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tbates / umx

Making Structural Equation Modeling (SEM) in R quick & powerful
https://tbates.github.io/
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Complex survey (weighted) data #202

Closed lf-araujo closed 9 months ago

lf-araujo commented 1 year ago

Writing on behalf of a Brazilian colleague with limited command of English. This is not an issue, a question, rather. You can close it if you don't want your issues tracker polluted with Q.

They found this and this issues in the forum, but they are not ready to take on full OpenMx mode. Would it be possible to add the algebras for the weights using umx, so that they could specify the model using lavaan syntax?

Here the MWE, first data:

Click to expand! ```r structure(list(peso_rake = c(497.518, 497.518, 683.324, 683.324, 1533.863, 535.25, 497.518, 535.25, 683.324, 683.324, 3177.653, 535.25, 535.25, 1533.863, 497.518, 535.25, 535.25, 497.518, 497.518, 535.25, 535.25, 1533.863, 535.25, 497.518, 1533.863, 1533.863, 1533.863, 1533.863, 3177.653, 1533.863, 1533.863, 1533.863, 535.25, 497.518, 497.518, 497.518, 535.25, 683.324, 1533.863, 497.518, 497.518, 497.518, 683.324, 535.25, 683.324, 535.25, 497.518, 683.324, 535.25, 1533.863, 535.25, 535.25, 535.25, 1533.863, 535.25, 497.518, 683.324, 466.788, 466.788, 497.518, 497.518, 554.242, 466.788, 497.518, 497.518, 466.788, 466.788, 497.518, 497.518, 535.068, 497.518, 683.324, 683.324, 683.324, 497.518, 497.518, 466.788, 466.788, 497.518, 497.518, 497.518, 683.324, 535.068, 466.788, 466.788, 466.788, 683.324, 466.788, 466.788, 466.788, 466.788, 466.788, 466.788, 535.068, 535.068, 497.518, 535.068, 497.518, 1643.163, 497.518, 497.518, 466.788, 466.788, 466.788, 497.518, 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38.3491599707816, 38.3491599707816, 38.3491599707816, 20.3125, 28.515625, 28.515625, 25.390625, 31.161373901474, 31.161373901474, 31.161373901474, 26.8135904499541, 26.8135904499541, 25.7777777777778, 25.7777777777778, 25.7777777777778, 25.7777777777778, 25.7777777777778, 28.0612244897959, 28.0612244897959, 28.0612244897959, 28.0612244897959, 18.5910367469641, 18.5910367469641, 27.34375, 27.34375, 26.0226442658875, 26.0226442658875, 23.4235574063058, 23.4235574063058, 23.4235574063058, 15.625, 24.256837098692, 24.256837098692, 26.609712545079, 24.8888888888889, 24.8888888888889, 24.8888888888889, 21.3333333333333, 23.5294117647059, 23.5294117647059, 27.5312294543064, 27.4076572777871, 25.4374808122451, 26.2222222222222, 26.2975778546713, 36.3573407202216, 25, 25, 25, 25, 25, 30.4779662232421, 37.7551020408163, 37.7551020408163, 37.7551020408163, 37.7551020408163, 37.7551020408163, 37.7551020408163, 37.7551020408163, 25.390625, 