Thanks to Kit Baum, new versions of gologit2 and oglm are now available on SSC. These only affect users of Stata 14 (but both programs continue to work under Stata 11.2).
Under Stata 14, both programs now support marginal analysis with multiple outcomes (http://www.stata.com/stata14/margins-multi-outcomes/). As the Stata documentation explains,
In other words. before Stata 14, if you had 4 different outcomes, you had to give 4 different margins commands. Now you only need one.
Both programs deal with situations where the assumptions of the proportional odds/ parallel lines models for ordinal models are violated. Here are descriptions of the programs:
gologit2 estimates generalized ordered logit models for ordinal dependent variables. A major strength of gologit2 is that it can also estimate three special cases of the generalized model: the proportional odds/parallel lines model, the partial proportional odds model, and the logistic regression model. Hence, gologit2 can estimate models that are less restrictive than the proportional odds /parallel lines models estimated by ologit (whose assumptions are often violated) but more parsimonious and interpretable than those estimated by a non-ordinal method, such as multinomial logistic regression (i.e. mlogit). The svy: prefix, as well as factor variables and post-estimation commands such as margins, are supported. Other key strengths of gologit2 include options for linear constraints, alternative model parameterizations, automated model fitting, alternative link functions (logit, probit, complementary log-log, log-log & cauchit), and the computation of estimated probabilities via the predict command. gologit2 works under Stata 11.2 or higher. Those with older versions of Stata should use gologit29 instead. gologit2 is inspired by Vincent Fu's gologit program and is backward compatible with both it and gologit29 but offers several additional powerful options.
oglm estimates Ordinal Generalized Linear Models. It supports several link functions, including logit, probit, complementary log-log, log-log and cauchit. When an ordinal regression model incorrectly assumes that error variances are the same for all cases, the standard errors are wrong and (unlike OLS regression) the parameter estimates are biased. With oglm you can estimate heterogeneous choice/ location-scale models that explicitly specify the determinants of heteroskedasticity in an attempt to understand and correct for it. Several other special cases of ordinal generalized linear models can also be estimated by oglm. oglm was inspired by the SPSS PLUM routine but differs somewhat in its terminology, labeling of links, and the variables that are allowed when modeling heteroskedasticity. Stata 9 or 10 users should use oglm9 instead.
For more on the programs, see
http://www3.nd.edu/~rwilliam/gologit2/index.html
http://www3.nd.edu/~rwilliam/oglm/index.html
Under Stata 14, both programs now support marginal analysis with multiple outcomes (http://www.stata.com/stata14/margins-multi-outcomes/). As the Stata documentation explains,
When we fit models for ordinal or categorical response variables, we can make predictions for each outcome. margins calculates statistics such as marginal means, marginal effects, adjusted predictions, and predictive margins. These can also be computed for each response variable or for each outcome of an ordinal or a categorical variable. margins now automatically performs the requested marginal analysis for all variables and all outcomes. You can, of course, still request that it produce the analysis for only a single outcome or set of specified outcomes.
Both programs deal with situations where the assumptions of the proportional odds/ parallel lines models for ordinal models are violated. Here are descriptions of the programs:
gologit2 estimates generalized ordered logit models for ordinal dependent variables. A major strength of gologit2 is that it can also estimate three special cases of the generalized model: the proportional odds/parallel lines model, the partial proportional odds model, and the logistic regression model. Hence, gologit2 can estimate models that are less restrictive than the proportional odds /parallel lines models estimated by ologit (whose assumptions are often violated) but more parsimonious and interpretable than those estimated by a non-ordinal method, such as multinomial logistic regression (i.e. mlogit). The svy: prefix, as well as factor variables and post-estimation commands such as margins, are supported. Other key strengths of gologit2 include options for linear constraints, alternative model parameterizations, automated model fitting, alternative link functions (logit, probit, complementary log-log, log-log & cauchit), and the computation of estimated probabilities via the predict command. gologit2 works under Stata 11.2 or higher. Those with older versions of Stata should use gologit29 instead. gologit2 is inspired by Vincent Fu's gologit program and is backward compatible with both it and gologit29 but offers several additional powerful options.
oglm estimates Ordinal Generalized Linear Models. It supports several link functions, including logit, probit, complementary log-log, log-log and cauchit. When an ordinal regression model incorrectly assumes that error variances are the same for all cases, the standard errors are wrong and (unlike OLS regression) the parameter estimates are biased. With oglm you can estimate heterogeneous choice/ location-scale models that explicitly specify the determinants of heteroskedasticity in an attempt to understand and correct for it. Several other special cases of ordinal generalized linear models can also be estimated by oglm. oglm was inspired by the SPSS PLUM routine but differs somewhat in its terminology, labeling of links, and the variables that are allowed when modeling heteroskedasticity. Stata 9 or 10 users should use oglm9 instead.
For more on the programs, see
http://www3.nd.edu/~rwilliam/gologit2/index.html
http://www3.nd.edu/~rwilliam/oglm/index.html
