Hi,
I am trying to use the below dataset as an input for the mslt command which does not allow the by option. I would like to run mslt by the profile variable and save the output (e.g., ei_x) for each value of profile. The code below does the job but it would be more efficient if it is wrapped up using the tempfile for example. Thanks for your help.
NM
I am trying to use the below dataset as an input for the mslt command which does not allow the by option. I would like to run mslt by the profile variable and save the output (e.g., ei_x) for each value of profile. The code below does the job but it would be more efficient if it is wrapped up using the tempfile for example. Thanks for your help.
NM
use mslt_hus.dta, clear
keep if profile=="_NCG"
drop profile
mslt, l0(710 290 0) death matrix proportion summary constant
mata e
matrix list ei_x, format(%9.3f)
use mslt_hus.dta, clear
keep if profile=="_low"
drop profile
mslt, l0(710 290 0) death matrix proportion summary constant
mata e
matrix list ei_x, format(%9.3f)
use mslt_hus.dta, clear
keep if profile=="_high"
drop profile
mslt, l0(710 290 0) death matrix proportion summary constant
mata e
matrix list ei_x, format(%9.3f)
keep if profile=="_NCG"
drop profile
mslt, l0(710 290 0) death matrix proportion summary constant
mata e
matrix list ei_x, format(%9.3f)
use mslt_hus.dta, clear
keep if profile=="_low"
drop profile
mslt, l0(710 290 0) death matrix proportion summary constant
mata e
matrix list ei_x, format(%9.3f)
use mslt_hus.dta, clear
keep if profile=="_high"
drop profile
mslt, l0(710 290 0) death matrix proportion summary constant
mata e
matrix list ei_x, format(%9.3f)
Code:
* Example generated by -dataex-. For more info, type help dataex clear input byte age str5 profile float(m12 m13 m21 m23) 0 "_NCG" .11613345 .005381245 .432161 .01959142 2 "_NCG" .11811066 .006348093 .4310168 .022441866 4 "_NCG" .12009545 .007487027 .42969385 .025696164 6 "_NCG" .12208322 .008828105 .4281682 .02940817 8 "_NCG" .12406842 .010406424 .4264133 .033637885 10 "_NCG" .12604442 .012262885 .42439955 .03845185 12 "_NCG" .12800331 .014445046 .42209435 .04392344 14 "_NCG" .12993564 .017008059 .419462 .05013302 16 "_NCG" .13183025 .02001567 .4164637 .05716796 18 "_NCG" .1336739 .023541303 .4130575 .06512241 20 "_NCG" .13545115 .027669115 .4091988 .07409667 22 "_NCG" .13714385 .03249508 .4048405 .08419625 24 "_NCG" .13873093 .03812794 .39993405 .0955304 26 "_NCG" .14018814 .04468996 .3944299 .10821001 28 "_NCG" .14148763 .05231737 .388279 .12234492 30 "_NCG" .14259788 .06116025 .3814343 .1380404 32 "_NCG" .1434835 .071381696 .37385255 .1553929 34 "_NCG" .14410533 .08315601 .36549655 .17448515 36 "_NCG" .14442071 .09666557 .3563376 .19538052 38 "_NCG" .14438409 .11209606 .3463583 .21811703 40 "_NCG" .14394796 .12962992 .335555 .2427013 0 "_high" .11581836 .0042963796 .4349149 .015716398 2 "_high" .11781436 .005069349 .4340128 .018013397 4 "_high" .11982293 .005980295 .4329648 .02063907 6 "_high" .12184045 .007053471 .4317511 .023638254 8 "_high" .12386251 .0083172545 .4303492 .02706122 10 "_high" .12588385 .009804802 .4287342 .03096413 12 "_high" .12789811 .01155478 .4268783 .035409458 14 "_high" .12989771 .013612183 .4247507 .04046632 16 "_high" .13187362 .016029239 .4223175 .04621075 18 "_high" .13381512 .018866394 .4195416 .05272583 20 "_high" .13570954 .022193346 .416383 .06010156 22 "_high" .13754196 .02609014 .4127985 .06843453 24 "_high" .13929495 .03064822 .4087424 .07782717 26 "_high" .14094818 .03597146 .4041668 .08838664 28 "_high" .14247815 .04217698 .39902255 .1002231 30 "_high" .14385787 .04939575 .3932599 .11344738 32 "_high" .14505664 .05777266 .3868303 .12816803 34 "_high" .14603987 .06746599 .3796878 .14448747 36 "_high" .1467691 .07864603 .371791 .16249755 38 "_high" .14720218 .09149241 .3631056 .18227425 40 "_high" .14729373 .10619006 .3536068 .20387194 0 "_low" .11561423 .0039817053 .4359835 .014592486 2 "_low" .11761398 .004698351 .4351517 .01672801 4 "_low" .11962774 .005543029 .4341837 .019169973 6 "_low" .12165219 .006538285 .43306085 .021960454 8 "_low" .12368326 .007710519 .4317621 .025146717 10 "_low" .12571608 .009090593 .4302638 .02878167 12 "_low" .12774478 .010714534 .4285397 .03292431 14 "_low" .1297623 .012624303 .4265606 .0376401 16 "_low" .13176031 .01486867 .4242943 .0430013 18 "_low" .13372879 .017504156 .4217054 .04908717 20 "_low" .13565594 .020596044 .4187554 .05598396 22 "_low" .13752787 .024219457 .4154027 .06378476 24 "_low" .13932821 .02846043 .4116029 .072588935 26 "_low" .14103793 .033416968 .4073091 .08250125 28 "_low" .1426349 .03919996 .4024727 .09363048 30 "_low" .14409362 .0459339 .397044 .10608742 32 "_low" .14538498 .05375722 .3909736 .11998227 34 "_low" .14647605 .06282212 .3842138 .1354213 36 "_low" .1473299 .07329352 .3767204 .1525027 38 "_low" .14790584 .0853471 .3684549 .1713118 40 "_low" .1481597 .09916586 .359387 .19191533 end
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