25.390625, 25.390625, 29.0005948839976, 29.0005948839976, 29.0005948839976, 24.2382271468144, 22.9481157445588, 22.9481157445588, 27.681660899654, 31.8668252080856, 31.8668252080856, 26.6586187817514, 26.6586187817514, 26.6586187817514, 26.6586187817514, 26.6586187817514, 26.6586187817514, 26.6586187817514, 32.8731097961867, 32.8731097961867, 28.7104793249536, 28.7104793249536, 24.21875, 24.21875, 35.7559561895442, 35.7559561895442, 35.7559561895442, 35.7559561895442, 35.7559561895442, 35.7559561895442, 35.7559561895442, 21.09375, 21.09375, 21.09375, 32.8824141519251, 32.8824141519251, 23.2334561768947, 36.1987501905197, 36.1987501905197, 26.8923231381548, 24.609375, 24.609375, 24.609375, 24.3232286833926, 24.3232286833926, 24.3232286833926, 25.8064516129032, 25.8064516129032, 25.8064516129032, 38.1963223363981, 30.078125, 21.484375, 29.6875, 29.6875, 29.6875, 29.6875, 24.9134948096886, 24.9134948096886, 16.40625, 16.40625, 16.40625, 43.691725597946, 43.691725597946, 43.691725597946, 32.1428571428571, 32.1428571428571, 31.633714880333, 31.633714880333, 28, 30.4271978579253, 16.796875, 27.3034383821556, 24.3438094940857, 26.8351800554017, 22.2222222222222, 22.2222222222222, 28.4444444444444, 28.4444444444444, 28.4444444444444, 39.5555555555556, 31.8668252080856, 19.2343779320698, 25.390625, 25.390625, 20.5498866213152, 27.1808999081726, 27.1808999081726, 27.1808999081726, 38.5411172867053, 22.0603537981269, 25.390625, 25.390625, 25.5588462006572, 25.5588462006572, 22.8623685413809, 28.3987180007303, 26.4380708219837, 26.4380708219837, 26.4380708219837, 20.5049339997437, 25.9701155481953, 22.8060172799439, 22.8060172799439, 22.8060172799439, 31.4317883280165, 31.4317883280165, 31.4317883280165, 29.2101099436083, 31.7863954227591, 24.2214532871972, 25.9645421720962, 24.3496091246956, 24.3496091246956, 24.3496091246956, 24.3496091246956, 24.3496091246956, 23.9389897746315, 23.9389897746315, 22.640864624717, 40.3061224489796, 28.515625, 28.515625, 28.515625, 40.2527700831025, 40.2527700831025, 40.2527700831025, 40.2527700831025, 31.4317883280165, 22.65625, 23.7386621315193, 27.8876170655567, 28, 23.5303663434622, 22.3093564088697, 22.3093564088697, 22.3093564088697, 30.297783933518, 30.297783933518, 21.09375, 31.5775623876146, 24.4444444444444, 25.0995015956112, 25.0995015956112, 25.0995015956112, 25.0995015956112, 31.5115016981198, 29.1363163371488, 29.1363163371488, 26.4462809917355, 33.984375, 28.2283864654296, 28.2283864654296, 27.2680055401662, 27.2680055401662, 27.2680055401662, 47.3228772309356, 47.3228772309356, 26.953125, 26.953125, 30.0711684319556, 31.2394143550755, 31.2394143550755, 31.2394143550755, 38.671875, 38.671875, 38.671875, 32.0512820512821, 23.828125, 23.828125, 23.828125, 25.8645124716553 ), sexo = c(1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L), quant_doe_cron = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 0, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 0, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 0, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 3, 3, 3, 3, 1, 1, 0, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 5, 5, 5, 5, 0, 2, 2, 1, 4, 4, 4, 4, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 0, 2, 2, 1, 1, 2, 2, 1, 1, 1, 3, 3, 3, 1, 1, 0, 4, 4, 4, 4, 4, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 3, 3, 3, 3, 0, 2, 2, 2, 2, 3, 2, 2, 3, 3, 3, 1, 3, 3, 3, 1, 1, 1, 2, 2, 2, 2, 3, 3, 1, 1, 3, 3, 3, 3, 3, 7, 7, 7, 7, 7, 7, 7, 7, 7, 3, 3, 3, 3, 1, 1, 3, 3, 1, 1, 7, 7, 1, 1, 7, 7, 7, 1, 3, 3, 2, 1, 2, 2, 1, 2, 2, 2, 3, 1, 4, 4, 4, 4, 4, 4, 3, 1, 1, 1, 1, 1, 4, 5, 1, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 2, 2, 2, 4, 4, 1, 3, 3, 3, 3, 3, 3, 0, 3, 2, 3, 2, 3, 3, 3, 4, 4, 3, 3, 2, 2, 2, 1, 2, 2, 3, 3, 3, 1, 1, 2, 2, 2, 2, 4, 1, 2, 2, 3, 4, 3, 3, 3, 2, 2, 1, 1, 2, 5, 5, 2, 2, 1, 1, 1, 1, 3, 3, 0, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 1, 4, 4, 4, 1, 1, 2, 2, 2, 0, 2, 2, 1, 2, 3, 3, 3, 2, 2, 2, 4, 4, 4, 4, 4, 4, 0, 0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 6, 6, 6, 6, 6, 6, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 0, 4, 4, 4, 4, 4, 2, 1, 1, 1, 1, 0, 0, 2, 2, 2, 3, 2, 2, 2, 2, 2, 3, 3, 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 4, 1, 2, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 2, 2, 1, 2, 2, 2, 1, 1, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 2, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 3, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 4, 4, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 4, 4, 4, 2, 0, 0, 1, 3, 3, 2, 2, 2, 2, 2, 2, 2, 1, 1, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 1, 1, 1, 3, 3, 1, 5, 5, 5, 1, 1, 1, 3, 3, 3, 2, 1, 4, 3, 3, 3, 3, 1, 1, 3, 3, 3, 4, 4, 4, 4, 4, 1, 1, 2, 2, 2, 3, 1, 1, 3, 3, 2, 2, 2, 1, 1, 3, 2, 2, 1, 3, 3, 3, 3, 1, 7, 7, 7, 7, 7, 4, 3, 3, 3, 1, 1, 4, 4, 4, 3, 3, 3, 1, 1, 1, 2, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 3, 1, 1, 1, 1, 1, 4, 4, 4, 7, 7, 2, 2, 1, 4, 4, 4, 4, 1, 1, 1, 1, 3, 1, 1, 4, 4, 4, 2, 2, 3, 3, 1, 1, 1, 1, 3, 3, 3, 0, 2, 2, 2, 2), multimorbidade = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1), quant_med_cron = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 2, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 1, 2, 2, 1, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 0, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 8, 8, 1, 1, 8, 8, 8, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 3, 3, 3, 2, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 1, 1, 3, 3, 3, 1, 2, 2, 1, 3, 3, 3, 1, 2, 2, 1, 2, 2, 1, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 3, 3, 3, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 3, 3, 3, 2, 2, 0, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 3, 3, 3, 3, 3, 1, 1, 1, 4, 4, 4, 4, 2, 2, 1, 1, 1, 1, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 1, 1, 2, 2, 2, 1, 1, 1, 3, 3, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 3, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 1, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 1, 2, 2, 1, 3, 3, 3, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 2, 2, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 1, 3, 3, 3, 1, 1, 8, 8, 8, 8, 8, 1, 3, 3, 3, 1, 1, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 3, 3, 3, 2, 2, 1, 1, 1, 1, 1, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 3, 3, 3, 8, 8, 1, 1, 1, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 3, 3, 3, 0, 1, 1, 1, 1), regiao = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), medic_psi = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), tempo_uso_medicamento = c(NA, 3L, 5L, 6L, 5L, 3L, 2L, 1L, 1L, 3L, 15L, NA, 10L, 2L, 5L, NA, 4L, NA, 3L, 20L, NA, 4L, 8L, 6L, 6L, NA, 1L, 1L, NA, 2L, NA, 7L, 3L, 8L, 5L, 6L, 5L, 5L, 4L, NA, 4L, NA, 1L, 8L, 2L, 8L, 8L, 2L, 8L, 2L, 8L, NA, 30L, 3L, 10L, 4L, 6L, NA, 2L, NA, NA, 1L, 5L, 3L, 5L, 10L, 4L, 3L, NA, 10L, 1L, NA, 12L, 12L, NA, 11L, 6L, NA, NA, 4L, 1L, 4L, 5L, NA, NA, 4L, 6L, 3L, 4L, NA, NA, 8L, 5L, 4L, NA, 5L, 1L, 8L, 2L, 6L, NA, NA, 3L, 2L, 3L, 4L, NA, NA, 1L, 2L, 5L, NA, NA, 2L, 3L, 8L, 3L, 4L, 6L, 3L, 10L, 6L, 2L, 7L, 5L, 5L, 5L, 2L, 3L, NA, NA, 5L, 5L, NA, 2L, 3L, 3L, 6L, NA, 2L, 1L, 2L, 4L, 3L, 3L, 6L, NA, 2L, NA, 6L, 5L, NA, 6L, 2L, 5L, 2L, 2L, NA, NA, 2L, 3L, NA, 2L, 3L, 2L, 3L, 2L, 10L, NA, NA, 11L, 25L, NA, NA, NA, 7L, 8L, 3L, 2L, 6L, 5L, 4L, 2L, 3L, 4L, 2L, 2L, 2L, 2L, 5L, 2L, 10L, NA, 8L, 2L, 2L, 7L, NA, NA, NA, 7L, 2L, 2L, 3L, 2L, 1L, 2L, 1L, NA, 2L, 5L, 8L, 1L, NA, 5L, NA, NA, 1L, NA, 3L, 2L, 5L, 4L, 4L, 5L, 5L, 5L, NA, 4L, 4L, 4L, 2L, 26L, 8L, 5L, 5L, 8L, NA, 4L, NA, NA, NA, NA, NA, 22L, NA, 3L, 2L, 3L, 2L, NA, NA, NA, NA, 5L, 6L, 7L, 3L, NA, 6L, 2L, 2L, 2L, 2L, 5L, 5L, 4L, 4L, 4L, 3L, NA, 4L, NA, NA, 6L, 5L, 8L, 5L, NA, 9L, NA, NA, 2L, 2L, 2L, 5L, 4L, NA, NA, 10L, 2L, 10L, 10L, 5L, 3L, 4L, NA, 4L, NA, 2L, NA, NA, 15L, NA, NA, 4L, 4L, 5L, NA, 18L, NA, 10L, NA, 6L, NA, 1L, 3L, 8L, 5L, 3L, 3L, 1L, NA, 2L, 2L, 10L, 2L, 1L, 3L, NA, 2L, 3L, 2L, 1L, 6L, 8L, 10L, 4L, NA, 6L, 1L, 4L, 3L, NA, 2L, 2L, 2L, 30L, NA, NA, NA, NA, NA, 3L, NA, NA, NA, NA, 1L, NA, NA, NA, 1L, 3L, 3L, NA, NA, 1L, 10L, NA, NA, NA, NA, 2L, 1L, NA, 6L, NA, NA, NA, NA, 1L, 4L, 4L, 4L, 4L, 5L, 4L, 1L, 2L, 19L, 19L, 