Hello, stata community. I would like your help once again,
I am using mixed effects linear regression to evaluate food consumption as an exposure in the first years of life and child growth at age 5 as an outcome. In this example, I have as an exposure variable food consumption up to the first 3 months of life (cenario - variable with 3 categories) and as an outcome variable, repeated measurements of height for age (Variable z_altidade) in these children up to 5 years of age (however, the amount of measurements per child (id) varies (2-46 measurements).To find out how the scenario variable (food consumption in the first 3 months of life) affects height for age over the years, I did the following step by step:
*I tested the best function (model fit): linear, quadratic or cubic
*generate the terms quadratic and cubic age
gen idade_meses_en2= idade_meses_en^2
gen idade_meses_en3= idade_meses_en^3
*linear function
mixed z_altidade idade_meses_en || id: idade_meses_en , cov(uns) variance
estat ic
* AIC: 1674308
* BIC: 1674376
* quadratic function
mixed z_altidade idade_meses_en idade_meses_en2 || id: idade_meses_en, cov(uns) variance
estat ic
* AIC: 1673929
* BIC: 1674008
* cubic function
mixed z_altidade idade_meses_en idade_meses_en2 idade_meses_en3 || id: idade_meses_en, cov(uns) variance
estat ic
* AIC: 1673912
* BIC: 1674002
*model that best fits is the cubic model - lower AIC and BIC values. In this way, I ran the final model considering the quadratic and cubic terms, including interactions between age (idade_meses_en/ idade_meses_en2/ idade_meses_en3) and cenario. I found the following output:
. mixed z_altidade idade_meses_en2 idade_meses_en3 c.idade_meses_en## i.cenario c.idade_meses_en2## i.cenario c.idade_meses_en3## i.cenario|| id: idade_meses_en, cov(un)
note: idade_meses_en2 omitted because of collinearity.
note: idade_meses_en3 omitted because of collinearity.
Performing EM optimization ...
Performing gradient-based optimization:
Iteration 0: log likelihood = -836287.86
Iteration 1: log likelihood = -836286.06
Iteration 2: log likelihood = -836286.06
Computing standard errors ...
Mixed-effects ML regression Number of obs = 597,237
Group variable: id Number of groups = 127,422
Obs per group:
min = 2
avg = 4.7
max = 46
Wald chi2(11) = 3329.97
Log likelihood = -836286.06 Prob > chi2 = 0.0000
-----------------------------------------------------------------------------------------------
z_altidade | Coefficient Std. err. z P>|z| [95% conf. interval]
------------------------------+----------------------------------------------------------------
idade_meses_en2 | .0007663 .0000795 9.64 0.000 .0006106 .0009221
idade_meses_en3 | -2.10e-06 1.47e-06 -1.43 0.154 -4.98e-06 7.87e-07
idade_meses_en | -.0346841 .0010568 -32.82 0.000 -.0367555 -.0326128
|
cenario |
Substitutos do leite materno | -.3389908 .0113932 -29.75 0.000 -.361321 -.3166606
IA precoce | -.129324 .0185984 -6.95 0.000 -.1657761 -.0928719
|
cenario#c.idade_meses_en |
Substitutos do leite materno | .0657829 .0022711 28.97 0.000 .0613317 .0702342
IA precoce | .0261245 .0034945 7.48 0.000 .0192754 .0329736
|
idade_meses_en2 | 0 (omitted)
|
cenario#c.idade_meses_en2 |
Substitutos do leite materno | -.0027962 .0001651 -16.94 0.000 -.0031199 -.0024726
IA precoce | -.0011431 .0002284 -5.00 0.000 -.0015908 -.0006954
|
idade_meses_en3 | 0 (omitted)
|
cenario#c.idade_meses_en3 |
Substitutos do leite materno | .0000361 2.97e-06 12.16 0.000 .0000303 .000042
IA precoce | .0000136 3.71e-06 3.66 0.000 6.30e-06 .0000208
|
_cons | .2197004 .005296 41.48 0.000 .2093204 .2300803
-----------------------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects parameters | Estimate Std. err. [95% conf. interval]
-----------------------------+------------------------------------------------
id: Unstructured |
var(idade_meses_en) | .0059077 .00007 .0057722 .0060465
var(_cons) | 1.669051 .0085306 1.652415 1.685855
cov(idade_meses_en,_cons) | -.0586498 .0006555 -.0599346 -.057365
-----------------------------+------------------------------------------------
var(Residual) | .5247229 .0012235 .5223304 .5271263
------------------------------------------------------------------------------
LR test vs. linear model: chi2(3) = 3.7e+05 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.
