help binreg dialogs: binreg
also see: binreg postestimation
-------------------------------------------------------------------------------
Title
[R] binreg -- Generalized linear models: Extensions to the binomial
family
Syntax
binreg depvar [indepvars] [if] [in] [weight] [, options]
options description
-------------------------------------------------------------------------
Model
noconstant suppress constant term
or use logit link and report odds ratios
rr use log link and report risk ratios
hr use log-complement link and report health
ratios
rd use identity link and report risk differences
n(#|varname) use # or varname for number of trials
exposure(varname) include ln(varname) in model with coefficient
constrained to 1
offset(varname) include varname in model with coefficient
constrained to 1
constraints(constraints) apply specified linear constraints
collinear keep collinear variables
mu(varname) use varname as the initial estimate for the
mean of depvar
init(varname) synonym for mu(varname)
SE/Robust
vce(vcetype) vcetype may be eim, robust, cluster clustvar,
oim, opg, bootstrap, jackknife, hac kernel,
jackknife1, or unbiased
t(varname) variable name corresponding to time
vfactor(#) multiply variance matrix by scalar #
disp(#) quasi-likelihood multiplier
scale(x2|dev|#) set the scale parameter; default is scale(1)
Reporting
level(#) set confidence level; default is level(95)
coefficients report nonexponentiated coefficients
nocnsreport do not display constraints
display_options control spacing and display of omitted
variables and base and empty cells
Maximization
irls use iterated, reweighted least-squares
optimization; the default
ml use maximum likelihood optimization
maximize_options control the maximization process; seldom used
fisher(#) Fisher scoring steps
search search for good starting values
+ coeflegend display coefficients' legend instead of
coefficient table
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+ coeflegend does not appear in the dialog box.
indepvars may contain factor variables; see fvvarlist.
depvar and indepvars may contain time-series operators; see tsvarlist.
bootstrap, by, jackknife, mi estimate, rolling, and statsby are allowed;
see prefix.
vce(bootstrap), vce(jackknife), and vce(jackknife1) are not allowed with
the mi estimate prefix.
Weights are not allowed with the bootstrap prefix.
aweights are not allowed with the jackknife prefix.
fweights, aweights, iweights, and pweights are allowed; see weight.
See [R] binreg postestimation for features available after estimation.
Menu
Statistics > Generalized linear models > GLM for the binomial family
Description
binreg fits generalized linear models for the binomial family. It
estimates odds ratios, risk ratios, health ratios, and risk differences.
The available links are
Option Implied link Parameter
-----------------------------------------------
or logit odds ratios = exp(b)
rr log risk ratios = exp(b)
hr log complement health ratios = exp(b)
rd identity risk differences = b
Estimates of odds, risk, and health ratios are obtained by exponentiating
the appropriate coefficients. The or option produces the same results as
Stata's logistic command, and or coefficients yields the same results as
the logit command. When no link is specified or implied, or is assumed.
Options
+-------+
----+ Model +------------------------------------------------------------
noconstant; see [R] estimation options.
or requests the logit link and results in odds ratios if coefficients is
not specified.
rr requests the log link and results in risk ratios if coefficients is
not specified.
hr requests the log-complement link and results in health ratios if
coefficients is not specified.
rd requests the identity link and results in risk differences.
n(#|varname) specifies either a constant integer to use as the
denominator for the binomial family or a variable that holds the
denominator for each observation.
exposure(varname), offset(varname), constraints(constraints), collinear;
see [R] estimation options. constraints(constraints) and collinear
are not allowed with irls.
mu(varname) specifies varname containing an initial estimate for the mean
of depvar. This option can be useful if you encounter convergence
difficulties. init(varname) is a synonym.
+-----------+
----+ SE/Robust +--------------------------------------------------------
vce(vcetype) specifies the type of standard error reported, which
includes types that are robust to some kinds of misspecification,
that allow for intragroup correlation, that are derived from
asymptotic theory, and that use bootstrap or jackknife methods; see
[R] vce_option.
vce(eim), the default, uses the expected information matrix for the
variance estimator.
binreg also allows the following:
vce(hac kernel [#]) specifies that a heteroskedasticity- and
autocorrelation-consistent (HAC) variance estimate be used. HAC
refers to the general form for combining weighted matrices to
form the variance estimate. There are three kernels built into
binreg. kernel is a user-written program or one of
nwest | gallant | anderson
If # not specified, N - 2 is assumed.
vce(jackknife1) specifies that the one-step jackknife estimate of
variance be used.
vce(unbiased) specifies that the unbiased sandwich estimate of
variance be used.
t(varname) specifies the variable name corresponding to time; see [TS]
tsset. binreg does not always need to know t(), though it does if
vce(hac ... ) is specified. Then you can either specify the time
variable with t(), or you can tsset your data before calling binreg.
When the time variable is required, binreg assumes that the
observations are spaced equally over time.
vfactor(#) specifies a scalar by which to multiply the resulting variance
matrix. This option allows users to match output with other
packages, which may apply degrees of freedom or other small-sample
corrections to estimates of variance.
disp(#) multiplies the variance of depvar by # and divides the deviance
by #. The resulting distributions are members of the quasilikelihood
family.
scale(x2|dev|#) overrides the default scale parameter. This option is
allowed only with Hessian (information matrix) variance estimates.
