I would like Stata to develop some options for programming options for Mata to "easily" develop programs that can use multiple processors at the same time.
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That would be awesome. Something like parallised loops would be a great thing!Originally posted by FernandoRios View PostComment
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This is very basic, but:
Robust ANOVA options, like Welch's F-test or Brown-Forsythe's F-test.
Psychology has a fixation with ANOVA and it's still pretty normative for reviewers to request these. I <think> these as add-ons to the oneway or anova command as options would be terrific.Comment
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David Speed, I agree with your suggestions in #513. Meanwhile, here is some code I cobbled together earlier this year to compute Welch's F. Maybe it will help in the meanwhile.
Code:// Date: 15-Jun-2021 // Use equations shown in Andy Field's document to compute // Welch's F-test. // https://www.discoveringstatistics.com/docs/welchf.pdf // The data AF uses come from this document: // https://www.discoveringstatistics.com/repository/contrasts.pdf clear input byte group y 1 3 1 2 1 1 1 1 1 4 2 5 2 2 2 4 2 2 2 3 3 7 3 4 3 5 3 3 3 6 end preserve drop if missing(group) statsby Ybar=r(mean) Var=r(Var) n=r(N), by(group) clear: summarize y list quietly { generate double Wt = n/Var generate double WtMean = Wt*Ybar egen double SumWt = total(Wt) egen double SumWtMean = total(WtMean) generate double WelchGM = SumWtMean/SumWt generate double WtSqDev = Wt*(Ybar-WelchGM)^2 egen double SSmodel = total(WtSqDev) generate byte k = _N // # of groups generate double MSmodel = SSmodel/(k-1) generate double Lterm = (1-Wt/SumWt)^2 / (n-1) egen double lambda = total(Lterm) replace lambda = lambda*3/(k^2-1) generate double MSerror = 1+2*lambda*(k-2)/3 generate double WelchF = MSmodel/MSerror generate byte df1 = k-1 generate double df2 = 1/lambda generate double p = Ftail(df1,df2,WelchF) } list WelchF-p in 1 display _newline /// "From SPSS ONEWAY:" _newline /// "Welch F = "4.320451 _newline /// " df1 = " 2 _newline /// " df2 = " 7.943375 _newline /// " p = " .053738 restore // Date: 17-Aug-2021 // Compare Welch's F-test to multilevel modeling approach // in post #3 here: // https://www.statalist.org/forums/forum/general-stata-discussion/general/1427959-anova-rejection-of-bartlett-test // Note that I have added the dfmethod(anova) option. mixed y i.group, ml residuals(independent, by(group)) /// nolrtest nolog dfmethod(anova) margins group margins group, pwcompare(effects) mcompare(noadjust) // Compare to -regress- with vce(robust) regress y i.group, vce(robust)--
Bruce Weaver
Email: [email protected]
Web: http://sites.google.com/a/lakeheadu.ca/bweaver/
Version: Stata/MP 18.0 (Windows)Comment
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I'd guess that if this was technically feasible it would have been done a long time ago but I'll put in a pitch for "on the fly" use of variable transformations in estimation commands, e.g.A subset of RHS transformations can be effected using factor variable notation, of course.Code:reg log(y) x1 x2 poisson y x x^2 x^3 etc.
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John Mullahy;n1686463]I'd guess that if this was technically feasible it would have been done a long time ago but I'll put in a pitch for "on the fly" use of variable transformations in estimation commands, e.g.A subset of RHS transformations can be effected using factor variable notation, of course.[/QUOTE]Code:reg log(y) x1 x2 poisson y x x^2 x^3 etc.
I think it must be easily implementable since first differences in the time domain are possible and that's a simple mathematical operations.
Even from a coding perspective it should be easy to implement. Neither (, ^ or other mathematical operators are allowed in variablenames.
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Interesting.
I think someone else already did something like that some time ago. I also wrote a small script that works at least with regress -fgreg- (function for regress).
It actually does what John suggests, with the caveat that it "recognizes" functions based on spaces.
fgreg y x1 x2 x1*x2 x1^2
is valid, but this:
freg y x1 x2 x1 ^ 2
is not.
Something i haven't thought about it. I could technically combine this with -f_able- to still be able to estimate marginal effects!
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John Mullahy #515 , indeed Jeroen Weesie contributed -exprcmd- and -expr- to the STB way back in 1998. I still use it for quick exploration.
His -expr:- can be used as a prefix as follows.