It is known that the interpretation is not so literal and that graphic representation can help in the interpretation of findings. Furthermore, I am interested in showing the results of this model at specific ages. To do this, I used the margins and marginsplot commands, but I think they don't work well because the cenario categories appear as a linear function over time, with the model that best fits being the cubic model. How can I do this? Should I then plot the interaction of scenario with cubic age? The graph below shows the result of the syntax used.
margins i.cenario, at(idade_meses_en=(0(6)60)) vsquish
marginsplot, name(modelo_teste1, replace) x(idade_meses_en) noci
What I would like: model growth curves for the height/age index (y) at specific ages (x), considering categories of the cenario variable.
I use version 17 of Stata. I greatly appreciate your help
Here's a snippet of the database as an example
----------------------- copy starting from the next line -----------------------
------------------ copy up to and including the previous line ------------------
I am using mixed effects linear regression to evaluate food consumption as an exposure in the first years of life and child growth at age 5 as an outcome. In this example, I have as an exposure variable food consumption up to the first 3 months of life (cenario - variable with 3 categories) and as an outcome variable, repeated measurements of height for age (Variable z_altidade) in these children up to 5 years of age (however, the amount of measurements per child (id) varies (2-46 measurements).To find out how the scenario variable (food consumption in the first 3 months of life) affects height for age over the years, I did the following step by step:
*I tested the best function (model fit): linear, quadratic or cubic
*generate the terms quadratic and cubic age
gen idade_meses_en2= idade_meses_en^2
gen idade_meses_en3= idade_meses_en^3
*linear function
mixed z_altidade idade_meses_en || id: idade_meses_en , cov(uns) variance
estat ic
* AIC: 1674308
* BIC: 1674376
* quadratic function
mixed z_altidade idade_meses_en idade_meses_en2 || id: idade_meses_en, cov(uns) variance
estat ic
* AIC: 1673929
* BIC: 1674008
* cubic function
mixed z_altidade idade_meses_en idade_meses_en2 idade_meses_en3 || id: idade_meses_en, cov(uns) variance
estat ic
* AIC: 1673912
* BIC: 1674002
*model that best fits is the cubic model - lower AIC and BIC values. In this way, I ran the final model considering the quadratic and cubic terms, including interactions between age (idade_meses_en/ idade_meses_en2/ idade_meses_en3) and cenario. I found the following output:
. mixed z_altidade idade_meses_en2 idade_meses_en3 c.idade_meses_en## i.cenario c.idade_meses_en2## i.cenario c.idade_meses_en3## i.cenario|| id: idade_meses_en, cov(un)
note: idade_meses_en2 omitted because of collinearity.
note: idade_meses_en3 omitted because of collinearity.
Performing EM optimization ...
Performing gradient-based optimization:
Iteration 0: log likelihood = -836287.86
Iteration 1: log likelihood = -836286.06
Iteration 2: log likelihood = -836286.06
Computing standard errors ...