By default, scale(1) is assumed for discrete distributions (binomial,
Poisson, and negative binomial), and scale(x2) is assumed for
continuous distributions (Gaussian, gamma, and inverse Gaussian).
scale(x2) specifies that the scale parameter be set to the Pearson
chi-squared (or generalized chi-squared) statistic divided by the
residual degrees of freedom, which was recommended by McCullagh and
Nelder as a good general choice for continuous distributions.
scale(dev) sets the scale parameter to the deviance divided by the
residual degrees of freedom. This option provides an alternative to
scale(x2) for continuous distributions and overdispersed or
underdispersed discrete distributions.
scale(#) sets the scale parameter to #.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#), noconstant; see [R] estimation options.
coefficients displays the nonexponentiated coefficients and corresponding
standard errors and confidence intervals. This option has no effect
when the rd option is specified, because it always presents the
nonexponentiated coefficients.
nocnsreport; see [R] estimation options.
display_options: noomitted, vsquish, noemptycells, baselevels,
allbaselevels; see [R] estimation options.
+--------------+
----+ Maximization +-----------------------------------------------------
irls requests iterated, reweighted least-squares (IRLS) optimization of
the deviance instead of Newton-Raphson optimization of the log
likelihood. This option is the default.
ml requests that optimization be carried out by using Stata's ml command.
maximize_options: technique(algorithm_spec), [no]log, trace, gradient,
showstep, hessian, showtolerance, difficult, iterate(#),
tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance,
from(init_specs); see [R] maximize. These options are seldom used.
Setting the optimization method to ml, with technique() set to
something other than BHHH, changes the vcetype to vce(oim).
Specifying technique(bhhh) changes vcetype to vce(opg).
fisher(#) specifies the number of Newton-Raphson steps that should use
the Fisher scoring Hessian or expected information matrix (EIM)
before switching to the observed information matrix (OIM). This
option is available only if ml is specified and is useful only for
Newton-Raphson optimization.
search specifies that the command search for good starting values. This
option is available only if ml is specified and is useful only for
Newton-Raphson optimization.
The following option is available with binreg but is not shown in the
dialog box:
coeflegend; see [R] estimation options.
Examples
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Setup
. webuse lbw
Report odds ratios
. binreg low age lwt i.race smoke ptl ht ui, or
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Setup
. webuse binreg
Report risk ratios
. binreg d i.soc i.alc i.smo, n(n) rr
Obtain nonexponentiated coefficients
. binreg d i.soc i.alc i.smo, n(n) rr coeff
Report risk differences
. binreg d i.soc i.alc i.smo, n(n) rd
Report health ratios
. binreg d i.soc i.alc i.smo, n(n) hr
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Saved results
binreg, irls saves the following in e():
Scalars
e(N) number of observations
e(k) number of parameters
e(k_eq_model) number of equations in model Wald test
e(df_m) model degrees of freedom
e(df) residual degrees of freedom
e(phi) model scale parameter
e(disp) dispersion parameter
e(bic) model BIC
e(N_clust) number of clusters
e(deviance) deviance
e(deviance_s) scaled deviance
e(deviance_p) Pearson deviance
e(deviance_ps) scaled Pearson deviance
e(dispers) dispersion
e(dispers_s) scaled dispersion
e(dispers_p) Pearson dispersion
e(dispers_ps) scaled Pearson dispersion
e(vf) factor set by vfactor(), 1 if not set
e(rank) rank of e(V)
e(rc) return code
Macros
e(cmd) binreg
e(cmdline) command as typed
e(depvar) name of dependent variable
e(eform) eform() option implied by or, rr, hr, or rd
e(varfunc) name of variance function used
e(varfunct) Binomial
e(varfuncf) variance function
e(link) link function used by glm
e(linkt) link title
e(linkf) link form
e(m) number of binomial trials
e(wtype) weight type
e(wexp) weight expression
e(title_fl) family-link title
e(clustvar) name of cluster variable
e(offset) offset
e(cons) noconstant or not set
e(hac_kernel) HAC kernel
e(hac_lag) HAC lag
e(vce) vcetype specified in vce()
e(vcetype) title used to label Std. Err.
e(opt) type of optimization
e(opt1) optimization title, line 1
e(opt2) optimization title, line 2
e(crittype) optimization criterion
e(properties) b V
e(predict) program used to implement predict
e(marginsnotok) predictions disallowed 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
e(V_modelbased) model-based variance
Functions
e(sample) marks estimation sample
binreg, ml saves the following in e():
Scalars
e(N) number of observations
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(df) residual degrees of freedom
e(phi) model scale parameter
e(aic) model AIC, if ml
e(bic) model BIC
e(ll) log likelihood, if ml
e(N_clust) number of clusters
e(chi2) chi-squared statistic
e(p) significance
e(deviance) deviance
e(deviance_s) scaled deviance
e(deviance_p) Pearson deviance
e(deviance_ps) scaled Pearson deviance
e(dispers) dispersion
e(dispers_s) scaled dispersion
e(dispers_p) Pearson dispersion
e(dispers_ps) scaled Pearson dispersion
e(vf) factor set by vfactor(), 1 if not set
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) binreg
e(cmdline) command as typed
e(depvar) name of dependent variable
e(eform) eform() option implied by or, rr, hr, or rd
e(varfunc) name of variance function used
e(varfunct) Binomial
e(varfuncf) variance function
e(link) link function used by glm
e(linkt) link title
e(linkf) link form
e(m) number of binomial trials
e(wtype) weight type
e(wexp) weight expression
e(title) title in estimation output
e(title_fl) family-link title
e(clustvar) name of cluster variable
e(offset) offset
e(cons) noconstant or not set
e(hac_kernel) HAC kernel
e(hac_lag) HAC lag
e(chi2type) 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(opt1) optimization title, line 1
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(marginsnotok) predictions disallowed 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(V) variance-covariance matrix of the estimators
e(V_modelbased) model-based variance
Functions
e(sample) marks estimation sample
Also see
Manual: [R] binreg
Help: [R] binreg postestimation;
[R] glm
