/****************************************************************************** *! version 3.0.1 Ian White 27may2015 Sigma = s' * sigma0 * s Given correlation matrix sigma0, unknown vector of SDs s IRW, 5may2011 ******************************************************************************/ prog def mvmeta_bscov_correlation, rclass syntax [if] [in], [log /// setup start(string) mmSigma(string) /// Set up mm1 mm2 notrunc /// Method of moments varparms(string) /// Within -ml- postfit /// After -ml- ] local p \$MVMETA_p marksample touse tempname startchol Sigma binit vinit init // GENERAL CODE if "\$MVMETA_taulog"=="taulog" { // Estimate tau on log scale local exp exp local tauname logtau } else { // Estimate tau on untransformed scale, but ignore negative sign local exp abs local tauname tau } forvalues r=1/`p' { local yname`r' = word("\$MVMETA_ylist",`r') } // IF RUN AT SET-UP STAGE if "`setup'"!="" { di as text "Note: variance-covariance matrix is " as result "correlation equal to \$MVMETA_sigma0exp" tempname junk cap mat `junk' = \$MVMETA_sigma0 + I(`p') if _rc { di as error "mvmeta_bscov_correlation: matrix must be `p'x`p'" exit 498 } // STARTING VALUES tempname startmat if "`start'"!="" { cap matrix `startmat' = `start' if !_rc cap assert rowsof(`startmat')==1 if !_rc cap assert colsof(`startmat')==`p' if _rc { di as error "Error in start(`start'): need 1 x `p' matrix" exit 499 } } else mat `startmat' = J(1,`p',1) forvalues r=1/`p' { mat `startmat'[1,`r'] = `exp'(`startmat'[1,`r']) local eqlist `eqlist' (`tauname'_`yname`r'':) local colnames `colnames' "`tauname'_`yname`r''" } mat `Sigma' = diag(`startmat') * (\$MVMETA_sigma0) * diag(`startmat') mvmeta_mufromsigma, sigma(`Sigma') mat `binit' = e(b) mat `vinit' = (`startmat') mat colnames `vinit' = `colnames' mat `init' = (`binit', `vinit') return local nvarparms = `p' return matrix binit=`binit' return matrix init=`init' // SET UP EQUATIONS return local eqlist `eqlist' } // Compute Sigma from row vector of variance parameters - used by mvmeta_lmata.ado if "`varparms'" != "" { forvalues r=1/`p' { mat `varparms'[1,`r'] = `exp'(`varparms'[1,`r']) } matrix `Sigma' = diag(`varparms') * (\$MVMETA_sigma0) * diag(`varparms') } if "`postfit'" != "" { tempname parmvec mat `parmvec' = J(1,`p',.) forvalues r=1/`p' { mat `parmvec'[1,`r'] = `exp'([`tauname'_`yname`r'']_b[_cons]) } matrix `Sigma' = diag(`parmvec') * (\$MVMETA_sigma0) * diag(`parmvec') mat rownames `Sigma' = \$MVMETA_ylist mat colnames `Sigma' = \$MVMETA_ylist // SET UP VARIANCE EXPRESSIONS tempname sigma0 mat `sigma0' = \$MVMETA_sigma0 forvalues r=1/`p' { forvalues s=1/`r' { local this = `sigma0'[`s',`r'] if `s'==`r' return local Sigma`s'`r' `this'*(`exp'([`tauname'_`yname`r'']_b[_cons]))^2 else return local Sigma`s'`r' `this'*`exp'([`tauname'_`yname`r'']_b[_cons])*`exp'([`tauname'_`yname`s'']_b[_cons]) } } } if !mi("`mm1'`mm2'") { // METHOD OF MOMENTS di as error "Sorry, method of moments is not yet implemented for covariance(proportional) exit 498 return scalar truncated = `truncated' return matrix Q = `Q' return matrix Qa = `Qa' return matrix Qb = `Qb' } return matrix Sigma = `Sigma' return scalar nparms_aux = `p' return scalar neqs_aux = `p' end