Recycle/reuse returned results in Stata

Recycle/reuse returned results in Stata

• UCLA: "How can I access information stored after I run a command in Stata (returned results)?" 
• The Stata Blog: Drukker (2015). Programming an estimation command in Stata: Where to store your stuff
• Stackoverflow (2014).Saving coefficients and standard errors as variables
• Lembcke (2009). Advanced Stata Topics
• SSCC. An Introduction to Mata
• Stata commands are grouped into 4 major categories: r-class, e-class, s-class, and n-class commands. Also a c-class contains the values of system parameters and settings, along with certain constants.
• The commands produce the statistical results are either r-class or e-class. e-class commands produce the estimation results, others are belong to r-class.
• After submitting "contrast", Stata generates a L matrix (r(L)), you can check the contrast coefficients using "matrix list r(L)".
• If don't know what results are outputted, use "return list" or "ereturn list" to find them. The scalar results from a r-class can be used with the "r(...)" and scalar results from e-class command can be used with "e(...)". Here, "..." is the name showed using "return list" or "ereturn list". The use of results in matrix form is a little tricky. "_b[...]" or "_se[...]" have to be used; here, "..." is the variable name of a coefficient in the model. The results for a constant is used as "_b[_cons]" for beta coefficient or "_se[_cons]" for standard error. A matric results can also converted into a matrix: "mat B=e(b)", then "disp B[rowno, colon]".
• To show variance-covariance matrix, use: "estat vce" or just simple "matlist e(V)", and to show correlation, use: "estat vce, correlation".
• You can "estimate store" and "estimate restore" a set of estimates with a name in memory, in such way, the following command will not be erased. If want to save and use it as a permanent file, you can use "estimate save" and "estimate use".
• A single number can been converted into scalar, for example, "scalar xyz=_b[agecat]". However, the scalar has to be used with a pseudofunction scalar(), for example, "display scalar(xyz)" (more info)
• The e(V) and e(b) matrices can be converted into variables of a dataset using "svmat" (convert variables into matrix using "mkmat"), which is similar to "putmat and getmat" of mata (matrix ref.):
• mat D = e(b)', e(b)'
• svmat double D, name(coef)
• mat se1=vecdiag(e(V))
• mat se2=vecdiag(e(V))
• mat SE = se1, se2
• svmat SE, name(se)
• The "ereturn display" can use the e(V) and e(b) matrices to return a r-class matrix "r(table)"
• "margins" also gives e-class results:
• webuse dollhill3,clear
• poisson deaths i.smokes##c.agecat, exposure(pyears)
• est store tempreg
• margins smokes, gen(dhat) predict(ir) // undocumented gen()
• mean dhat1 // for smokes = 0
• scalar dhat1=_b[dhat1] // .00810452
• margins smokes, eydx(agecat) predict(ir) post
• scalar eydxsmokes0=_b[0.smokes] // 1.046826
• est restore tempreg
• margins smokes, dydx(agecat) predict(ir) post
• scalar dydxsmokes0=_b[0.smokes] // .00848402
• disp scalar(dydxsmokes0)/scalar(dhat1) // gives 1.046826
• Gould(2010).Mata Matters; (2011).Mata, the missing manual. Baum(2009).Using Mata to work more effectively in Stata
• putmat and getmat - Put Stata variables into Mata and vice versa
• mata r2=(1\2\3)
• mata b=st_matrix("e(b)")'
• mata se=sqrt(diagonal(st_matrix("e(V)")))
• getmata r2 b se, force
• vwls b r2, sd(se)
• reg b r2
• Rename "rowname" and "colname" of a matrix

     program estmatrename, eclass
       matrix BB = e(b)
       matrix colnames BB = "1.race" "2.race" "3.race"
       ereturn repost b = BB, rename
       matrix VV = e(V)
       matrix colnames VV = "1.race" "2.race" "3.race"
       matrix rownames VV = "1.race" "2.race" "3.race"
       ereturn repost V = VV
     end

• total heartatk [pw=swgt], over(race)
• estmatrename
• lincom (_b[3.race]-_b[1.race])/2
• test _b[1.race]=_b[2.race]
• contrast {race 1 -1 0}
• contrast p(1).race
• Convert ln(RR) into RR and percent change
• webuse dollhill3
• poisson deaths smokes i.agecat,exposure(pyears) irr margins agecat, predict(ir) post
• qui nlcom (lnRR21:ln((_b[2.agecat]/_b[1.agecat])))(lnRR31:ln((_b[3.agecat]/_b[1.agecat]))) (lnRR41:ln((_b[4.agecat]/_b[1.agecat]))), post
• ereturn disp,eform(RR) cformat(%5.2f) pformat(%5.4f)
• mat rtable=r(table)'
• mat RR=rtable[1...,"b"],rtable[1...,"ll".."ul"]
• mata st_matrix("pctable",(st_matrix("RR"):-1):*100)
• mat coln pctable=RR LL UL
• matlist pctable, format(%10.2f)r