help mean dialogs: mean svy: mean
also see: mean postestimation
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Title
[R] mean -- Estimate means
Syntax
mean varlist [if] [in] [weight] [, options]
options description
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Model
stdize(varname) variable identifying strata for standardization
stdweight(varname) weight variable for standardization
nostdrescale do not rescale the standard weight variable
if/in/over
over(varlist, ...) group over subpopulations defined by varlist;
optionally, suppress group labels
SE/Cluster
vce(vcetype) vcetype may be analytic, cluster clustvar,
bootstrap, or jackknife
Reporting
level(#) set confidence level; default is level(95)
noheader suppress table header
nolegend suppress table legend
+ coeflegend display coefficients' legend instead of
coefficient table
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+ coeflegend does not appear in the dialog box.
bootstrap, jackknife, mi estimate, rolling, statsby, 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.
aweights are not allowed with the jackknife prefix.
vce() and weights are not allowed with the svy prefix.
fweights, pweights, aweights, and iweights are allowed; see weight.
See [R] mean postestimation for features available after estimation.
Menu
Statistics > Summaries, tables, and tests > Summary and descriptive
statistics > Means
Description
mean produces estimates of means, along with standard errors.
Options
+-------+
----+ Model +------------------------------------------------------------
stdize(varname) specifies that the point estimates be adjusted by direct
standardization across the strata identified by varname. This option
requires the stdweight() option.
stdweight(varname) specifies the weight variable associated with the
strata identified in the stdize() option. The standardization
weights must be constant within the standard strata.
nostdrescale prevents the standardization weights from being rescaled
within the over() groups. This option requires stdize() but is
ignored if the over() option is not specified.
+------------+
----+ if/in/over +-------------------------------------------------------
over(varlist [, nolabel]) specifies that estimates be computed for
multiple subpopulations, which are identified by the different values
of the variables in varlist.
When this option is supplied with one variable name, such as over(
varname), the value labels of varname are used to identify the
subpopulations. If varname does not have labeled values (or there
are unlabeled values), the values themselves are used, provided that
they are nonnegative integers. Noninteger values, negative values,
and labels that are not valid Stata names are substituted with a
default identifier.
When over() is supplied with multiple variable names, each
subpopulation is assigned a unique default identifier.
nolabel specifies that value labels attached to the variables
identifying the subpopulations be ignored.
+------------+
----+ SE/Cluster +-------------------------------------------------------
vce(vcetype) specifies the type of standard error reported, which
includes types that are derived from asymptotic theory, that allow
for intragroup correlation, and that use bootstrap or jackknife
methods; see [R] vce_option.
vce(analytic), the default, uses the analytically derived variance
estimator associated with the sample mean.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#); see [R] estimation options.
noheader prevents the table header from being displayed. This option
implies nolegend.
nolegend prevents the table legend identifying the subpopulations from
being displayed.
The following option is available with mean but is not shown in the
dialog box:
coeflegend; see [R] estimation options.
Examples
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Setup
. webuse fuel
Estimate the average mileage of the cars without the fuel treatment
(mpg1) and those with the fuel treatment (mpg2)
. mean mpg1 mpg2
Stack mpg1 on top of mpg2, creating mpg
. stack mpg1 mpg2, into(mpg) clear
Summarize mpg by type
. mean mpg, over(_stack)
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Setup
. webuse hbp, clear
Stratify sample by age, race, and sex
. egen strata = group(age race sex) if inlist(year, 1990, 1992)
Create standardization weight equal to sample size for each stratum
. by strata, sort: gen stdw=_N
Compute standardized mean using the observed distribution of age, race,
and sex as the standard
. mean hbp, over(city year) stdize(strata) stdweight(stdw)
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Setup
. webuse highschool, clear
Estimate a population mean using survey data
. svy: mean weight
Estimate mean of weight for each subpopulation identified by sex
. svy: mean weight, over(sex)
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Saved results
mean saves the following in e():
Scalars
e(N) number of observations
e(N_over) number of subpopulations
e(N_stdize) number of standard strata
e(N_clust) number of clusters
e(k_eq) number of equations in e(b)
e(df_r) sample degrees of freedom
e(rank) rank of e(V)
Macros
e(cmd) mean
e(cmdline) command as typed
e(varlist) varlist
e(stdize) varname from stdize()
e(stdweight) varname from stdweight()
e(wtype) weight type
e(wexp) weight expression
e(title) title in estimation output
e(cluster) name of cluster variable
e(over) varlist from over()
e(over_labels) labels from over() variables
e(over_namelist) names from e(over_labels)
e(vce) vcetype specified in vce()
e(vcetype) title used to label Std. Err.
e(properties) b V
e(estat_cmd) program used to implement estat
e(marginsnotok) predictions disallowed by margins
Matrices
e(b) vector of mean estimates
e(V) (co)variance estimates
e(_N) vector of numbers of nonmissing observations
e(_N_stdsum) number of nonmissing observations within the
standard strata
e(_p_stdize) standardizing proportions
e(error) error code corresponding to e(b)
Functions
e(sample) marks estimation sample
Also see
Manual: [R] mean
Help: [R] mean postestimation;
[R] ameans, [R] proportion, [R] ratio, [R] summarize, [R] total
