[R] margins -- Marginal means, predictive margins, and marginal effects
Syntax
margins [marginlist] [if] [in] [weight] [, response_options options]
where marginlist is a list of factor variables or interactions that
appear in the current estimation results. The variables may be typed
with or without the i. prefix, and you may use any factor-variable
syntax:
. margins i.sex i.group i.sex#i.group
. margins sex group sex#i.group
. margins sex##group
response_options Description
-------------------------------------------------------------------------
Main
predict(pred_opt) estimate margins for predict, pred_opt
expression(pnl_exp) estimate margins for pnl_exp
dydx(varlist) estimate marginal effect of variables in varlist
eyex(varlist) estimate elasticities of variables in varlist
dyex(varlist) estimate semielasticity -- d(y)/d(lnx)
eydx(varlist) estimate semielasticity -- d(lny)/d(x)
continuous treat factor-level indicators as continuous
-------------------------------------------------------------------------
options Description
-------------------------------------------------------------------------
Main
grand add the overall margin; default if no marginlist
At
at(atspec) estimate margins at specified values of
covariates
atmeans estimate margins at the means of covariates
asbalanced treat all factor variables as balanced
if/in/over
over(varlist) estimate margins at unique values of varlist
subpop(subspec) estimate margins for subpopulation
Within
within(varlist) estimate margins at unique values of the nesting
factors in varlist
Contrast
contrast_options any options documented in [R] margins, contrast
Pairwise comparisons
pwcompare_options any options documented in [R] margins, pwcompare
SE
vce(delta) estimate SEs using delta method; the default
vce(unconditional) estimate SEs allowing for sampling of covariates
nose do not estimate SEs
Advanced
noweights ignore weights specified in estimation
noesample do not restrict margins to the estimation sample
emptycells(empspec) treatment of empty cells for balanced factors
estimtolerance(tol) specify numerical tolerance used to determine
estimable functions; default is
estimtolerance(1e-5)
noestimcheck suppress estimability checks
force estimate margins despite potential problems
chainrule use the chain rule when computing derivatives
nochainrule do not use the chain rule
Reporting
level(#) set confidence level; default is level(95)
mcompare(method) adjust for multiple comparisons; default is
mcompare(noadjust)
noatlegend suppress legend of fixed covariate values
post post margins and their VCE as estimation results
display_options control columns and column formats, row spacing,
line width, and factor-variable labeling
df(#) use t distribution with # degrees of freedom for
computing p-values and confidence intervals
-------------------------------------------------------------------------
method Description
-------------------------------------------------------------------------
noadjust do not adjust for multiple comparisons; the
default
bonferroni [adjustall] Bonferroni's method; adjust across all terms
sidak [adjustall] Sidak's method; adjust across all terms
scheffe Scheffe's method
-------------------------------------------------------------------------
Time-series operators are allowed if they were used in the estimation.
See at() under Options for a description of atspec.
fweights, aweights, iweights, and pweights are allowed; see weight.
df(#) does not appear in the dialog box.
Menu
Statistics > Postestimation
Description
Margins are statistics calculated from predictions of a previously fit
model at fixed values of some covariates and averaging or otherwise
integrating over the remaining covariates.
The margins command estimates margins of responses for specified values
of covariates and presents the results as a table.
Capabilities include estimated marginal means, least-squares means,
average and conditional marginal and partial effects (which may be
reported as derivatives or as elasticities), average and conditional
adjusted predictions, and predictive margins.
Options
Warning: The option descriptions are brief and use jargon. Skip to
Remarks and examples in [R] margins if you are reading about margins for
the first time.
+------+
----+ Main +-------------------------------------------------------------
predict(pred_opt) and expression(pnl_exp) are mutually exclusive; they
specify the response. If neither is specified, the response will be
the default prediction that would be produced by predict after the
underlying estimation command. Some estimation commands, such as
mlogit, document a different default prediction for margins than for
predict.
predict(pred_opt) specifies the option(s) to be specified with the
predict command to produce the variable that will be used as the
response. After estimation by logistic, you could specify
predict(xb) to obtain linear predictions rather than the predict
command's default, the probabilities.
