help slogit dialogs: slogit svy: slogit
also see: slogit postestimation
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Title
[R] slogit -- Stereotype logistic regression
Syntax
slogit depvar [indepvars] [if] [in] [weight] [, options]
options description
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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 spacing and display of omitted variables
and base and empty cells
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 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.
bootstrap, by, 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.
See [R] slogit postestimation for features available after estimation.
Menu
Statistics > Categorical outcomes > Stereotype logistic regression
Description
slogit fits maximum-likelihood stereotype logistic regression models as
developed by Anderson (1984). Like multinomial logistic and ordered
logistic models, stereotype logistic models are for use with categorical
dependent variables. In a multinomial logistic model, the categories
cannot be ranked, whereas in an ordered logistic model the categories
follow a natural ranking scheme. You can view stereotype logistic models
as a compromise between those two models. You can use them when you are
unsure of the relevance of the ordering, as is often the case when
subjects are asked to assess or judge something. You can also use them
in place of multinomial logistic models when you suspect that some of the
alternatives are similar. Unlike ordered logistic models, stereotype
logistic models do not impose the proportional-odds assumption.
Options
+-------+
----+ 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 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, that are
robust to some kinds of misspecification, that allow for intragroup
correlation, and that use bootstrap or jackknife methods; 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: 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).
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]
generate).
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.
The following option is available with slogit but is not shown in the
dialog box:
coeflegend; see [R] estimation options.
Examples
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Setup
. webuse auto2yr
One-dimensional model
. slogit repair foreign mpg price gratio
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Setup
. webuse sysdsn1
Saturated, two-dimensional model
. slogit insure age male nonwhite i.site, dim(2) base(1)
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Saved results
slogit saves 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 model Wald test
e(k_autoCns) number of base, empty, and omitted constraints
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) significance
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(k_eq_skip) identifies which equations should not be reported
in the coefficient table
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(labels) outcome labels or numeric levels
e(vce) vcetype specified in vce()
e(vcetype) title used to label Std. Err.
e(diparm#) display transformed parameter #
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(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
Reference
Anderson, J. A. 1984. Regression and ordered categorical variables (with
discussion). Journal of the Royal Statistical Society, Series B 46:
1-30.
Also see
Manual: [R] slogit
Help: [R] slogit postestimation;
[R] logistic, [R] mlogit, [R] ologit, [R] oprobit, [R] roc
