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| From | Catharine Morgan <Catharine.Morgan@manchester.ac.uk> |
| To | "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |
| Subject | st: path analysis sureg, cmdok, boostrap with imputed data? |
| Date | Thu, 14 Apr 2011 10:13:59 +0000 |
Hi Statalist users,
I am trying to test the effect of an independent variable (iv) through a mediator (m1) on a dependent variable (y), all of which are continuous variables. In addition I wish to add further covariates, three continuous and one categorical. I have successfully used a complete case dataset using the sureg command. However I now wish to test the imputed data set (40 imputations) and have used the sureg command using cmdok and mi in STATA 11.
I have the following queries:
1) I have read there may be issues with some commands not supported by mi but sureg with cmdok appears to be working, are there any indicators from my output to suggest otherwise or obvious signs I should look out for?
2) I have run bootstrapping (with the help of a colleague) and wanted to check about the warning STATA returns. I am wondering why strata 41 appears on the output rather than 40. I think it may be using m=0 data also. Do you know how to correct this if this is incorrect?
3) Does anyone know how I can add in multiple independent variables to test them simultaneously on the same mediator and dependent variable?
I have listed the syntax and output below. Many thanks for your time, Cathy
******************************************************************************************************************************************
This is the syntax I have used:
xi:mi estimate, cmdok: sureg (m1 iv cov1 cov2 cov3 i.cov4) (y m1 iv cov1 cov2 cov3 i.cov4)
capture program drop bootcm
program bootcm, rclass
drop _mi_id
bys _mi_m: gen _mi_id = _n
xi:mi estimate, cmdok: sureg (m1 iv cov1 cov2 cov3 i.cov4) (y m1 iv cov1 cov2 cov3 i.cov4) matrix b = e(b_mi) return scalar ind1 = el("b", 1, colnumb("b", "m1:iv")) return scalar ind2 = el("b", 1, colnumb("b", "y:m1")) return scalar indtotal = el("b", 1, colnumb("b", "m1:iv"))*el("b", 1, colnumb("b", "y:m1")) end
bootstrap r(ind1) r(ind2) r(indtotal), reps(500) strata(_mi_m) :bootcm estat bootstrap, all
The output STATA is returning:
. xi:mi estimate, cmdok: sureg (m1 iv cov1 cov2 cov3 i.cov4) (y m1 iv cov1 cov2 cov3 i.cov4)
i.depriv_grps_i _Idepriv_gr_1-4 (naturally coded; _Idepriv_gr_1 omitted)
Multiple-imputation estimates Imputations = 40
Number of obs = 425
Average RVI = 0.1076
DF adjustment: Large sample DF: min = 650.87
avg = 30595.89
max = 162393.01
------------------------------------------------------------------------------
| Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------
-------------+------
m1 |
iv | .263259 .0405813 6.49 0.000 .1836816 .3428365
cov1 | .6513497 .3615691 1.80 0.072 -.0573187 1.360018
cov2 | .0063562 .013241 0.48 0.631 -.0195965 .0323088
cov3 | .0120347 .015811 0.76 0.447 -.0189553 .0430248
_Icov4~2 | -.1198675 .4329068 -0.28 0.782 -.9683557 .7286206
_Icov4~3 | .202258 .4486146 0.45 0.652 -.6770235 1.08154
_Icov4~4 | .0695109 .4543683 0.15 0.878 -.8210761 .9600979
_cons | 13.70543 1.352386 10.13 0.000 11.05437 16.3565
-------------+----------------------------------------------------------
-------------+------
y |
m1 | 1.828701 .1590137 11.50 0.000 1.516739 2.140663
iv | -.0633312 .1370068 -0.46 0.644 -.3323599 .2056975
cov1 | 1.952565 1.141438 1.71 0.087 -.2856063 4.190736
cov2 | .1639724 .042429 3.86 0.000 .0807423 .2472025
cov3 | -.073326 .0499443 -1.47 0.142 -.1712756 .0246236
_Icov4~2 | -1.714443 1.367262 -1.25 0.210 -4.395557 .9666698
_Icov4~3 | .3836167 1.38614 0.28 0.782 -2.333895 3.101129
_Icov4~4 | -1.18265 1.39184 -0.85 0.396 -3.911344 1.546045
_cons | 26.85021 4.797198 5.60 0.000 17.43863 36.26179
------------------------------------------------------------------------------
. capture program drop bootcm
. program bootcm, rclass
1. drop _mi_id
2. bys _mi_m: gen _mi_id = _n
3. xi:mi estimate, cmdok: sureg (m1 iv cov1 cov2 cov3 i.cov4) (y m1 iv cov1 cov2 cov3 i.cov4)
4. matrix b = e(b_mi)
5. return scalar ind1 = el("b", 1, colnumb("b", "m1:iv"))
6. return scalar ind2 = el("b", 1, colnumb("b", "y:m1"))
7. return scalar indtotal = el("b", 1, colnumb("b", "m1:iv"))*el("b", 1, colnumb("b", "y:m1"))
8. end
. bootstrap r(ind1) r(ind2) r(indtotal), reps(500) strata(_mi_m) :bootcm (running bootcm on estimation sample)
Warning: Because bootcm is not an estimation command or does not set e(sample), bootstrap has no way to determine which observations
are used in calculating the statistics and so assumes that all observations are used. This means that no observations will
be excluded from the resampling because of missing values or other reasons.
If the assumption is not true, press Break, save the data, and drop the observations that are to be excluded. Be sure that
the dataset in memory contains only the relevant data.
Bootstrap results
Number of strata = 41 Number of obs = 17425
Replications = 500
command: bootcm
_bs_1: r(ind1)
_bs_2: r(ind2)
_bs_3: r(indtotal)
------------------------------------------------------------------------------
| Observed Bootstrap Normal-based
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------
-------------+------
_bs_1 | .263259 .040638 6.48 0.000 .18361 .342908
_bs_2 | 1.828701 .2084724 8.77 0.000 1.420103 2.2373
_bs_3 | .4814221 .0772151 6.23 0.000 .3300833 .6327608
------------------------------------------------------------------------------
. estat bootstrap, all
Bootstrap results
Number of strata = 41 Number of obs = 17425
Replications = 500
command: bootcm
_bs_1: r(ind1)
_bs_2: r(ind2)
_bs_3: r(indtotal)
------------------------------------------------------------------------------
| Observed Bootstrap
| Coef. Bias Std. Err. [95% Conf. Interval]
-------------+----------------------------------------------------------
-------------+------
_bs_1 | .26325901 -.021616 .040638 .18361 .342908 (N)
| .1613802 .314101 (P)
| .1995111 .3861749 (BC)
_bs_2 | 1.8287012 -.2486033 .20847243 1.420103 2.2373 (N)
| 1.132552 1.99977 (P)
| 1.691838 2.138577 (BC)
_bs_3 | .48142207 -.1006636 .07721506 .3300833 .6327608 (N)
| .2299137 .5390809 (P)
| .4234512 .6634043 (BC)
------------------------------------------------------------------------------
(N) normal confidence interval
(P) percentile confidence interval
(BC) bias-corrected confidence interval
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