Multiple predict() options can be specified to compute margins of
multiple predictions simultaneously.
expression(pnl_exp) specifies the response as an expression. See
Description and Remarks and examples in [R] predictnl for a full
description of pnl_exp. After estimation by logistic, you might
specify expression(exp(predict(xb))) to use relative odds rather
than probabilities as the response. For examples, see Example
12: Margins of a specified expression in [R] margins.
dydx(varlist), eyex(varlist), dyex(varlist), and eydx(varlist) request
that margins report derivatives of the response with respect to
varlist rather than on the response itself. eyex(), dyex(), and
eydx() report derivatives as elasticities; see Expressing derivatives
as elasticities in [R] margins.
continuous is relevant only when one of dydx() or eydx() is also
specified. It specifies that the levels of factor variables be
treated as continuous; see Derivatives versus discrete differences in
[R] margins. This option is implied if there is a single-level
factor variable specified in dydx() or eydx().
grand specifies that the overall margin be reported. grand is assumed
when marginlist is empty.
+----+
----+ At +---------------------------------------------------------------
at(atspec) specifies values for covariates to be treated as fixed.
at(age=20) fixes covariate age to the value specified. at() may be
used to fix continuous or factor covariates.
at(age=20 sex=1) simultaneously fixes covariates age and sex at the
values specified.
at(age=(20 30 40 50)) fixes age first at 20, then at 30, ....
margins produces separate results for each specified value.
at(age=(20(10)50)) does the same as at(age=(20 30 40 50)); that is,
you may specify a numlist.
at((mean) age (median) distance) fixes the covariates at the summary
statistics specified. at((p25) _all) fixes all covariates at
their 25th percentile values. See Syntax of at() for the full
list of summary-statistic modifiers.
at((mean) _all (median) x x2=1.2 z=(1 2 3)) is processed from
general to specific, with settings for named covariates
overriding general settings specified via _all. Thus, all
covariates are fixed at their means except for x (fixed at its
median), x2 (fixed at 1.2), and z (fixed first at 1, then at 2,
and finally at 3).
at((means) _all (asobserved) x2) is a convenient way to set all
covariates except x2 to the mean.
Multiple at() options can be specified, and each will produce a
different set of margins.
See Syntax of at() for more information.
atmeans specifies that covariates be fixed at their means and is
shorthand for at((mean) _all). atmeans differs from at((mean) _all)
in that atmeans will affect subsequent at() options. For instance,
. margins ..., atmeans at((p25) x) at((p75) x)
produces two sets of margins with both sets evaluated at the means of
all covariates except x.
asbalanced is shorthand for at((asbalanced) _factor) and specifies that
factor covariates be evaluated as though there were an equal number
of observations in each level; see Obtaining margins as though the
data were balanced in [R] margins. asbalanced differs from
at((asbalanced) _factor) in that asbalanced will affect subsequent
at() options in the same way as atmeans does.
+------------+
----+ if/in/over +-------------------------------------------------------
over(varlist) specifies that separate sets of margins be estimated for
the groups defined by varlist. The variables in varlist must contain
nonnegative integer (or missing) values. The variables need not be
covariates in your model. When over() is combined with the
vce(unconditional) option, each group is treated as a subpopulation;
see [SVY] Subpopulation estimation.
subpop([varname] [if]) is intended for use with the vce(unconditional)
option. It specifies that margins be estimated for the single
subpopulation identified by the indicator variable or by the if
expression or by both. Zero or missing indicates that the
observation be excluded; nonzero or nonmissing, that it be included.
See [SVY] Subpopulation estimation for why subpop() is preferred to
if expressions and in ranges when also using vce(unconditional). If
subpop() is used without vce(unconditional), it is treated merely as
an additional if qualifier.