19L, 19L, 3L, NA, NA, NA, 30L, NA, 2L, 2L, NA, 1L, 10L, 2L, 8L, 7L, 15L, 2L, 1L, 4L, 3L, 3L, 1L, 14L, 14L, 11L, 14L, 14L, 2L, 10L, NA, 7L, NA, 11L, 6L, 2L, NA, 4L, NA, NA, 2L, NA, NA, 8L, 4L, NA, 2L, 1L, NA, 9L, 4L, 2L, 3L, 9L, 90L, NA, NA, NA, 3L, 3L, 4L, 3L, 3L, 4L, 20L, NA, 2L, 2L, 35L, 3L, 6L, 6L, 6L, 10L, NA, NA, 2L, NA, NA, NA, NA, NA, 10L, 1L, 5L, 5L, 6L, NA, 10L, 10L, 24L, 1L, NA, NA, 3L, 1L, NA, 8L, NA, 6L, 19L, 19L, 1L, 4L, 3L, 1L, 1L, 4L, 11L, 1L, 2L, 10L, 2L, 8L, 3L, 16L, 15L, NA, 5L, 5L, 1L, 1L, 2L, 1L, NA, 5L, 1L, NA, 11L, NA, NA, NA, NA, 15L, NA, 15L, NA, NA, NA, 3L, 10L, 10L, 9L, 7L, 10L, 1L, NA, 20L, 3L, 2L, 2L, 4L, NA, 2L, NA, NA, NA, 6L, 10L, NA, 3L, 2L, NA, 2L, 1L, 4L, 4L, 1L, 4L, 1L, 5L, 20L, 50L, 2L, 10L, 5L, 1L, 2L, 2L, NA, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 1L, 10L, NA, NA, 6L, NA, NA, 6L, 3L, 1L, 3L, NA, 3L, 3L, 2L, 10L, 2L, NA, NA, 2L, 2L, 3L, 20L, 3L, 1L, 3L, 1L, 1L, 10L, 9L, 10L, 3L, 2L, NA, 1L, 35L, NA, 3L, 3L, 2L, 2L, 1L, NA, 1L, 1L, 4L, 2L, 2L, 0L, 4L, 2L, 5L, 2L, 2L, 1L, 12L, 1L, 1L, 3L, 3L, 2L, NA, NA, NA, 2L, 2L, 1L, 1L, 5L, 1L, 2L, 2L, 3L, 8L, 8L, 3L, 3L, 5L, 5L, 5L, 6L, 3L, NA, 1L, 3L, 1L, 1L, 2L, 3L, 5L, 5L, 5L, 7L, 7L, 7L, 7L, 1L, 4L, 3L, 2L, 4L, 12L, 9L, 9L, 5L, 9L, 10L, 5L, 20L, 8L, 4L, 1L, 10L, NA, 10L, 10L, 3L, 8L, 8L, 1L, 10L, NA, 5L, NA, 5L, 4L, 4L, 1L, 1L, 3L, 1L, 4L, 3L, NA, 8L, 2L, 3L, 3L, 3L, 3L, 2L, 6L, 6L, 3L, 4L, 6L, 5L, 5L, NA, 2L, 2L, 2L, 2L, 8L, 8L, 8L, 8L, 8L, 10L, 1L, 4L, 3L, 4L, 10L, 8L, 3L, 10L, 8L, 2L, 8L, 8L, 5L, 10L, NA, NA, 14L, 14L, 10L, NA, 15L, 12L, 5L, 2L, 9L, 9L, 6L, 1L, 3L, 2L, 2L, 2L, NA, NA, 8L, 3L, NA, 15L, 3L, 3L, 10L, 8L, 8L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, NA, 5L, 5L, 5L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 3L, 4L, 4L, 4L, 6L, 4L, 4L, 5L, 4L, 3L, 2L, 1L, 8L, 1L, 1L, 1L, 16L, NA, 2L, 8L, 12L, 3L, 8L, 8L, 8L, 8L, 15L, 5L, 15L, 10L, 3L, 3L, 1L, 2L, 3L, 4L, 6L, 6L, 1L, 15L, 1L, 3L, NA, 4L, 2L, 150L, 150L, 2L, 2L, 1L, 1L, 10L, 6L, 1L, 1L, 10L, 1L, 1L, 2L, 7L, 7L, 2L, 2L, 1L, 1L, NA, 2L, 2L, 4L, 4L, 2L, 1L, 1L, 4L, 4L, 4L, 3L, 2L, 5L, 6L, 6L, 6L, 6L, 9L, 9L, NA, 1L, 1L, 3L, 15L, 20L, 12L, 20L, 1L, 1L, 2L, 5L, 2L, 3L, 2L, 1L, 1L, NA, 20L, NA, NA, 22L, NA, 20L, 5L, 8L, 1L, 5L, 2L, 2L, 5L, 2L, 1L, NA, NA, NA, NA, 6L, 2L, 10L, 3L, 2L, NA, 6L, 1L, 21L, 6L, 6L, 6L, 3L, 24L, 10L, 20L, 4L, 10L, 5L, 2L, 1L, 3L, 3L, 1L, 10L, 3L, 3L, 3L, 7L, 6L, 3L, 4L, 19L, NA, 1L, 1L, 1L, 10L, 2L, 2L, 3L, 2L, NA, 1L, 5L, 8L, 3L, 1L, 1L, 20L, 20L, 1L, 1L, 1L, 4L, 4L, 4L, 15L, NA, 15L, 5L, 3L, 6L, 6L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 4L, 8L, 15L, 8L, 6L), numero_vezes_medic = 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1L, 1L)), row.names = c(NA, 1000L), class = "data.frame") ```

