-------------------------------------------------------------------------------
help  mltrsq                              Katja Moehring and  Alexander Schmidt
-------------------------------------------------------------------------------

Calculating R-squared after two-level mixed models (beta version)

    Syntax

        mltrsq [ , ] [ full ]



    mltrsq is part of the mlt (multilevel tools) package.


Description

    mltrsq is an postestimation command for xtmixed (Stata Version 12 or
    above). It works after mixed models with two levels.  mltrsq gives two
    different R-squared values for each level:

        (1.) R-squared as proposed by Snijders and Bosker (1994: 350-354), also
        see Snijders and Bosker (1999, 99-105); and

        (2.) R-squared proposed by Bryk and Raudenbush (1992: 68).

    mltrsq will use the same Likelihood-function that has been specified for
    xtmixed.  Note that in Stata 12 the default Likelihood-function is Maximum
    Likelihood (mle).

    mltrsq provides different statistics as scalars. These results can be used
    with  estimates table or  estout (if installed). We provide the following
    statistics:

            +----------+
        ----+  scalars +-------------------------------------------------------
 
        e(N_l2)           number of level-2 units 

        e(sb_rsq_l1)      level-1 Snijders/Bosker R-squared

        e(sb_rsq_l2)      level-2 Snijders/Bosker R-squared

        e(br_rsq_l1)      level-1 Bryk/Raudenbush R-squared

        e(br_rsq_l2)      level-2 Bryk/Raudenbush R-squared 

 
Options

    full lists additionally the Harmonic mean of the level-2 group sizes, which
        is used for the calculation of the R-squared according to Snijders and
        Bosker, and the Random-effects parameters of the specified model and
        the null-model.  mltrsq will also report the variance components of the
        null model and the last model estimated by the user.

Example

    Load data set (ISSP 2006)
    . net get mlt
    . use redistribution.dta

    Multilevel regression of "Support for income redistribution"
    . xtmixed gr_incdiff sex age incperc rgdppc gini || Country: , mle var

    Calculate R-sqaured
    . mltrsq

    Use statistics in estimation table
    . est store m1
    . esttab m1, stats(N_l2 sb_rsq_l1 sb_rsq_l2)




References

    ISSP (2006): International Social Survey Programme - Role of Government IV,
        GESIS StudyNo: ZA4700, Edition 1.0, doi:10.4232/1.4700.

    Tom A.B. Snijders, and Roel J. Bosker (1994): “Modeled Variance in
        Two-Level Models.” Sociological Methods & Research 22 (3), 342-363.

    Tom A.B. Snijders and Roel J. Bosker (1999): Multilevel Analysis. An
        Introduction to Basic and Advanced Multilevel Modeling. London: Sage.

    A.S. Bryk and S.W. Raudenbush (1992): Hierarchical Linear Models in Social
        and Behavioral Research: Applications and Data Analysis Methods.
        Newbury Park, CA: Sage Publications.


Authors

    Katja Moehring, GK SOLCIFE, University of Cologne,
      moehring@wiso.uni-koeln.de, www.katjamoehring.de.

    Alexander Schmidt, GK SOCLIFE and Chair for Empirical Economic and Social
      Research, University of Cologne, alex@alexanderwschmidt.de,
      www.alexanderwschmidt.de.


Also see