+--------+
----+ Within +-----------------------------------------------------------
within(varlist) allows for nested designs. varlist contains the nesting
variable(s) over which margins are to be estimated. See Obtaining
margins with nested designs in [R] margins. As with over(varlist),
when within(varlist) is combined with vce(unconditional), each level
of the variables in varlist is treated as a subpopulation.
+----------+
----+ Contrast +---------------------------------------------------------
contrast_options are any of the options documented in [R] margins,
contrast.
+----------------------+
----+ Pairwise comparisons +---------------------------------------------
pwcompare_options are any of the options documented in [R] margins,
pwcompare.
+----+
----+ SE +---------------------------------------------------------------
vce(delta) and vce(unconditional) specify how the VCE and,
correspondingly, standard errors are calculated.
vce(delta) is the default. The delta method is applied to the
formula for the response and the VCE of the estimation command.
This method assumes that values of the covariates used to
calculate the response are given or, if all covariates are not
fixed using at(), that the data are given.
vce(unconditional) specifies that the covariates that are not fixed
be treated in a way that accounts for their having been sampled.
The VCE is estimated using the linearization method. This method
allows for heteroskedasticity or other violations of
distributional assumptions and allows for correlation among the
observations in the same manner as vce(robust) and vce(cluster
...), which may have been specified with the estimation command.
This method also accounts for complex survey designs if the data
are svyset. See Obtaining margins with survey data and
representative samples in [R] margins. When you use complex
survey data, this method requires that the linearized variance
estimation method be used for the model. See [SVY] svy
postestimation for an example of margins with replication-based
methods.
nose suppresses calculation of the VCE and standard errors. See
Requirements for model specification in [R] margins for an example of
the use of this option.
+----------+
----+ Advanced +---------------------------------------------------------
noweights specifies that any weights specified on the previous estimation
command be ignored by margins. By default, margins uses the weights
specified on the estimator to average responses and to compute
summary statistics. If weights are specified on the margins command,
they override previously specified weights, making it unnecessary to
specify noweights. The noweights option is not allowed after svy:
estimation when the vce(unconditional) option is specified.
For multilevel models, such as meglm, the default behavior is to
construct a single weight value for each observation by multiplying
the corresponding multilevel weights within the given observation.
noesample specifies that margins not restrict its computations to the
estimation sample used by the previous estimation command. See
Example 15: Margins evaluated out of sample in [R] margins.
With the default delta-method VCE, noesample margins may be estimated
on samples other than the estimation sample; such results are valid
under the assumption that the data used are treated as being given.
You can specify noesample and vce(unconditional) together, but if you
do, you should be sure that the data in memory correspond to the
original e(sample). To show that you understand that, you must also
specify the force option. Be aware that making the
vce(unconditional) calculation on a sample different from the
estimation sample would be equivalent to estimating the coefficients
on one set of data and computing the scores used by the linearization
on another set; see [P] _robust.
emptycells(strict) and emptycells(reweight) are relevant only when the
asbalanced option is also specified. emptycells() specifies how
empty cells are handled in interactions involving factor variables
that are being treated as balanced; see Obtaining margins as though
the data were balanced in [R] margins.
emptycells(strict) is the default; it specifies that margins
involving empty cells be treated as not estimable.
emptycells(reweight) specifies that the effects of the observed cells
be increased to accommodate any missing cells. This makes the
margin estimable but changes its interpretation.
emptycells(reweight) is implied when the within() option is
specified.
estimtolerance(tol) specifies the numerical tolerance used to determine
estimable functions. The default is estimtolerance(1e-5).
