Stata 15 help for glogit

blogit, bprobit, glogit, and gprobit continue to work but, as of Stata 14, are no longer an official part of Stata. This is the original help file, which we will no longer update, so some links may no longer work.

See glm for a recommended alternative. Logistic and probit models for grouped data can be fit via maximum likelihood estimation using glm if pos_var is specified as the dependent variable and the family(binomial pop_var) option is specified, where pos_var and pop_var are defined as below. For a logistic model, specify the link(logit) option. For a probit model, specify the link(probit) option.

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Title

[R] glogit -- Logit and probit estimation for grouped data

Syntax

Logistic regression for grouped data

blogit pos_var pop_var [indepvars] [if] [in] [, blogit_options]

Probit regression for grouped data

bprobit pos_var pop_var [indepvars] [if] [in] [, bprobit_options]

Weighted least-squares logistic regression for grouped data

glogit pos_var pop_var [indepvars] [if] [in] [, glogit_options]

Weighted least-squares probit regression for grouped data

gprobit pos_var pop_var [indepvars] [if] [in] [, gprobit_options]

blogit_options Description ------------------------------------------------------------------------- Model noconstant suppress constant term asis retain perfect predictor variables offset(varname) include varname in model with coefficient constrained to 1 constraints(constraints) apply specified linear constraints collinear keep collinear variables

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

Reporting level(#) set confidence level; default is level(95) or report odds ratios 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

nocoef do not display coefficient table; seldom used coeflegend display legend instead of statistics -------------------------------------------------------------------------

bprobit_options Description ------------------------------------------------------------------------- Model noconstant suppress constant term asis retain perfect predictor variables offset(varname) include varname in model with coefficient constrained to 1 constraints(constraints) apply specified linear constraints collinear keep collinear variables

SE/Robust vce(vcetype) vcetype may be oim, robust, cluster clustvar, 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 nocoef do not display coefficient table; seldom used coeflegend display legend instead of statistics -------------------------------------------------------------------------

glogit_options Description ------------------------------------------------------------------------- SE vce(vcetype) vcetype may be ols, bootstrap, or jackknife

Reporting level(#) set confidence level; default is level(95) or report odds ratios display_options control columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling

coeflegend display legend instead of statistics -------------------------------------------------------------------------

gprobit_options Description ------------------------------------------------------------------------- SE vce(vcetype) vcetype may be ols, bootstrap, or jackknife

Reporting level(#) set confidence level; default is level(95) display_options control columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling

coeflegend display legend instead of statistics -------------------------------------------------------------------------

indepvars may contain factor variables; see fvvarlist. bootstrap, by, jackknife, rolling, and statsby are allowed; see prefix. fp is allowed with blogit and bprobit. nocoef and coeflegend do not appear in the dialog box. See [R] glogit postestimation for features available after estimation.

Menu

blogit

Statistics > Binary outcomes > Grouped data > Logit regression for grouped data

bprobit

Statistics > Binary outcomes > Grouped data > Probit regression for grouped data

glogit

Statistics > Binary outcomes > Grouped data > Weighted least-squares logit regression

gprobit

Statistics > Binary outcomes > Grouped data > Weighted least-squares probit regression

Description

blogit and bprobit produce maximum-likelihood logit and probit estimates on grouped ("blocked") data; glogit and gprobit produce weighted least-squares estimates. In the syntax diagrams above, pos_var and pop_var refer to variables containing the total number of positive responses and the total population.

See logistic estimation commands for a list of related estimation commands.

Options for blogit and bprobit

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

noconstant; see [R] estimation options.

asis forces retention of perfect predictor variables and their associated perfectly predicted observations and may produce instabilities in maximization; see [R] probit.

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

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

vce(vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory (oim), 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.

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

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

or (blogit only) reports the estimated coefficients transformed to odds ratios, that is, exp(b) rather than b. Standard errors and confidence intervals are similarly transformed. This option affects how results are displayed, not how they are estimated. or may be specified at estimation or when replaying previously estimated results.

