milrtest----------------------------------------------------------------------------------------------------------------Syntaxmilrtesttest_varlistMay be used after using mim to estimate models using:regress,logitandologit.bymay not be used withmilrtest. --------------------------------------------------------------------------------------------------------------Warningmilrtestis not compatible with the current version ofmimdue to changes in the information returned by . An updated version ofmilrtestmay become available in the future. In the mean time, should you wish to usemilrtest, an older, compatible version ofmim(v1.1.1) is still available (seenet sj 8-1 st0139).Descriptionmilrtestperforms a likelihood ratio test of nested models after analysis of multiply imputed datasets usingmim. This test is described in Meng and Rubin (1992, see Allison, 2001 for a more accessible source). The null hypothesis is that the unconstrained model (i.e. the model with more independent variables) fits the data no better than the constrained model (i.e. the model with fewer independent variables). The user runs the unconstrained model, then specifies intest_varlistthe variables to be constrained in the constrained model.milrtestshould be run immediately aftermim.milrtestdoes not take if or in, instead restrictions on the sample, etc. are taken from mim.Saved resultsmilrtestsaves the following in r() Below h0 and h1 represent the null and alternative hypotheses respectively. Unconstrained models are those where betas (for both h0 and h1) are freely estimated each of the m imputed datasets. Constrained models are those where all betas (and in the case of ologit cutpoints) are constrained to the mean of the estimates from the unconstrained model (that is, they are constrained to the MI estimate of the coefficients). scalars: r(d_m) mean of likelihood ratio chi-squares for h1 vs h0 in unconstrained models r(d_L) mean of likelihood ratio chi-squares for h1 vs h0 in constrained models r(p) p value of final statistic r(df_d) denominator degrees of freedom r(df_n) numerator degrees of freedom r(test_stat) F statistic r(m) number of imputed datasets used in estimation Scalars containing the log likelihoods for each of the m imputed datasets under various models: r(h0_c_m) Constrained model under h0 r(h1_c_m) Constrained model under h1 r(h0_uc_m) Unconstrained model under h0 r(h1_uc_m) Unconstrained model under h1 macros: r(cmd) Name of the estimation command r(h0_model) Model under the null hypothesis r(h1_model) Model under the alternative hypothesis matrices: r(h0_coefs) Coefficient estimates for null model r(h1_coefs) Coefficient estimates for alternative model Note: The coefficient estimates for the null model r(h0_coefs) and the alternative model r(h1_coefs) are the estimates of the regression coefficients based on the analysis of m MI datasets. These are the values to which the coefficients are constrained for the constrained model.Allison, P. D. (2001) Missing Data. Save University Papers Series on Quantitative Applications in the Social Sciences, 07-136. Thousand Oaks, CA: Sage. Carlin, J. B., Calati, J. C. & Royson, P. (2008) A new framework for managing and analyzing multiply imputed data in Stata. The Stata Journal 8(1): 49-67. Little, R. J. A., & Rubin, D. B. (2002) Statistical analysis with missing data. Hoboken, N.J: Wiley. Meng, X. & Rubin, D. B. (1992) Performing likelihood ratio tests with multiply-imputed data sets. Biometrika 79(1):103-111.ReferencesRose Anne Medeiros UCLA Academic Technology Services rosem@ats.ucla.eduAuthor