Mixed-effects ML regression Number of obs = 597,237
Group variable: id Number of groups = 127,422
Obs per group:
min = 2
avg = 4.7
max = 46
Wald chi2(11) = 3329.97
Log likelihood = -836286.06 Prob > chi2 = 0.0000
-----------------------------------------------------------------------------------------------
z_altidade | Coefficient Std. err. z P>|z| [95% conf. interval]
------------------------------+----------------------------------------------------------------
idade_meses_en2 | .0007663 .0000795 9.64 0.000 .0006106 .0009221
idade_meses_en3 | -2.10e-06 1.47e-06 -1.43 0.154 -4.98e-06 7.87e-07
idade_meses_en | -.0346841 .0010568 -32.82 0.000 -.0367555 -.0326128
|
cenario |
Substitutos do leite materno | -.3389908 .0113932 -29.75 0.000 -.361321 -.3166606
IA precoce | -.129324 .0185984 -6.95 0.000 -.1657761 -.0928719
|
cenario#c.idade_meses_en |
Substitutos do leite materno | .0657829 .0022711 28.97 0.000 .0613317 .0702342
IA precoce | .0261245 .0034945 7.48 0.000 .0192754 .0329736
|
idade_meses_en2 | 0 (omitted)
|
cenario#c.idade_meses_en2 |
Substitutos do leite materno | -.0027962 .0001651 -16.94 0.000 -.0031199 -.0024726
IA precoce | -.0011431 .0002284 -5.00 0.000 -.0015908 -.0006954
|
idade_meses_en3 | 0 (omitted)
|
cenario#c.idade_meses_en3 |
Substitutos do leite materno | .0000361 2.97e-06 12.16 0.000 .0000303 .000042
IA precoce | .0000136 3.71e-06 3.66 0.000 6.30e-06 .0000208
|
_cons | .2197004 .005296 41.48 0.000 .2093204 .2300803
-----------------------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects parameters | Estimate Std. err. [95% conf. interval]
-----------------------------+------------------------------------------------
id: Unstructured |
var(idade_meses_en) | .0059077 .00007 .0057722 .0060465
var(_cons) | 1.669051 .0085306 1.652415 1.685855
cov(idade_meses_en,_cons) | -.0586498 .0006555 -.0599346 -.057365
-----------------------------+------------------------------------------------
var(Residual) | .5247229 .0012235 .5223304 .5271263
------------------------------------------------------------------------------
LR test vs. linear model: chi2(3) = 3.7e+05 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.
It is known that the interpretation is not so literal and that graphic representation can help in the interpretation of findings. Furthermore, I am interested in showing the results of this model at specific ages. To do this, I used the margins and marginsplot commands, but I think they don't work well because the cenario categories appear as a linear function over time, with the model that best fits being the cubic model. How can I do this? Should I then plot the interaction of scenario with cubic age? The graph below shows the result of the syntax used.
margins i.cenario, at(idade_meses_en=(0(6)60)) vsquishmarginsplot, name(modelo_teste1, replace) x(idade_meses_en) noci
What I would like: model growth curves for the height/age index (y) at specific ages (x), considering categories of the cenario variable.
