E-mail: [email protected]
---
*
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*
* For searches and help try:
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---
Erik Ingelsson, MD, PhD
Current affiliation (until June 30, 2007):
Framingham Heart Study
73 Mt. Wayte Avenue, Suite 2
Framingham, MA 01702-5827
Phone: 508-935-3453
Fax: 508-626-1262
Cell: 508-202-8493
Permanent affiliation:
Uppsala University, Department of Public Health and Caring Sciences, Uppsala
Science Park, SE-751 85 Uppsala, SWEDEN.
Fax: +46-18-611 79 76
E-mail: [email protected]
---
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/