help blogit, help bprobit, dialogs: blogit bprobit
help glogit, help gprobit glogit gprobit
also see: glogit postestimation
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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 [rhsvars] [if] [in] [, blogit_options]
Probit regression for grouped data
bprobit pos_var pop_var [rhsvars] [if] [in] [, bprobit_options]
Weighted least-squares logistic regression for grouped data
glogit pos_var pop_var [rhsvars] [if] [in] [, glogit_options]
Weighted least-squares probit regression for grouped data
gprobit pos_var pop_var [rhsvars] [if] [in] [, gprobit_options]
blogit_options description
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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 spacing and display of omitted
variables and base and empty cells
Maximization
maximize_options control the maximization process; seldom
used
+ nocoef do not display coefficient table; seldom
used
+ coeflegend display coefficients' legend instead of
coefficient table
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bprobit_options description
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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 spacing and display of omitted
variables and base and empty cells
Maximization
maximize_options control the maximization process; seldom
used
+ nocoef do not display coefficient table; seldom
used
+ coeflegend display coefficients' legend instead of
coefficient table
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glogit_options description
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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 spacing and display of omitted
variables and base and empty cells
+ coeflegend display coefficients' legend instead of
coefficient table
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gprobit_options description
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SE
vce(vcetype) vcetype may be ols, bootstrap, or jackknife
Reporting
level(#) set confidence level; default is level(95)
display_options control spacing and display of omitted
variables and base and empty cells
+ coeflegend display coefficients' legend instead of
coefficient table
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+ nocoef and coeflegend do not appear in the dialog box.
rhsvars may contain factor variables; see fvvarlist.
bootstrap, by, jackknife, rolling, and statsby are allowed; see prefix.
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, 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, that are
robust to some kinds of misspecification, that allow for intragroup
correlation, and that use bootstrap or jackknife methods; see [R]
vce_option.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#); see [R] estimation options.
or (blogit only) reports the estimated coefficients transformed to odds
ratios, i.e., 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: noomitted, vsquish, noemptycells, baselevels,
allbaselevels; see [R] estimation options.
+--------------+
----+ Maximization +-----------------------------------------------------
maximize_options: difficult, technique(algorithm_spec), iterate(#),
[no]log, trace, gradient, showstep, hessian, showtolerance,
tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance,
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 and that use
bootstrap or jackknife methods; 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, i.e., 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: noomitted, vsquish, noemptycells, baselevels,
allbaselevels; 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
Saved results
blogit and bprobit save 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 model Wald test
e(k_dv) number of dependent variables
e(k_autoCns) number of base, empty, and omitted constraints
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) name of dependent variable
e(wtype) weight type
e(wexp) weight expression
e(title) title in estimation output
e(clustvar) name of cluster variable
e(offset) offset
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(singularHmethod) m-marquardt or hybrid; method used when Hessian is
singular
e(crittype) optimization criterion
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 save 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) name of dependent variable
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
Also see
Manual: [R] glogit
Help: [R] glogit postestimation;
[R] logistic, [R] logit, [R] probit