A linear combination of the model coefficients z is found to be not
estimable if
mreldif(z, z*H) > tol
where H is defined in Methods and formulas.
noestimcheck specifies that margins not check for estimability. By
default, the requested margins are checked and those found not
estimable are reported as such. Nonestimability is usually caused by
empty cells. If noestimcheck is specified, estimates are computed in
the usual way and reported even though the resulting estimates are
manipulable, which is to say they can differ across equivalent models
having different parameterizations. See Estimability of margins in
[R] margins.
force instructs margins to proceed in some situations where it would
otherwise issue an error message because of apparent violations of
assumptions. Do not be casual about specifying force. You need to
understand and fully evaluate the statistical issues. For an example
of the use of force, see Using margins after the estimates use
command in [R] margins.
chainrule and nochainrule specify whether margins uses the chain rule
when numerically computing derivatives. You need not specify these
options when using margins after any official Stata estimator;
margins will choose the appropriate method automatically.
Specify nochainrule after estimation by a community-contributed
command. We recommend using nochainrule, even though chainrule is
usually safe and is always faster. nochainrule is safer because it
makes no assumptions about how the parameters and covariates join to
form the response.
nochainrule is implied when the expression() option is specified.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#) specifies the confidence level, as a percentage, for confidence
intervals. The default is level(95) or as set by set level.
mcompare(method) specifies the method for computing p-values and
confidence intervals that account for multiple comparisons within a
factor-variable term.
Most methods adjust the comparisonwise error rate, alpha_c, to
achieve a prespecified experimentwise error rate, alpha_e.
mcompare(noadjust) is the default; it specifies no adjustment.
alpha_c = alpha_e
mcompare(bonferroni) adjusts the comparisonwise error rate based on
the upper limit of the Bonferroni inequality
alpha_e <= m * alpha_c
where m is the number of comparisons within the term.
The adjusted comparisonwise error rate is
alpha_c = alpha_e/m
mcompare(sidak) adjusts the comparisonwise error rate based on the
upper limit of the probability inequality
alpha_e <= 1 - (1 - alpha_c)^m
where m is the number of comparisons within the term.
The adjusted comparisonwise error rate is
alpha_c = 1 - (1 - alpha_e)^(1/m)
This adjustment is exact when the m comparisons are independent.
mcompare(scheffe) controls the experimentwise error rate using the F
or chi-squared distribution with degrees of freedom equal to the
rank of the term.
mcompare(method adjustall) specifies that the multiple-comparison
adjustments count all comparisons across all terms rather than
performing multiple comparisons term by term. This leads to more
conservative adjustments when multiple variables or terms are
specified in marginslist. This option is compatible only with
the bonferroni and sidak methods.
noatlegend specifies that the legend showing the fixed values of
covariates be suppressed.
post causes margins to behave like a Stata estimation (e-class) command.
margins posts the vector of estimated margins along with the
estimated variance-covariance matrix to e(), so you can treat the
estimated margins just as you would results from any other estimation
command. For example, you could use test to perform simultaneous
tests of hypotheses on the margins, or you could use lincom to create
linear combinations. See Example 10: Testing margins -- contrasts of
margins in [R] margins.
display_options: noci, nopvalues, vsquish, nofvlabel, fvwrap(#),
fvwrapon(style), cformat(%fmt), pformat(%fmt), sformat(%fmt), and
nolstretch.
noci suppresses confidence intervals from being reported in the
coefficient table.
nopvalues suppresses p-values and their test statistics from being
reported in the coefficient table.
vsquish specifies that the blank space separating factor-variable
terms or time-series-operated variables from other variables in
the model be suppressed.
nofvlabel displays factor-variable level values rather than attached
value labels. This option overrides the fvlabel setting; see [R]
set showbaselevels.
fvwrap(#) allows long value labels to wrap the first # lines in the
coefficient table. This option overrides the fvwrap setting; see
[R] set showbaselevels.
fvwrapon(style) specifies whether value labels that wrap will break
at word boundaries or break based on available space.
fvwrapon(word), the default, specifies that value labels break at
word boundaries.
fvwrapon(width) specifies that value labels break based on
available space.