I use version 17 of Stata. I greatly appreciate your help
Here's a snippet of the database as an example
----------------------- copy starting from the next line -----------------------
Code:
* Example generated by -dataex-. For more info, type help dataex clear input long id byte sexo_ca int ano_acomp_ca float(idade_ca idade_meses_ca cenario alt peso idade_en ano_acomp_en imc_calc idade_meses_en z_altidade seq_CAEN2 max_CAEN2 idade_meses_en2 idade_meses_en3) 10576959 0 2017 .05749487 .6899384 1 54 4 .07939767 2017 13.717422 .952772 2.604984 1 6 .9077745 .8649022 10576959 0 2017 .05749487 .6899384 1 59 5.925 .24640657 2017 17.020971 2.956879 .9491886 2 6 8.743133 25.852385 10576959 0 2017 .05749487 .6899384 1 66 7 .4791239 2018 16.069788 5.749487 .8883988 3 6 33.0566 190.0585 10576959 0 2017 .05749487 .6899384 1 69 8 .5968515 2018 16.803192 7.162218 .7397134 4 6 51.29736 367.4029 10576959 0 2017 .05749487 .6899384 1 73 10 .8651608 2018 18.765247 10.38193 .6152673 5 6 107.78445 1119.0106 10576959 0 2017 .05749487 .6899384 1 75 10 1.1334702 2018 17.777779 13.601643 -.08274923 6 6 185.0047 2516.368 11055885 1 2016 .16974674 2.0369608 0 62 6.55 .25188228 2016 17.039543 3.022587 .27920705 1 5 9.136034 27.61446 11055885 1 2016 .16974674 2.0369608 0 65 7 .3422314 2016 16.568047 4.106776 .5353787 2 5 16.86561 69.26329 11055885 1 2016 .16974674 2.0369608 0 66 7.3 .4188912 2016 16.758495 5.026694 .04612643 3 5 25.26765 127.01274 11055885 1 2016 .16974674 2.0369608 0 66 7.3 .5010267 2016 16.758495 6.012321 -.7585881 4 5 36.148 217.33334 11055885 1 2016 .16974674 2.0369608 0 69 7.6 .5722108 2016 15.963033 6.866529 .6430932 5 5 47.14923 323.7516 11532240 1 2016 .06297057 .7556468 1 58 5.19 .12594114 2016 15.428062 1.5112936 1.682775 1 6 2.2840085 3.4518075 11532240 1 2016 .06297057 .7556468 1 59 11.65 .1943874 2016 33.467392 2.3326488 .28748262 2 6 5.44125 12.692526 11532240 1 2016 .06297057 .7556468 1 62 6.5 .25188228 2016 16.90947 3.022587 .27920705 3 6 9.136034 27.61446 11532240 1 2016 .06297057 .7556468 1 66 8.14 .386037 2016 18.686869 4.6324434 1.0159707 4 6 21.45953 99.41006 11532240 1 2016 .06297057 .7556468 1 75 11.17 .7556468 2017 19.85778 9.067762 1.3512913 5 6 82.22431 745.5905 11532240 1 2016 .06297057 .7556468 1 81 14 1.0622861 2017 21.33821 12.747434 2.209877 6 6 162.49707 2071.4207 11673070 1 2016 .013689253 .16427104 0 50 3.36 .013689253 2016 13.44 .16427104 .06116878 1 2 .026984975 .00443285 11673070 1 2016 .013689253 .16427104 0 80 11.7 1.0841889 2017 18.28125 13.010267 1.2701492 2 2 169.26706 2202.2097 17647115 0 2015 .12594114 1.5112936 0 57.8 5.3 .12594114 2015 15.864275 1.5112936 2.1045804 1 2 2.2840085 3.4518075 17647115 0 2015 .12594114 1.5112936 0 62 6.1 .20807666 2015 15.868886 2.49692 2.4225495 2 2 6.234609 15.56732 27974818 1 2015 .09034908 1.0841889 0 49 2.32 .09034908 2015 9.662641 1.0841889 -2.940797 1 5 1.1754657 1.2744268 27974818 1 2015 .09034908 1.0841889 0 55 5.2 .2053388 2015 