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.

The following options are available with blogit and bprobit but are not shown in the dialog box:

nocoef specifies that the coefficient table not be displayed. This option is sometimes used by program writers but is useless interactively.

coeflegend; see [R] estimation options.

Options for glogit and gprobit

+----+ ----+ SE +---------------------------------------------------------------

vce(vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory (ols) and that use bootstrap or jackknife methods (bootstrap, jackknife); see [R] vce_option.

vce(ols), the default, uses the standard variance estimator for ordinary least-squares regression.

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

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

or (glogit only) reports the estimated coefficients transformed to odds ratios, that is, e^b rather than b. Standard errors and confidence intervals are similarly transformed. This option affects how results are displayed, not how they are estimated. or may be specified at estimation or when replaying previously estimated results.

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.

The following option is available with glogit and gprobit but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Examples

Setup . webuse xmpl2

Logistic regression for grouped data . blogit deaths pop agecat exposed

Same as above, but report odds ratios rather than coefficients . blogit deaths pop agecat exposed, or

Weighted least-squares logistic regression for grouped data . glogit deaths pop agecat exposed

Same as above, but report odds ratios rather than coefficients . glogit deaths pop agecat exposed, or

Probit regression for grouped data . bprobit deaths pop agecat exposed

Replay with 99% confidence intervals . bprobit, level(99)

Weighted least-squares probit regression for grouped data . gprobit deaths pop agecat exposed

Stored results

blogit and bprobit store the following in e():

Scalars e(N) number of observations e(N_cds) number of completely determined successes e(N_cdf) number of completely determined failures e(k) number of parameters e(k_eq) number of equations in e(b) e(k_eq_model) number of equations in overall model test e(k_dv) number of dependent variables e(df_m) model degrees of freedom e(r2_p) pseudo-R-squared e(ll) log likelihood e(ll_0) log likelihood, constant-only model e(N_clust) number of clusters e(chi2) chi-squared e(p) significance e(rank) rank of e(V) e(ic) number of iterations e(rc) return code e(converged) 1 if converged, 0 otherwise

Macros e(cmd) blogit or bprobit e(cmdline) command as typed e(depvar) variable containing number of positive responses and variable containing population size e(wtype) weight type e(wexp) weight expression e(title) title in estimation output e(clustvar) name of cluster variable e(offset) linear offset variable e(chi2type) Wald or LR; 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(marginsok) predictions allowed by margins e(asbalanced) factor variables fvset as asbalanced e(asobserved) factor variables fvset as asobserved

Matrices e(b) coefficient vector e(Cns) constraints matrix e(ilog) iteration log (up to 20 iterations) e(gradient) gradient vector e(mns) vector of means of the independent variables e(rules) information about perfect predictors e(V) variance-covariance matrix of the estimators e(V_modelbased) model-based variance

Functions e(sample) marks estimation sample

glogit and gprobit store the following in e():

Scalars e(N) number of observations e(mss) model sum of squares e(df_m) model degrees of freedom e(rss) residual sum of squares e(df_r) residual degrees of freedom e(r2) R-squared e(r2_a) adjusted R-squared e(F) F statistic e(rmse) root mean squared error e(rank) rank of e(V)

Macros e(cmd) glogit or gprobit e(cmdline) command as typed e(depvar) variable containing number of positive responses and variable containing population size e(model) ols e(title) title in estimation output e(vce) vcetype specified in vce() e(vcetype) title used to label Std. Err. e(properties) b V e(predict) program used to implement predict e(marginsok) predictions allowed by margins e(asbalanced) factor variables fvset as asbalanced e(asobserved) factor variables fvset as asobserved

Matrices e(b) coefficient vector e(V) variance-covariance matrix of the estimators

Functions e(sample) marks estimation sample


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