help cloglog dialogs: cloglog svy: cloglog
also see: cloglog postestimation
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Title
[R] cloglog -- Complementary log-log regression
Syntax
cloglog depvar [indepvars] [if] [in] [weight] [, options]
options description
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Model
noconstant suppress constant term
offset(varname) include varname in model with coefficient
constrained to 1
asis retain perfect predictor variables
constraints(constraints) apply specified linear constraints
collinear keep collinear variables
SE/Robust
vce(vcetype) vcetype may be oim, robust, cluster clustvar,
opg, bootstrap, or jackknife
Reporting
level(#) set confidence level; default is level(95)
eform report exponentiated coefficients
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
+ 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, nestreg, rolling, statsby,
stepwise, and svy are allowed; see prefix.
vce(bootstrap) and vce(jackknife) are not allowed with the mi estimate
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.
See [R] cloglog postestimation for features available after estimation.
Menu
Statistics > Binary outcomes > Complementary log-log regression
Description
cloglog fits maximum-likelihood complementary log-log models.
See logistic estimation commands for a list of related estimation
commands.
Options
+-------+
----+ Model +------------------------------------------------------------
noconstant, offset(varname); 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.
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.
eform displays the exponentiated coefficients and corresponding standard
errors and confidence intervals.
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.
Setting the optimization type to technique(bhhh) resets the default
vcetype to vce(opg).
The following option is available with cloglog but is not shown in the
dialog box:
coeflegend; see [R] estimation options.
Examples
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Setup
. webuse lbw
Fit complementary log-log model
. cloglog low age lwt i.race smoke ptl ht ui
Replay results with exponentiated coefficients
. cloglog, eform
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Setup
. sysuse auto
Fit complementary log-log model with robust variance estimates
. cloglog foreign weight mpg, vce(robust)
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Saved results
cloglog 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(N_f) number of zero outcomes
e(N_s) number of nonzero outcomes
e(df_m) model degrees of freedom
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) cloglog
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(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] cloglog
Help: [R] cloglog postestimation;
[R] clogit, [R] cusum, [R] glm, [R] logistic, [R] scobit, [SVY]
svy estimation, [XT] xtcloglog