This option overrides the fvwrapon setting; see [R] set
showbaselevels.
cformat(%fmt) specifies how to format margins, standard errors, and
confidence limits in the table of estimated margins.
pformat(%fmt) specifies how to format p-values in the table of
estimated margins.
sformat(%fmt) specifies how to format test statistics in the table of
estimated margins.
nolstretch specifies that the width of the table of estimated margins
not be automatically widened to accommodate longer variable
names. The default, lstretch, is to automatically widen the table
of estimated margins up to the width of the Results window.
Specifying lstretch or nolstretch overrides the setting given by
set lstretch. If set lstretch has not been set, the default is
lstretch. nolstretch is not shown in the dialog box.
The following option is available with margins but is not shown in the
dialog box:
df(#) specifies that the t distribution with # degrees of freedom be used
for computing p-values and confidence intervals. The default
typically is to use the standard normal distribution. However, if
the estimation command computes the residual degrees of freedom
(e(df_r)) and predict(xb) is specified with margins, the default is
to use the t distribution with e(df_r) degrees of freedom.
Examples
These examples are intended for quick reference. For a conceptual
overview of margins and examples with discussion see Remarks and examples
in [R] margins.
Examples: obtaining margins of responses
Setup
. webuse margex
A simple case after regress
. regress y i.sex i.group
. margins sex
A simple case after logistic
. logistic outcome i.sex i.group
. margins sex
Average response versus response at average
. margins sex
. margins sex, atmeans
Multiple margins from one margins command
. margins sex group
Margins with interaction terms
. logistic outcome i.sex i.group sex#group
. margins sex group
Margins with continuous variables
. logistic outcome i.sex i.group sex#group age
. margins sex group
Margins of continuous variables
. logistic outcome i.sex i.group sex#group age
. margins sex group
. margins, at(age=40)
. margins, at(age=(30 35 40 45 50))
Or, equivalently
. margins, at(age=(30(5)50))
Margins of interactions
. margins sex#group
Margins of a specified prediction
. tobit ycn i.sex i.group sex#group age, ul(90)
. margins sex, predict(ystar(.,90))
Margins of a specified expression
. margins sex, expression( predict(ystar(.,90)) / predict(xb) )
Margins with multiple outcomes (responses)
. mlogit group i.sex age
. margins sex
. margins sex, predict(outcome(1))
Margins with multiple equations
. sureg (y = i.sex age) (distance = i.sex i.group)
. margins sex
. margins sex, predict(equation(y))
. margins sex, expression(predict(equation(y)) -
predict(equation(distance)))
Margins evaluated out of sample
. webuse margex
. tobit ycn i.sex i.group sex#group age, ul(90)
. webuse peach
. margins sex, predict(ystar(.,90)) noesample
Examples: obtaining marginal effects
Setup
. webuse margex
. logistic outcome treatment##group age c.age#c.age treatment#c.age
Average marginal effect (partial effects) of one covariate
. margins, dydx(treatment)
Average marginal effects of all covariates
. margins, dydx(*)
Marginal effects evaluated over the response surface
. margins group, dydx(treatment) at(age=(20(10)60))
Examples: obtaining margins with survey data and representative samples
Inferences for populations, margins of response
. webuse margex
. logistic outcome i.sex i.group sex#group age, vce(robust)
. margins sex group, vce(unconditional)
Inferences for populations, marginal effects
. margins, dydx(*) vce(unconditional)
Inferences for populations with svyset data
. webuse nhanes2
. svyset
. svy: logistic highbp sex##agegrp##c.bmi
. margins agegrp, vce(unconditional)
Examples: obtaining margins as though the data were balanced
Setup
. webuse acmemanuf
Balancing using asbalanced
. regress y pressure##temp
. margins, asbalanced
Balancing nonlinear responses
. logistic acceptable pressure##temp
. margins, asbalanced
Treating a subset of covariates as balanced
. webuse margex
. regress y arm##sex sex##agegroup
. margins, at((asbalanced) arm)
. margins, at((asbalanced) arm agegroup)
. margins, at((asbalanced) arm agegroup sex)
Balancing in the presence of empty cells
. webuse estimability
. regress y sex##group
. margins sex, asbalanced
. margins sex, asbalanced emptycells(reweight)
Video examples
Introduction to margins, part 1: Categorical variables
Introduction to margins, part 2: Continuous variables
Introduction to margins, part 3: Interactions
Addendum: Syntax of at()
In option at(atspec), atspec may contain one or more of the following
specifications:
varlist
(stat) varlist
varname = #
varname = (numlist)
varname = generate(exp)
where
1. varnames must be covariates in the current estimation results.