17.190083 2.4640656 -1.712049 2 5 6.071619 14.960867 27974818 1 2015 .09034908 1.0841889 0 61 7.26 .3641342 2015 19.510885 4.36961 -1.3869892 3 5 19.09349 83.4311 27974818 1 2015 .09034908 1.0841889 0 61 7.23 .4517454 2015 19.43026 5.420945 -2.3218367 4 5 29.38664 159.30336 27974818 1 2015 .09034908 1.0841889 0 68 9.65 .58590007 2015 20.869377 7.030801 -.5363694 5 5 49.43216 347.5477 29640336 1 2016 .1889117 2.2669406 2 74 10.45 .9226557 2017 19.083273 11.07187 -.23131172 1 6 122.58628 1357.2593 29640336 1 2016 .1889117 2.2669406 2 83 12.03 1.4592744 2018 17.462622 17.511293 .6632237 2 6 306.6454 5369.757 29640336 1 2016 .1889117 2.2669406 2 85 13.56 1.670089 2018 18.768167 20.04107 .2844399 3 6 401.6444 8049.384 29640336 1 2016 .1889117 2.2669406 2 97 16.3 2.2997947 2018 17.323837 27.597536 2.2812006 4 6 761.624 21018.945 29640336 1 2016 .1889117 2.2669406 2 98 17.6 2.587269 2019 18.325699 31.04723 1.5428514 5 6 963.9305 29927.37 29640336 1 2016 .1889117 2.2669406 2 98 18.1 2.691307 2019 18.846315 32.29569 1.3168868 6 6 1043.0115 33684.773 32832552 0 2015 .027378507 .3285421 0 49 3.43 .027378507 2015 14.285714 .3285421 -.07929426 1 4 .1079399 .0354628 32832552 0 2015 .027378507 .3285421 0 50 4 .08487337 2015 16 1.0184804 -1.8867936 2 4 1.0373024 1.0564722 32832552 0 2015 .027378507 .3285421 0 57 4.85 .1889117 2015 14.92767 2.2669406 -.03305185 3 4 5.139019 11.649852 32832552 0 2015 .027378507 .3285421 0 57 6.15 .3367556 2015 18.928902 4.0410676 -2.3515866 4 4 16.330227 65.991554 33695767 1 2019 .03011636 .3613963 0 50 3.56 .03011636 2019 14.24 .3613963 .06116878 1 5 .13060729 .047201 33695767 1 2019 .03011636 .3613963 0 51.5 5.112 .10130048 2019 19.2742 1.2156057 -1.6564716 2 5 1.4776973 1.7962973 33695767 1 2019 .03011636 .3613963 0 51.5 5.112 .1670089 2019 19.2742 2.0041068 -3.461639 3 5 4.0164437 8.049382 33695767 1 2019 .03011636 .3613963 0 57.5 6.59 .2984257 2019 19.93195 3.581109 -1.9219668 4 5 12.82434 45.92536 33695767 1 2019 .03011636 .3613963 0 71 9.43 .8104038 2019 18.706606 9.724846 -.4318246 5 5 94.57262 919.7042 33718216 0 2015 .13141684 1.577002 0 51 3.97 .13141684 2015 15.26336 1.577002 -1.375079 1 2 2.4869354 3.9219024 33718216 0 2015 .13141684 1.577002 0 59 5.46 .29021218 2015 15.685148 3.482546 -.3814132 2 2 12.12813 42.23677 34955417 0 2016 .03011636 .3613963 0 51 3.94 .03011636 2016 15.14802 .3613963 .9944171 1 4 .13060729 .047201 34955417 0 2016 .03011636 .3613963 0 57 5.2 .1505818 2016 16.004925 1.8069816 1.695209 2 4 3.265182 5.900125 34955417 0 2016 .03011636 .3613963 0 70 8.6 .6324435 2016 17.551022 7.589323 1.1716125 3 4 57.59782 437.1284 34955417 0 2016 .03011636 .3613963 0 80 10 1.486653 2017 15.625 17.839836 .11547854 4 4 318.2598 5677.702 36421115 0 2018 .12046544 1.4455853 0 54 4.34 .12046544 2018 14.883403 1.4455853 .16006504 1 4 2.0897167 3.020864 36421115 0 2018 .12046544 1.4455853 0 58 5.39 .25735798 2018 16.022593 3.088296 -.8564585 2 4 9.537572 29.454844 36421115 0 2018 .12046544 