Stata 15 help for slogit

[R] slogit -- Stereotype logistic regression


slogit depvar [indepvars] [if] [in] [weight] [, options]

options Description ------------------------------------------------------------------------- Model dimension(#) dimension of the model; default is dimension(1) baseoutcome(#|lbl) set the base outcome to # or lbl; default is the last outcome constraints(numlist) apply specified linear constraints collinear keep collinear variables nocorner do not generate the corner constraints

SE/Robust vce(vcetype) vcetype may be oim, robust, cluster clustvar, opg, bootstrap, or jackknife

Reporting level(#) set confidence level; default is level(95) nocnsreport do not display constraints display_options control columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling

Maximization maximize_options control the maximization process; seldom used initialize(initype) method of initializing scale parameters; initype can be constant, random, or svd; see Options for details nonormalize do not normalize the numeric variables

coeflegend display legend instead of statistics ------------------------------------------------------------------------- indepvars may contain factor variables; see fvvarlist. bootstrap, by, fp, jackknife, rolling, statsby, and svy are allowed; see prefix. Weights are not allowed with the bootstrap prefix. vce() and weights are not allowed with the svy prefix. fweights, iweights, and pweights are allowed; see weight. coeflegend does not appear in the dialog box. See [R] slogit postestimation for features available after estimation.


Statistics > Categorical outcomes > Stereotype logistic regression


slogit fits Anderson's (1984) maximum-likelihood stereotype logistic regression model for categorical dependent variables. Stereotype logistic models can be used when the relevance of the ordering is unclear. These models do not impose the proportional-odds assumption.


+-------+ ----+ Model +------------------------------------------------------------

dimension(#) specifies the dimension of the model, which is the number of equations required to describe the relationship between the dependent variable and the independent variables. The maximum dimension is min(m-1,p), where m is the number of categories of the dependent variable and p is the number of independent variables in the model. The stereotype model with maximum dimension is a reparameterization of the multinomial logistic model.

baseoutcome(#|lbl) specifies the outcome level whose scale parameters and intercept are constrained to be zero. The base outcome may be specified as a number or a label. By default, slogit assumes that the outcome levels are ordered and uses the largest level of the dependent variable as the base outcome.

constraints(numlist), collinear; see [R] estimation options.

By default, the linear equality constraints suggested by Anderson (1984), termed the corner constraints, are generated for you. You can add constraints to these as needed, or you can turn off the corner constraints by specifying nocorner. These constraints are in addition to the constraints placed on the phi parameters corresponding to baseoutcome(#).

nocorner specifies that slogit not generate the corner constraints. If you specify nocorner, you must specify at least dimension()*dimension() constraints for the model to be identified.

+-----------+ ----+ SE/Robust +--------------------------------------------------------

vce(vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory (oim, opg), that are robust to some kinds of misspecification (robust), that allow for intragroup correlation (cluster clustvar), and that use bootstrap or jackknife methods (bootstrap, jackknife); see [R] vce_option.

If specifying vce(bootstrap) or vce(jackknife), you must also specify baseoutcome().

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#); see [R] estimation options.

nocnsreport; see [R] estimation options.

display_options: noci, nopvalues, noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style), cformat(%fmt), pformat(%fmt), sformat(%fmt), and nolstretch; see [R] estimation options.

+--------------+ ----+ Maximization +-----------------------------------------------------

maximize_options: difficult, technique(algorithm_spec), iterate(#), [no]log, trace, gradient, showstep, hessian, showtolerance, tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance, and from(init_specs); see [R] maximize. These options are seldom used.

Setting the optimization type to technique(bhhh) resets the default vcetype to vce(opg).

initialize(constant|random|svd) specifies how initial estimates are computed. The default, initialize(constant), is to set the scale parameters to the constant min(.5,1/d), where d is the dimension specified in dimension().

initialize(random) requests that uniformly distributed random numbers between 0 and 1 be used as initial values for the scale parameters. If you specify this option, you should also use set seed to ensure that you can replicate your results; see [R] set seed.

initialize(svd) requests that a singular value decomposition (SVD) be performed on the matrix of regression estimates from mlogit to reduce its rank to the dimension specified in dimension(). slogit uses the reduced-rank components of the SVD as initial estimates for the scale and regression coefficients. For details, see Methods and formulas in [R] slogit.

nonormalize specifies that the numeric variables not be normalized. Normalization of the numeric variables improves numerical stability but consumes more memory in generating temporary double-precision variables. Variables that are of type byte are not normalized, and if initial estimates are specified using the from() option, normalization of variables is not performed. See Methods and formulas in [R] slogit for more information.

