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From |
Michael McCulloch <[email protected]> |

To |
[email protected] |

Subject |
Re: st: dropped observations in bootstrap |

Date |
Sun, 7 Oct 2007 10:59:49 -0700 |

Thank you Maarten for illustrating how to set temporary variables within a program. This has solved the problem of dropped observations, and the same bootstrap program now runs successfully. My temporary elation is tempered by the nagging discomfort of not knowing why the use of temporary variables resolved the issue. I'll proceed with your suggested -trace- approach. Many thanks, Michael

--- Michael McCulloch <[email protected]> wrote:One step you can take is to call the program -msm- you created, outsideI'm running a weighted Cox regression within a bootstrap program. If I conduct the analysis program, step by step outside the bootstrap it runs just fine. However, when I conduct the exact same analysis within the bootstrap program, I get the error message: "Bootstrap replications (50) ----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 50 insufficient observations to compute bootstrap standard errors no results will be saved r(2000);" What I'd like to learn is: how can I discover what happened to those observations during the bootstrap program? Note that these observations are not dropped during the manual analysis outside the bootstrap program. I've illustrated my code below, using an adapted version of Stata's system data file cancer.dta.

-bootstrap- and see if it works, you can call it a second time and you

will see that it no longer works because several variables are

-generate-ed which already exist (where created during the first call

to -msm-). How did I know to call -msm- twice? I made the same mistake

you made many many times... One solution is to put the variables and

weights in temporary variables, as is shown in the example below.

Another usefull debuging tool is to first set trace on before calling

-msm-, and it is probably useful to first set the tracedepth to 2 or 3,

to avoid being swamped with output.

*---------------- begin example that works ------------

sysuse cancer, replace

ren died failed

tab drug

gen newdrug=0

replace newdrug=1 if drug==2|drug==3

drop drug

rename newdrug drug

tab drug

gen yeartreated=2007

gen monthtreated=10

gen daytreated=05

gen addyear=0

replace addyear=1 if studytime>=12 & studytime<24

replace addyear=2 if studytime>=24 & studytime<36

replace addyear=3 if studytime>=36

gen addmonths=0

replace addmonths=studytime-12 if studytime>=12 & studytime<24

replace addmonths=studytime-24 if studytime>=24 & studytime<36

replace addmonths=studytime-36 if studytime>36

gen yeardied=2007

replace yeardied= yeartreated + addyear if addyear!=0

list yeartreated yeardied addyear

gen monthdied=10

replace monthdied = monthtreated + addmonths if addmonths!=0

list monthtreated monthdied addmonths

replace yeardied=yeardied+1 if monthdied>=12 & monthdied<24

gen newmonthdied=monthdied

replace newmonthdied=monthdied-12 if monthdied>=12 & monthdied<24

replace newmonthdied=monthdied-24 if monthdied>=24

drop monthdied

gen monthdied = newmonthdied

drop newmonthdied

gen daydied=05

replace monthdied=monthdied+studytime if studytime<12

replace yeardied=yeardied+1 if monthdied>12

replace monthdied =monthdied-12 if monthdied>12

gen datedx = mdy(monthtreated, daytreated, yeartreated)

format datedx %d

gen datedied = mdy(monthdied, daydied, yeardied)

format datedied %d

sort datedied

list datedx datedied studytime

* PART 2

capture program drop msm

program define msm, rclass

tempname p_tcm_ALL p_notcm_ALL p_ALL p_noALL wt_stab_ALL

* fit the full model used in Traditional Cox

logit drug age

*estimate probability of treatment

predict `p_tcm_ALL'

gen `p_notcm_ALL' = 1-`p_tcm_ALL' if drug==0

*want p(drug) for both users and nonusers

replace `p_tcm_ALL' =`p_notcm_ALL' if drug==0

*create stabilized weight==P(A)/P(A|W)

logit drug

predict `p_ALL'

gen `p_noALL'=1-`p_ALL' if drug==0

replace `p_ALL' = `p_noALL' if drug==0

gen `wt_stab_ALL'=`p_ALL'/`p_tcm_ALL'

* stset the data

stset datedied [iweight=`wt_stab_ALL'], failure(failed) ///

origin(datedx) scale(30.4375) /*wt: stabilized IPTW*/

* cox

stcox drug

indeplist, local

foreach var of varlist `X' {

return scalar `var' = _b[`var']

}

end

set seed 12358

bootstrap drug=r(drug), eform reps(50): msm

*---------------------- end example that works -----------------

(For more on how to use examples I sent to the Statalist, see

http://home.fsw.vu.nl/m.buis/stata/exampleFAQ.html )

Hope this helps,

Maarten

-----------------------------------------

Maarten L. Buis

Department of Social Research Methodology

Vrije Universiteit Amsterdam

Boelelaan 1081

1081 HV Amsterdam

The Netherlands

visiting address:

Buitenveldertselaan 3 (Metropolitan), room Z434

+31 20 5986715

http://home.fsw.vu.nl/m.buis/

-----------------------------------------

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**Follow-Ups**:**Re: st: dropped observations in bootstrap***From:*Maarten buis <[email protected]>

**References**:**Re: st: dropped observations in bootstrap***From:*Maarten buis <[email protected]>

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