{smcl}
{hline}
help for {hi:brrcorr}
{hline}


{title:Correlations of variables with BRR-based significance calculations}


{p 8 17}{cmd:brrcorr}{space 2}[{it:varlist}] [{it:weight}] [{cmd:if} {it:exp}]
		[{cmd:in} {it:range}] [{cmd:,} 
		{cmd:pw} {cmdab:o:bs} {cmd:sig} {cmdab:p:rint(}{it:#}{cmd:)} {cmdab:st:ar(}{it:#}{cmd:)}
		{cmdab:b:onferroni} {cmdab:sid:ak}
		
{p 17 17}{cmdab:brrw:eight(}{it:varlist}{cmd:)} 
		{cmdab:fay(}{it:#}{cmd:)}
		{cmdab:dof(}{it:#}{cmd:)} ]

{cmd:by} {it:...} {cmd::} may be used with the above commands; see {help by}.

{cmd:pweights} are allowed; see {help weights}.


{title:Description}

{p}{cmd:brrcorr} displays the correlation matrix for {it:varlist} or, if {it:varlist} is not
specified, for all variables in the data.  Observations are excluded from the calculation due to missing values on
a casewise basis, unless the {cmd:pw} option is specified.

{p}Significance levels of the correlations are based on standard errors that are calculated 
using a series of user-supplied replication weights, by the
balanced repeated replication (BRR) method.  This is an alternate method to
the Taylor series linearization methods used by Stata's {help svy:svy-based} commands.


{title:Options}

{p 0 4}{cmd:brrweight()} specifies the list of variables that contain the replicate weights
for the dataset.  The standard errors for the model are based on the variation in 
the estimates generated across the various weights.

{p 4 4}A set of {cmd:brrweights} is required for the analysis.  Once the brrweights are specified
they are stored as a {help char:characteristic} of the dataset and need not be respecified in subsequent command.

{p 0 4}{cmd:fay()} specifies the k value that should be used for weighting the estimates, based
on Fay's method.  The default is zero, meaning that simple averaging will be used.  As with the replicate weights,
the value for fay() is stored as a characteristic of the dataset once it is specified, and need not be
re-specified in subsequent commands.

{p 0 4}{cmd:dof()} specifies the degrees of freedom for model fit and t-statistics.  The default is
to use the number of replications.

{p 0 4}{cmd:pw} specifies that observations are to be excluded from the calculations on a pairwise basis.  The default is 
casewise.

{p 0 4}{cmd:obs} adds a line to each row of the matrix reporting the number of observations used 
in calculating the correlation coefficient.

{p 0 4}{cmd:sig} adds a line to each row of the matrix reporting the significance level of each correlation coefficient.

{p 0 4}{cmd:print(}{it:#}{cmd:)} specifies the significance level of correlation coefficients to be printed.  
Coefficients with larger significance levels are left blank.  {cmd:print(10)} or {cmd:print(.1)}
would list only coefficients significant at the 10% level or better.

{p 0 4}{cmd:star(}{it:#}{cmd:)} specifies the significance level of coefficients to be starred.
{cmd:star(5)} or {cmd:star(.05)} would star all coefficients significant at the 5% level or better.

{p 0 4}{cmd:bonferroni} makes the Bonferroni adjustment to calculated significance levels.  
This affects printed significance levels and the {cmd:print()} and {cmd:star()} options.  
{cmd:"pwcorr, print(.05) bonferroni"} prints coefficients with Bonferroni-adjusted 
significance levels of .05 or less.

{p 0 4}{cmd:sidak} makes the Sidak adjustment to calculated significance levels.  
This affects printed significance levels and the {cmd:print()} and {cmd:star()} 
options.  {cmd:"pwcorr, print(.05) sidak"} prints coefficients with Sidak-adjusted
significance levels of .05 or less.


{title:Examples}

{p 8 12}{inp:. brrcorr dvcrate medage [pw=wgt], brrw(bw*)}

{p 8 12}{inp:. brrcorr mrgrate dvcrate medage, pw obs sig} 



{title:Methods and formulae}

{p}Point estimates are calculated using aweights.  
Significance levels of the estimated correlations are based on the variance matrix of each estimate, 
calculated as follows:

                   G    
          V = c * SUM [ (r - r(i))^2 ]
                  i=1

{p}where r is the estimated coefficient based on the full sample weights, 
r(i) is the estimated coefficient using the i'th set of replicate weights, 
G is the number of replicates, and c is a constant defined as:

         1 / G            for standard BRR (i.e. fay==0), or

         1 / (G*(1-k)^2)  for Fay's method.
         
{p}For calculation of variances and significances, the correlations are first
transformed using Fisher's Z transformation, in order to normalize the distribution.


{title:Saved Results}

{p}{cmd:brrcorr} saves in r() the following for the last-calculated correlation:

        rho  the estimated correlation coefficient
        N    the number of observations
        p    the p-level


{title:Acknowledgements}

{p}These commands consists largely of the ado file code from official Stata's {cmd:pwcorr} command.
I would like to thank Bobby Gutierrez at StataCorp for advice on implementation of BRR.


{title:Author}

	Nick Winter
	Cornell University
	nw53@cornell.edu