2. Variable names (whether in varname or varlist) may be
continuous variables, factor variables, or virtual level
variables, such as age, group, or 3.group.
3. varlist may also be one of three standard lists:
a. _all (all covariates),
b. _factor (all factor-variable covariates), or
c. _continuous (all continuous covariates).
4. stat can be any of the following:
-------------------------------------------------------------------------
Variables
stat Description allowed
-------------------------------------------------------------------------
asobserved at observed values in the sample (default) all
mean means (default for varlist) all
median medians continuous
p1 1st percentile continuous
p2 2nd percentile continuous
... 3rd-49th percentiles continuous
p50 50th percentile (same as median) continuous
... 51st-97th percentiles continuous
p98 98th percentile continuous
p99 99th percentile continuous
min minimums continuous
max maximums continuous
zero fixed at zero continuous
base base level factors
asbalanced all levels equally probable and sum to 1 factors
-------------------------------------------------------------------------
Any stat except zero, base, and asbalanced may be prefixed
with an o to get the overall statistic -- the sample over all
over() groups. For example, omean, omedian, and op25.
Overall statistics differ from their correspondingly named
statistics only when the over() or within() option is
specified. When no stat is specified, mean is assumed.
atspec can involve multiple settings for one covariate as well as
settings for multiple covariates. The following rules are applied when
more than one covariate or value is included:
1. When more than one covariate is referenced in atspec but each
covariate is set to only one value, all settings are applied in
combination. For example, at(x1=5 x2=0) results in margins being
estimated under one scenario, with x1 set to 5, x2 set to 0, and all
other covariates set to their observed values (the default).
2. When multiple values are specified for a covariate, the covariate
will be set to each of the values in turn. For example, at(x1=5
x1=10) or, equivalently, at(x1=(5 10)) specifies that x1 be set
first to 5 and then to 10.
3. When multiple values are specified for more than one covariate,
all possible combinations of settings are applied in turn. For
example, at(x1=(5 10) x2=(0 1)) results in margins being estimated
under four scenarios: (x1=5 x2=0), (x1=5 x2=1), (x1=10 x2=0), and
(x1=10 x2=1).
4. Settings may be specified for groups of covariates using three
general varlists -- _all, _factor, and _continuous. When atspec
includes both specifications with general varlists and
specifications with named covariates, the specifications for named
covariates take precedence over general ones. For example, at(x1=10
(means) _all) sets x1 to 10 while setting all other covariates to
their means.
5. Only one (stat) varlist specification can be applied to a
covariate. If more than one is specified, the rightmost
specification is respected. For example, at((means) x1 x2 (medians)
x1 x2) sets both x1 and x2 to their medians.
6. When both a (stat) specification and another specification are
included for a named covariate, the other specification takes
precedence. For example, at(x1=5 (means) x1) sets x1 to 5.
In addition, at() can be repeated. When multiple at() options are
specified, atspecs are processed sequentially. For instance, at(x1=5)
at(x2=0) results in margins being estimated under two scenarios. The
first sets x1 to 5 and all other covariates, including x2, to their
observed values. The second sets x2 to 0 and all other covariates to
their observed values. Note that this is different from the single
at(x1=5 x2=0) specification, which sets x1 and x2 to the specified values
simultaneously.
