{smcl}
{hline}
help for {hi:svrmodel}
{hline}
{title:Model estimates with balance repeated replication (BRR) based standard errors}
{p 8 15}{cmd:svrmodel} {it:varlist} [{cmd:if} {it:exp}] [{cmd:in} {it:range}] [{cmd:,}
{cmd:deff} {cmd:deft}
{cmd:cmd(}{it:command name}{cmd:)}
{cmdab:l:evel(}{it:#}{cmd:)} {cmd:or} {it:command_options} ]
{p}This command is for use with replication weights. You must set your data for
replication based survey estimation with {help svrset} or {help survwgt} before
using this command.
{p}Typing {cmd:svrmodel} without arguments
redisplays previous results.
{title:Description}
{p}{cmd:svrmodel} estimates regression-type models for complex survey data.
Standard errors are calculated using a series of user-supplied replication weights, by
balanced repeated replication (BRR) or survey jackknife (JK1, JK2, or JKn).
This is an alternate method to the Taylor series linearization methods
used by Stata's {help svy} commands. See {help survwgt} for details on the
creation of weights and estimation of variances with replication.
{p}{cmd:svrmodel} will run ols regression, logit/probit, ordered logit/probit, multinomial
logit, and poisson regression models. For other models, see {help svrest}.
{title:Options}
{p 0 4}{cmd:deff} and {cmd:deft} request that design effects deff and deft be displayed with the model
estimates. See {cmd:[R] svymean} for details.
{p 0 4}{cmd:cmd()} specifies the model estimation command.
Valid options are {help regress} {help logit} {help probit} {help logistic}
{help oprobit}, {help ologit}, {help mlogit} and {help poisson}. The default is ols regression.
{p 0 4}{cmd:or} specifies that coefficients from a logit model should be displayed as odds-ratios. See {help logit}.
{title:Example command and output}
{p 8 12}{inp:. svrmodel income region sex asset } {p_end}
{txt} OLS estimates with replicate-based (jkn) standard errors
{txt} Analysis weight: wgt{col 48}Number of obs ={res}{ralign 10:1904}
{txt} Replicate weights: {help svrset:jkn_1...}{txt}{col 48}Population size ={res}{ralign 10:10738}
{txt} Number of replicates: 30{txt}{col 48}Degrees of freedom{col 68}={res}{ralign 10:20}
{txt} {col 48}F({res}{ralign 4:3}{txt},{res}{ralign 7:18}{txt}) ={res}{ralign 10:86.40}
{txt} {col 48}Prob > F{col 68}={res}{ralign 10:0.0000}
{txt} {col 48}R-squared{col 68}={res}{ralign 10:0.0057}{txt}
{hline 13}{c TT}{hline 64}
income {c |} Coef. Std. Err. t P>|t| [95% Conf. Interval]
{hline 13}{c +}{hline 64}
region {c |}{res} .6214526 .116294 5.34 0.000 .3788676 .8640376
{txt} sex {c |}{res} -1.121041 .2671536 -4.20 0.000 -1.678313 -.5637684
{txt} asset {c |}{res} .1274654 .0077771 16.39 0.000 .1112427 .1436881
{txt} _cons {c |}{res} 37.61598 .5194737 72.41 0.000 36.53238 38.69959
{txt}{hline 13}{c BT}{hline 64}
{title:Saved Results}
{cmd:svrmodel} is an estimation command, so it saves model estimates and svr-based (co)variance
matrix in e(b) and e(V) and creates e(sample) to reflect the estimation sample. It also stores design effects
in e(deff) and e(deft), and the simple-random-sampling-without-replacement (co)variance matrix in e(V_srs).
{p}{help svytest} will estimate adjusted Wald linear hypothesis tests after BRR model estimation.
({cmd:svrmodel} specifies the estimation command as "svysvrmodel" in order to allow {cmd:svytest} to function.)
Scalars e(N_strata) and e(N_psu) are set in order to allow {cmd:svytest} to operate correctly. N_strata
is set to the degrees of freedom for the model (by default the number of replicates), and N_psu is set to twice the
degrees of freedom.
{help predict} after an estimation should work as documented for the relevant command. {input}Warning: this
has not been tested extensively{txt}.
{title:Methods and formulae}
{p}See {help survwgt}.
{title:Acknowledgements}
{p}I would like to thank Bobby Gutierrez at StataCorp for advice on implementation of BRR.
{title:Author}
Nick Winter
Cornell University
nw53@cornell.edu