The model they want to specify is:

modelo <- "
# latents
morbidity =~ quant_doe_cron + multimorbidade
psy_med =~ medic_psi + quant_med_cron + tempo_uso_medicamento
# mas aqui só para med psi

# regression
psy_med ~ idade + imc
morbidity ~ psy_med + idade + imc

idade ~~ idade
imc ~~ imc

So far they tried to add the algebras by including them in a RAM model, and then calling from a container model, similar to first link above:

test <- umxLav2RAM(modelo, data = data)
test  <- xmuValues(test,onlyTouchZeros = T )

test <- mxModel(test,name = "examp1",
        mxAlgebra(A %*% S %*% t(A),name="Covar1" ),     
        mxFitFunctionML(vector = TRUE))

example1a <- mxModel(model="Container", test,
    # attach the data to the model
    mxData(data, type="raw"),
    # Algebra for weights
    mxAlgebra(-2 * sum(data.peso_rake %x% log(examp1.fitfunction)), name = "obj"),
    mxAlgebraObjective("obj")
)

model1a <- mxRun(example1a)

umxSummary(model1a)

But unfortunately OpenMx is not picking up her intention of generating the likelihood per line and the estimates are coming out identical to the unweighted attempts.

Best,

Luis

mcneale commented 1 year ago

Perhaps take a look at ?mxData and see whether the weights= argument will make it easy to do what you want?

lf-araujo commented 1 year ago

Thanks Mike,

A bit embarrassed for how simple was this. Here is the solution:

test <- umxLav2RAM(modelo)

m1 <- mxModel(test, umxPath(means = test$manifestVars), mxData(data, type = "raw", weight = "peso_rake"))

m1run <- mxRun(m1) umxSummary(m1run) summary(m1run)

Table: Parameter loadings for model 'm1'

name Estimate SE type
20 56.7 2236.26 custom
21 26.27 1796.86 custom
22 10.42 25971.68 custom
23 2.3 42004.86 custom
24 0.09 210726.78 custom
25 12.28 19975.45 custom
26 69.71 1248.06 custom
5 morbidity_to_quant_doe_cron 1 0 Factor loading
6 morbidity_to_multimorbidade 0.18 352813.64 Factor loading
7 psy_med_to_medic_psi 1 0 Factor loading
8 psy_med_to_quant_med_cron 314.65 641.43 Factor loading
9 psy_med_to_tempo_uso_medicamento 1743.29 41.57 Factor loading
10 psy_med_to_morbidity 269.09 768.62 Factor to factor
18 morbidity_with_morbidity 1 22201.66 Factor Variance
19 psy_med_with_psy_med 0 165105361.45 Factor Variance
1 idade_to_morbidity 0.01 1389754.68 Manifest path
2 idade_to_psy_med 0 108885568.05 Manifest path
3 imc_to_morbidity 0 706100.3 Manifest path
4 imc_to_psy_med 0 55384264.1 Manifest path
11 idade_with_idade 75.97 415.86 Residual
12 imc_with_imc 117.64 166.54 Residual
13 quant_doe_cron_with_quant_doe_cron -0.54 26100.24 Residual
14 multimorbidade_with_multimorbidade 0.25 27824.08 Residual
15 medic_psi_with_medic_psi 0.01 2115454.03 Residual
16 quant_med_cron_with_quant_med_cron 0.44 19091.36 Residual
17 tempo_uso_medicamento_with_tempo_uso_medicamento 196.94 28.33 Residual

Model Fit: χ²(649350454) = 3028746718.02, p < 0.001; CFI = -49307.368; TLI = 0.998; RMSEA = 0.011

tbates commented 1 year ago

fyi, umxGxE_window (and other twin functions, as this is implemented as an xmu helper) does this via a a vector mode fitFunction... so your original idea wasn't far off :-)

tbates commented 1 year ago

It's crucial that modifications are tested (the weight parameter might not be working as expected, or at least might be exposing some more problems in OpenMx ignoring that feature.

Let me know if you can get umx_make("path/to/your/umx/folder") working. Happy to help. And then also add a test example to the help for umxRAM, and preferably a test_that file in the tests folder.

mcneale commented 1 year ago

I strongly suspect that mxRefModels() ignores weights. Will inform OpenMx team.

lf-araujo commented 1 year ago

Thanks Tim and Mike, I will add tests, but also a warning of unreliable fit stats. In testing I get clearly problematic fit. If I encounter problems in adding tests will ask here.

tbates commented 1 year ago

I'd not heard of it and can't see when it was added. The example in OpenMx using it uses custom compute plans and mxComputeNewtonRaphson

Is it intended for general use? If not, the Rd at least should be marked to reflect that.

According to a help post on openmx forum, it should act like simply duplicating rows (at least for FIML, and not for WLS... but doesn't show an error). But a simple case of integer increases in row counts should not induce any instability in fit.

If we can't show that, I'll revert the changes.

@lf-araujo A work around should be possible by making the RAM model use vector and then optimizing on the weight vector.

mcneale commented 1 year ago

It’s actually not in an obvious place. I was expecting it to be in mxExpectation or mxFitFunction, but it was only because I knew it existed that I could find it.