help proportion dialogs: proportion svy: proportion
also see: proportion postestimation
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Title
[R] proportion -- Estimate proportions
Syntax
proportion 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
nolabel suppress value labels from varlist
missing treat missing values like other values
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.
vce() and weights are not allowed with the svy prefix.
fweights, iweights, and pweights are allowed; see weight.
See [R] proportion postestimation for features available after
estimation.
Menu
Statistics > Summaries, tables, and tests > Summary and descriptive
statistics > Proportions
Description
proportion produces estimates of proportions, along with standard errors,
for the categories identified by the values in each variable of varlist.
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 strata identified in the stdize()
option.
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.
nolabel requests that value labels attached to the variables in varlist
be ignored.
missing specifies that missing values in varlist be treated as valid
categories, rather than omitted from the analysis (the default).
+------------+
----+ 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 proportion.
+-----------+
----+ 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 proportion but is not shown in the
dialog box:
coeflegend; see [R] estimation options.
Examples
Setup
. sysuse auto
Estimate proportions
. proportion rep78
Include missing values as a category of rep78
. proportion rep78, missing
Estimate proportions over values of foreign
. proportion rep78, over(foreign)
Saved results
proportion 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) proportion
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(namelist) proportion identifiers
e(label#) labels from #th variable in varlist
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
Matrices
e(b) vector of proportion 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] proportion
Help: [R] proportion postestimation;
[R] mean, [R] ratio, [R] total