1.4455853 0 70 10.82 .8870637 2019 22.08163 10.644764 -.6005127 3 4 113.311 1206.1688 36421115 0 2018 .12046544 1.4455853 0 74 12 1.1170431 2019 21.913805 13.404518 -.4630341 4 4 179.6811 2408.5386 37565537 1 2017 .1615332 1.9383984 1 60 5.3 .26009583 2017 14.722222 3.12115 -.6990924 1 2 9.741577 30.404924 37565537 1 2017 .1615332 1.9383984 1 71 7.1 .55852157 2018 14.084507 6.702259 1.5775474 2 2 44.92028 301.06732 39098684 1 2016 .10677618 1.281314 1 46 2.6 .10677618 2016 12.287334 1.281314 -4.481988 1 2 1.641766 2.103618 39098684 1 2016 .10677618 1.281314 1 49 44.95 .238193 2016 187.21367 2.858316 -4.711346 2 2 8.169971 23.35236 39121618 1 2016 .2108145 2.529774 0 59 5.6 .2108145 2016 16.087332 2.529774 .28748262 1 9 6.399757 16.189941 39121618 1 2016 .2108145 2.529774 0 63 6.6 .3258042 2016 16.628874 3.909651 .7683568 2 9 15.28537 59.76046 39121618 1 2016 .2108145 2.529774 0 67 7.5 .4791239 2016 16.707508 5.749487 .51971906 3 9 33.0566 190.0585 39121618 1 2016 .2108145 2.529774 0 69 8.1 .5722108 2016 17.013233 6.866529 .6430932 4 9 47.14923 323.7516 39121618 1 2016 .2108145 2.529774 0 70 8.7 .6899384 2016 17.755102 8.279261 -.27177352 5 9 68.54616 567.5115 39121618 1 2016 .2108145 2.529774 0 70 9.2 .7665982 2016 18.775509 9.199179 -.8776035 6 9 84.62489 778.4795 39121618 1 2016 .2108145 2.529774 0 74 9.95 .8678987 2016 18.170197 10.414784 .3145875 7 9 108.46774 1129.6681 39121618 1 2016 .2108145 2.529774 0 76 9.95 .9582478 2016 17.226454 11.498974 .6273015 8 9 132.2264 1520.468 39121618 1 2016 .2108145 2.529774 0 77 10.3 1.0349076 2016 17.372238 12.41889 .5265452 9 9 154.22885 1915.3513 39223276 1 2018 .07939767 .952772 0 53 4.35 .07939767 2018 15.485938 .952772 1.6458665 1 2 .9077745 .8649022 39223276 1 2018 .07939767 .952772 0 56.5 6.45 .2327173 2018 20.205183 2.792608 -.9622246 2 2 7.798658 21.778593 40501789 1 2017 .11772759 1.412731 2 53 4.4 .12320329 2017 15.663938 1.4784395 -.8858762 1 3 2.1857831 3.231548 40501789 1 2017 .11772759 1.412731 2 60 7 .3778234 2017 19.444445 4.533881 -1.8675812 2 3 20.55608 93.19881 40501789 1 2017 .11772759 1.412731 2 60 8.2 .4490075 2017 22.777777 5.388091 -2.795429 3 3 29.03152 156.42447 40863444 0 2019 .019164955 .22997946 0 51 3 .019164955 2019 11.534025 .22997946 .9944171 1 5 .05289055 .01216374 40863444 0 2019 .019164955 .22997946 0 56 5 .13689254 2019 15.943877 1.6427104 1.1834943 2 5 2.6984975 4.4328504 40863444 0 2019 .019164955 .22997946 0 62 5.6 .3258042 2019 14.568158 3.909651 1.043723 3 5 15.28537 59.76046 40863444 0 2019 .019164955 .22997946 0 66 7.1 .54757017 2019 16.299356 6.570842 .118645 4 5 43.17596 283.70242 40863444 0 2019 .019164955 .22997946 0 68 7.5 .6516085 2019 16.219723 7.819302 .3078144 5 5 61.14148 478.08365 40886644 0 2018 .06297057 .7556468 0 53 4.21 .06297057 2018 14.98754 .7556468 2.0681286 1 4 .5710021 .4314759 40886644 0 2018 .06297057 .7556468 0 62 6.06 .28199863 2018 15.764828 3.3839836 1.043723 2 4 11.451345 38.75116 40886644 0 2018 .06297057 .7556468 0 64 7.37 .3969884 2018 17.993164 4.76386 .8824854 3 4 22.694365 108.11278 40886644 0 2018 .06297057 .7556468 0 68 8.25 .5557837 2018 17.841696 6.669404 1.0010984 4 4 44.48095 296.66144 52379999 0 2016 .23545517 2.825462 0 57 4 .23545517 2016 12.31148 2.825462 -.03305185 1 22 7.983236 22.55633 52379999 0 2016 .23545517 2.825462 0 60 5.1 .3915127 2016 14.166666 4.698152 -.9655557 2 22 22.072634 103.70058 52379999 0 2016 .23545517 2.825462 0 62 5.32 .4845996 2016 13.83975 5.815195 -.9155464 3 22 33.816494 196.6495 52379999 0 2016 .23545517 2.825462 0 68 6.1 .6433949 2016 13.19204 7.720739 .3078144 4 22 59.60982 460.2318 52379999 0 2016 .23545517 2.825462 0 69 70.35 .9637235 2017 147.76308 11.564682 -1.4959625 5 22 133.74187 1546.6823 52379999 0 2016 .23545517 2.825462 0 74 8.4 1.3114306 2017 15.339664 15.737167 -1.2813606 6 22 247.65843 3897.4424 52379999 0 2016 .23545517 2.825462 0 78 8.6 1.486653 2017 14.135438 17.839836 -.5865136 7 22 318.2598 5677.702 52379999 0 2016 .23545517 2.825462 0 84 8.9 1.9000684 2018 12.613379 22.80082 -.19357643 8 22 519.87744 11853.633 52379999 0 2016 .23545517 2.825462 0 83 9.8 2.017796 2018 14.225577 24.21355 -.8416084 9 22 586.29614 14196.312 52379999 0 2016 .23545517 2.825462 0 87 9.8 2.2669404 2018 12.94755 27.203285 -.3794459 10 22 740.0187 20130.94 52379999 0 2016 .23545517 2.825462 0 86 10 2.554415 2018 13.520823 30.652977 -1.3256345 11 22 939.605 28801.69 52379999 0 2016 .23545517 2.825462 0 89 10 2.666667 2018 12.62467 32 -.879957 12 22 1024 32768 52379999 0 2016 .23545517 2.825462 0 89 10 2.7460644 2019 12.62467 32.952774 -.879957 13 22 1085.8854 35782.934 52379999 0 2016 .23545517 2.825462 0 89 10 2.806297 2019 12.62467 33.675564 -1.0684968 14 22 1134.0436 38189.56 52379999 0 2016 .23545517 2.825462 0 89 10.5 2.902122 2019 13.255902 34.825462 -1.2489586 15 22 1212.8129 42236.77 52379999 0 2016 .23545517 2.825462 0 89 10.9 2.973306 2019 13.760888 35.679672 -1.422331 16 22 1273.039 45421.61 52379999 0 2016 .23545517 2.825462 0 90 11 3.148528 2019 13.580247 37.78234 -1.489887 17 22 1427.5052 53934.49 52379999 0 2016 .23545517 2.825462 0 93 11 3.326489 2019 12.718233 39.91787 -1.0379856 18 22 1593.436 63606.57 52379999 0 2016 .23545517 2.825462 0 93 10.5 3.397673 2019 12.140132 40.77208 -1.1928927 19 22 1662.362 67777.95 52379999 0 2016 .23545517 2.825462 0 92 11 3.4770706 2019 12.99622 41.72485 -1.5909727 20 22 1740.9628 72641.41 52379999 0 2016 .23545517 2.825462 0 94 10.9 3.5674195 2019 12.335898 42.80904 -1.2407924 21 22 1832.6135 78452.42 52379999 0 2016 .23545517 2.825462 0 95 10.6 3.649555 2019 11.745152 43.79466 -1.1394521 22 22 1917.9724 83996.95 end label values sexo_ca sexo label def sexo 0 "feminino", modify label def sexo 1 "masculino", modify label values cenario cenario label def cenario 0 "LM exclusivo", modify label def cenario 1 "Substitutos do leite materno", modify label def cenario 2 "IA precoce", modify
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