The following option is available with slogit but is not shown in the dialog box:

coeflegend; see [R] estimation options.


Like multinomial logistic and ordered logistic models, stereotype logistic models are used with categorical dependent variables. They are often used when subjects are requested to assess or judge something. In a multinomial logistic model, the categories cannot be ranked. By contrast, in an ordered logistic model, the categories follow a natural ranking scheme and are subject to the proportional-odds assumption. Stereotype logistic regression can be viewed as a compromise between these two models and is primarily used when you are unsure of the relevance of the ordering of the outcome.

A common case is when subjects are asked to assess or judge something. For example, consider a survey in which consumers are asked to rate the quality of a product on a scale from 1 to 5, with 1 indicating poor quality and 5 indicating excellent quality. If the categories are monotonically related to one underlying latent variable, the ordered logistic model is appropriate. However, suppose that consumers weigh two or three latent factors when assessing quality. The stereotype logistic model is preferred to the ordered logistic model in this case because it allows you to specify multiple equations to capture the effects of the latent variables. Unlike multinomial logit models, the number of equations you specify could be fewer than m - 1, where m is the number of categories of the dependent variable. Stereotype logistic models are also used when categories may be indistinguishable. Suppose that a consumer must choose among A, B, C, or D. Multinomial logistic modeling assumes that the four choices are distinct in the sense that a consumer choosing one of the goods can distinguish its characteristics from the others. If goods A and B are in fact similar, consumers may be randomly picking between the two. One alternative is to combine the two categories and fit a three-category multinomial logistic model. A more flexible alternative is to use a stereotype logistic model.


--------------------------------------------------------------------------- Setup . webuse auto2yr

One-dimensional model . slogit repair foreign mpg price gratio

--------------------------------------------------------------------------- Setup . webuse sysdsn1

Saturated, two-dimensional model . slogit insure age male nonwhite, dim(2) base(1) ---------------------------------------------------------------------------

Stored results

slogit stores the following in e():

Scalars e(N) number of observations e(k) number of parameters e(k_indvars) number of independent variables e(k_out) number of outcomes e(k_eq) number of equations in e(b) e(k_eq_model) number of equations in overall model test e(df_m) Wald test degrees of freedom e(df_0) null model degrees of freedom e(k_dim) model dimension e(i_base) base outcome index e(ll) log likelihood e(ll_0) null model log likelihood e(N_clust) number of clusters e(chi2) chi-squared e(p) p-value for model test e(ic) number of iterations e(rank) rank of e(V) e(rc) return code e(converged) 1 if converged, 0 otherwise

Macros e(cmd) slogit e(cmdline) command as typed e(depvar) name of dependent variable e(indvars) independent variables e(wtype) weight type e(wexp) weight expression e(title) title in estimation output e(clustvar) name of cluster variable e(out#) outcome labels, # = 1, ..., e(k_out) e(chi2type) Wald; type of model chi-squared test e(vce) vcetype specified in vce() e(vcetype) title used to label Std. Err. e(opt) type of optimization e(which) max or min; whether optimizer is to perform maximization or minimization e(ml_method) type of ml method e(user) name of likelihood-evaluator program e(technique) maximization technique e(properties) b V e(predict) program used to implement predict e(marginsnotok) predictions disallowed by margins e(marginsdefault) default predict() specification for margins e(footnote) program used to implement the footnote display e(asbalanced) factor variables fvset as asbalanced e(asobserved) factor variables fvset as asobserved

Matrices e(b) coefficient vector e(outcomes) outcome values e(Cns) constraints matrix e(ilog) iteration log (up to 20 iterations) e(gradient) gradient vector e(V) variance-covariance matrix of the estimators e(V_modelbased) model-based variance

Functions e(sample) marks estimation sample


Anderson, J. A. 1984. Regression and ordered categorical variables (with discussion). Journal of the Royal Statistical Society, Series B 46: 1-30.

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