Hi everyone, I need to know what is de command for the reg of Maximun Likelihood with complete information
Thanks
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What do you mean with "complete information"?
What is your model (maximum likelihood is a way to estimate coefficients of a model, it is not the model itself)?---------------------------------
Maarten L. Buis
University of Konstanz
Department of history and sociology
box 40
78457 Konstanz
Germany
http://www.maartenbuis.nl
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Perhaps Juan is referring to full information maximum likelihood (FIML) which is an alternative to multiple imputation (MI). If so, you can use sem specifying the option -mlmv- (i.e. maximum likelihood with missing values).
Res.:Code:sysuse auto, clear misstable sum regress mpg displacement weight rep78 sem (mpg <- displacement weight rep78), method(mlmv)
Code:. regress mpg displacement weight rep78 Source | SS df MS Number of obs = 69 -------------+---------------------------------- F(3, 65) = 41.57 Model | 1538.32916 3 512.776386 Prob > F = 0.0000 Residual | 801.873741 65 12.3365191 R-squared = 0.6573 -------------+---------------------------------- Adj R-squared = 0.6415 Total | 2340.2029 68 34.4147485 Root MSE = 3.5123 ------------------------------------------------------------------------------ mpg | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- displacement | .0052128 .012669 0.41 0.682 -.0200889 .0305145 weight | -.0062376 .00148 -4.21 0.000 -.0091935 -.0032818 rep78 | .5863166 .4727835 1.24 0.219 -.3578973 1.530531 _cons | 37.17348 3.193452 11.64 0.000 30.79571 43.55124 ------------------------------------------------------------------------------ . . sem (mpg <- displacement weight rep78), method(mlmv) note: Missing values found in observed exogenous variables. Using the noxconditional behavior. Specify the forcexconditional option to override this behavior. Endogenous variables Observed: mpg Exogenous variables Observed: displacement weight rep78 Fitting saturated model: Iteration 0: log likelihood = -1264.4132 Iteration 1: log likelihood = -1261.0665 Iteration 2: log likelihood = -1260.9297 Iteration 3: log likelihood = -1260.9294 Iteration 4: log likelihood = -1260.9294 Fitting baseline model: Iteration 0: log likelihood = -1300.8914 Iteration 1: log likelihood = -1300.8908 Iteration 2: log likelihood = -1300.8908 Fitting target model: Iteration 0: log likelihood = -1260.9294 Iteration 1: log likelihood = -1260.9294 Structural equation model Number of obs = 74 Estimation method = mlmv Log likelihood = -1260.9294 ----------------------------------------------------------------------------------------- | OIM | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------------------------+---------------------------------------------------------------- Structural | mpg | displacement | .007011 .0097105 0.72 0.470 -.0120212 .0260432 weight | -.0064733 .0011392 -5.68 0.000 -.008706 -.0042406 rep78 | .5652976 .4424824 1.28 0.201 -.301952 1.432547 _cons | 37.53372 2.803958 13.39 0.000 32.03806 43.02937 ------------------------+---------------------------------------------------------------- mean(displacement)| 197.2973 10.60348 18.61 0.000 176.5149 218.0797 mean(weight)| 3019.459 89.73439 33.65 0.000 2843.583 3195.336 mean(rep78)| 3.40722 .1171219 29.09 0.000 3.177665 3.636775 ------------------------+---------------------------------------------------------------- var(e.mpg)| 11.20474 1.843411 8.116366 15.46828 var(displacement)| 8320.101 1367.816 6028.259 11483.26 var(weight)| 595867.3 97959.98 431730.6 822405.9 var(rep78)| .9575829 .1618018 .6876219 1.333531 ------------------------+---------------------------------------------------------------- cov(displacement,weight)| 63010.3 10984 5.74 0.000 41482.05 84538.54 cov(displacement,rep78)| -35.92531 11.40825 -3.15 0.002 -58.28506 -13.56556 cov(weight,rep78)| -291.7716 95.44073 -3.06 0.002 -478.832 -104.7112 ----------------------------------------------------------------------------------------- Note: The LR test of model vs. saturated is not reported because the fitted model is not full rank.Comment
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As a sidelight, I would like to see more commands support a fiml option when possible.-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
StataNow Version: 19.5 MP (2 processor)
EMAIL: [email protected]
WWW: https://academicweb.nd.edu/~rwilliam/Comment
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Dear Andrew, it doesn´t matter if I am using an equations system? is the same sintaxys?Originally posted by Andrew Musau View PostComment
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It depends what model you want to fit. If it is a linear regression model, then you do that as specified in #3. Notice below that without missing values, you obtain the same coefficients with sem as you do with regress.
Code:sysuse auto, clear (1978 Automobile Data) . regress mpg headroom weight, robust Linear regression Number of obs = 74 F(2, 71) = 58.52 Prob > F = 0.0000 R-squared = 0.6523 Root MSE = 3.4594 ------------------------------------------------------------------------------ | Robust mpg | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- headroom | -.2103507 .4157304 -0.51 0.614 -1.039294 .6185922 weight | -.005898 .0006819 -8.65 0.000 -.0072577 -.0045382 _cons | 39.73567 1.951442 20.36 0.000 35.8446 43.62673 ------------------------------------------------------------------------------ . sem (mpg <- headroom weight), method(mlmv) vce(robust) Endogenous variables Observed: mpg Exogenous variables Observed: headroom weight Fitting target model: Iteration 0: log pseudolikelihood = -874.60268 Iteration 1: log pseudolikelihood = -874.60268 Structural equation model Number of obs = 74 Estimation method = mlmv Log pseudolikelihood = -874.60268 ------------------------------------------------------------------------------ | Robust | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- Structural | mpg | headroom | -.2103507 .4099959 -0.51 0.608 -1.013928 .5932266 weight | -.005898 .0006725 -8.77 0.000 -.0072161 -.0045798 _cons | 39.73567 1.924524 20.65 0.000 35.96367 43.50767 -------------+---------------------------------------------------------------- var(e.mpg)| 11.4824 3.083837 6.78305 19.43751 ------------------------------------------------------------------------------Comment
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