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svyreg.hlp
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.-
help for ^svyreg^, ^svylogit^, ^svyprobt^ (manual: ^[R] svyreg^)
.-
Linear, logistic, and probit regressions for survey data
--------------------------------------------------------
^svyreg^ varlist [weight] [^if^ exp] [^in^ range] [^,^ common_options]
^svylogit^ varlist [weight] [^if^ exp] [^in^ range] [^, or^ common_options
maximize_options ]
^svyprobt^ varlist [weight] [^if^ exp] [^in^ range] [^,^ common_options
maximize_options ]
where common_options are
^nocon^stant ^str^ata^(^varname^) psu(^varname^) fpc(^varname^)^
^sub^pop^(^varname^) srs^subpop ^noadj^ust
^l^evel^(^#^) p^rob ^ci deff deft meff meft^
These commands share the features of all estimation commands; see help @est@.
The commands typed without arguments redisplay previous results. The
following options may be given when redisplaying results
^l^evel^(^#^) p^rob ^ci deff deft meff meft^
^svyreg^ allows ^pweight^s and ^iweight^s. ^svylogit^ and ^svyprobit^ allow ^pweight^s.
See help @weights@.
Warning: Use of ^if^ or ^in^ restrictions will not produce correct variance
estimates for subpopulations in many cases. To compare estimates
for subpopulations, use the ^by()^ or ^subpop()^ options.
Description
-----------
These commands estimate regression models for complex survey data.
^svyreg^ estimates linear regression.
^svylogit^ estimates pseudo-maximum-likelihood logistic regression.
^svyprobt^ estimates pseudo-maximum-likelihood probit model.
The dependent variable for ^svylogit^ and ^svyprobt^ should be a 0/1 variable
(or, more precisely, a zero/nonzero variable).
The commands allow any or all of the following: probability sampling weights,
stratification, and clustering. Associated variance estimates, design effects
(deff and deft), and misspecification effects (meff and meft) are computed.
The ^subpop()^ option will give estimates for a single subpopulation. For a
general discussion of various aspects of survey designs, including multistage
designs, see ^[U] 36 Overview of survey estimation^.
To describe strata and PSUs of your data and to handle the error message
"stratum with only one PSU detected", see help @svydes@.
To estimate linear combinations of coefficients and odds ratio for any
covariate group relative to another, see help @svylc@. To perform hypothesis
tests, see help @svytest@.
See help @_robust@ for a programmer's command that can compute variance
estimates for survey data for a user-defined program.
Options
-------
^noconstant^ estimates a model with an intercept.
^strata()^, ^psu()^, and ^fpc()^ are described in ^svyset^; see help @svyset@.
^by(^varlist^)^ specifies that estimates be computed for the subpopulations
defined by different values of the variable(s) in the varlist.
^subpop(^varname^)^ specifies that estimates be computed for the single
subpopulation defined by the observations for which varname~=0.
Typically, varname=1 defines the subpopulation and varname=0 indicates
observations not belonging to the subpopulation. For observations whose
subpopulation status is uncertain, varname should be set to missing.
^srssubpop^ can only be specified if ^by()^ or ^subpop()^ is specified. ^srssubpop^
requests that deff and deft be computed using an estimate of simple-
random-sampling variance for sampling within a subpopulation. If
^srssubpop^ is not specified, deff and deft are computed using an estimate
of simple-random-sampling variance for sampling from the entire
population. Typically, ^srssubpop^ would be given when computing
subpopulation estimates by strata or by groups of strata.
^noadjust^ specifies that the model Wald test be carried out as W/k distributed
F(k,d), where W is the Wald test statistic, k is the number of terms in
the model excluding the constant, d = total number of sampled PSUs minus
total number of strata, and F(k,d) is an F distribution. By default, an
adjusted Wald test is conducted: (d-k+1)W/(kd) distributed F(k,d-k+1).
^or^ (^svylogit^ only) reports the estimated coefficients transformed to odds
ratios, i.e., exp(b) rather than b. Standard errors and confidence
intervals are similarly transformed.
^level(^#^)^ specifies the confidence level (i.e., nominal coverage rate), in
percent, for confidence intervals; see help @level@.
^prob^ requests that the t statistic and p-value be displayed. The degrees of
freedom for the t are d = total number of sampled PSUs minus the total
number of strata (regardless of the number of terms in the model). If no
display options are specified then, by default, the t statistic and p-
value are displayed.
^ci^ requests that confidence intervals be displayed. If no display options are
specified then, by default, confidence intervals are displayed.
^deff^ requests that the design-effect measure deff be displayed. See
^[R] svymean^ for details.
^deft^ requests that the design-effect measure deft be displayed. See
^[R] svymean^ for details.
^meff^ requests that the meff measure of misspecification effects be displayed.
See ^[R] svymean^ for details.
^meft^ requests that the meft measure of misspecification effects be displayed.
See ^[R] svymean^ for details.
maximize_options control the maximization process; see ^[R] maximize^. You
should never have to specify them.
Examples
--------
. ^svyset pweight leadwt^
. ^svyset strata strata^
. ^svyset psu psu^
. ^svyreg loglead age female black race region2-region4^
. ^svylogit highbp height weight age age2 female^
. ^svylogit, or^
. ^svylogit highlead age, subpop(female) or^
Also see
--------
Manual: ^[U] 26 Estimation and post-estimation commands^
^[U] 35 Overview of model estimation^
^[U] 36 Overview of survey estimation^
^[R] svyreg^
On-line: help for @lincom@, @_robust@, @svy@, @svydes@, @svylc@, @svymean@, @svyset@,
@svytest@