Stored results
margins stores the following in r():
Scalars
r(N) number of observations
r(N_sub) subpopulation observations
r(N_clust) number of clusters
r(N_psu) number of sampled PSUs, survey data only
r(N_strata) number of strata, survey data only
r(df_r) variance degrees of freedom, survey data only
r(N_poststrata) number of post strata, survey data only
r(k_predict) number of predict() options
r(k_margins) number of terms in marginlist
r(k_by) number of subpopulations
r(k_at) number of at() options
r(level) confidence level of confidence intervals
Macros
r(cmd) margins
r(cmdline) command as typed
r(est_cmd) e(cmd) from original estimation results
r(est_cmdline) e(cmdline) from original estimation results
r(title) title in output
r(subpop) subspec from subpop()
r(model_vce) vcetype from estimation command
r(model_vcetype) Std. Err. title from estimation command
r(vce) vcetype specified in vce()
r(vcetype) title used to label Std. Err.
r(clustvar) name of cluster variable
r(margins) marginlist
r(predict#_opts) the #th predict() option
r(predict#_label) label from the #th predict() option
r(expression) response expression
r(xvars) varlist from dydx(), dyex(), eydx(), or eyex()
r(derivatives) "", "dy/dx", "dy/ex", "ey/dx", "ey/ex"
r(over) varlist from over()
r(within) varlist from within()
r(by) union of r(over) and r(within) lists
r(by#) interaction notation identifying the #th
subpopulation
r(atstats#) the #th at() specification
r(emptycells) empspec from emptycells()
r(mcmethod) method from mcompare()
r(mcadjustall) adjustall or empty
Matrices
r(b) estimates
r(V) variance-covariance matrix of the estimates
r(Jacobian) Jacobian matrix
r(_N) sample size corresponding to each margin estimate
r(at) matrix of values from the at() options
r(chainrule) chain rule information from the fitted model
r(error) margin estimability codes;
0 means estimable,
8 means not estimable
r(table) matrix containing the margins with their standard
errors, test statistics, p-values, and confidence
intervals
margins with the post option also stores the following in e():
Scalars
e(N) number of observations
e(N_sub) subpopulation observations
e(N_clust) number of clusters
e(N_psu) number of sampled PSUs, survey data only
e(N_strata) number of strata, survey data only
e(df_r) variance degrees of freedom, survey data only
e(N_poststrata) number of post strata, survey data only
e(k_predict) number of predict() options
e(k_margins) number of terms in marginlist
e(k_by) number of subpopulations
e(k_at) number of at() options
Macros
e(cmd) margins
e(cmdline) command as typed
e(est_cmd) e(cmd) from original estimation results
e(est_cmdline) e(cmdline) from original estimation results
e(wtype) weight type
e(wexp) weight expression
e(title) title in estimation output
e(subpop) subspec from subpop()
e(model_vce) vcetype from estimation command
e(model_vcetype) Std. Err. title from estimation command
e(vce) vcetype specified in vce()
e(vcetype) title used to label Std. Err.
e(clustvar) name of cluster variable
e(properties) b V, or just b if nose is specified
e(margins) marginlist
e(asbalanced) factor variables fvset as asbalanced
e(asobserved) factor variables fvset as asobserved
e(predict#_opts) the #th predict() option
e(predict#_label) label from the #th predict() option
e(expression) prediction expression
e(xvars) varlist from dydx(), dyex(), eydx(), or eyex()
e(derivatives) "", "dy/dx", "dy/ex", "ey/dx", "ey/ex"
e(over) varlist from over()
e(within) varlist from within()
e(by) union of r(over) and r(within) lists
e(by#) interaction notation identifying the #th
subpopulation
e(atstats#) the #th at() specification
e(emptycells) empspec from emptycells()
Matrices
e(b) estimates
e(V) variance-covariance matrix of the estimates
e(Jacobian) Jacobian matrix
e(_N) sample size corresponding to each margin estimate
e(error) error code corresponding to e(b)
e(at) matrix of values from the at() options
e(chainrule) chain rule information from the fitted model
Functions
e(sample) marks estimation sample