Alternatively, issuing his -exprcmd- once turns on his interpreter until it is subsequently turned off like this:Code:. sysuse auto (1978 automobile data) . expr: reg log(price) weight rep78 Expression _Expr0 := log(price) -> reg _Expr0 weight rep78 Source | SS df MS Number of obs = 69 -------------+---------------------------------- F(2, 66) = 21.24 Model | 4.00927365 2 2.00463683 Prob > F = 0.0000 Residual | 6.22817243 66 .094366249 R-squared = 0.3916 -------------+---------------------------------- Adj R-squared = 0.3732 Total | 10.2374461 68 .150550678 Root MSE = .30719 ------------------------------------------------------------------------------ _Expr0 | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- weight | .000333 .0000513 6.50 0.000 .0002307 .0004354 rep78 | .1272241 .0410658 3.10 0.003 .0452337 .2092146 _cons | 7.196296 .2500145 28.78 0.000 6.697125 7.695466 ------------------------------------------------------------------------------ . expr: poisson price weight weight^2 rep78 Expression _Expr0 := weight^2 -> poisson price weight _Expr0 rep78 Iteration 0: log likelihood = -21667.605 Iteration 1: log likelihood = -21655.464 Iteration 2: log likelihood = -21655.463 Poisson regression Number of obs = 69 LR chi2(2) = 36753.75 Prob > chi2 = 0.0000 Log likelihood = -21655.463 Pseudo R2 = 0.4591 ------------------------------------------------------------------------------ price | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- weight | -.0005043 .0000145 -34.90 0.000 -.0005326 -.000476 _Expr0 | 1.36e-07 2.20e-09 61.86 0.000 1.32e-07 1.40e-07 rep78 | .0955141 .0018534 51.53 0.000 .0918815 .0991466 _cons | 8.555204 .025696 332.94 0.000 8.50484 8.605567 -
His package also includes a cute utility -diest- to display the variable definitions of the temporary variables constructed by -expr- or -exprcmd-:Code:. sysuse auto (1978 automobile data) . exprcmd (expr) . reg log(price) weight rep78 Expression _Expr0 := log(price) -> reg _Expr0 weight rep78 Source | SS df MS Number of obs = 69 -------------+---------------------------------- F(2, 66) = 21.24 Model | 4.00927365 2 2.00463683 Prob > F = 0.0000 Residual | 6.22817243 66 .094366249 R-squared = 0.3916 -------------+---------------------------------- Adj R-squared = 0.3732 Total | 10.2374461 68 .150550678 Root MSE = .30719 ------------------------------------------------------------------------------ _Expr0 | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- weight | .000333 .0000513 6.50 0.000 .0002307 .0004354 rep78 | .1272241 .0410658 3.10 0.003 .0452337 .2092146 _cons | 7.196296 .2500145 28.78 0.000 6.697125 7.695466 ------------------------------------------------------------------------------ (expr) . poisson price weight weight^2 rep78 Expression _Expr0 := weight^2 -> poisson price weight _Expr0 rep78 Iteration 0: log likelihood = -21667.605 Iteration 1: log likelihood = -21655.464 Iteration 2: log likelihood = -21655.463 Poisson regression Number of obs = 69 LR chi2(2) = 36753.75 Prob > chi2 = 0.0000 Log likelihood = -21655.463 Pseudo R2 = 0.4591 ------------------------------------------------------------------------------ price | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- weight | -.0005043 .0000145 -34.90 0.000 -.0005326 -.000476 _Expr0 | 1.36e-07 2.20e-09 61.86 0.000 1.32e-07 1.40e-07 rep78 | .0955141 .0018534 51.53 0.000 .0918815 .0991466 _cons | 8.555204 .025696 332.94 0.000 8.50484 8.605567 ------------------------------------------------------------------------------ (expr) . .
It would be nice if these commands were updated to accommodate some of the capabilities of newer Stata versions.Code:. sysuse auto (1978 automobile data) . expr: reg log(price) weight weight^2 rep78 Expression _Expr0 := log(price) Expression _Expr1 := weight^2 -> reg _Expr0 weight _Expr1 rep78 Source | SS df MS Number of obs = 69 -------------+---------------------------------- F(3, 65) = 18.23 Model | 4.67772488 3 1.55924163 Prob > F = 0.0000 Residual | 5.5597212 65 .085534172 R-squared = 0.4569 -------------+---------------------------------- Adj R-squared = 0.4319 Total | 10.2374461 68 .150550678 Root MSE = .29246 ------------------------------------------------------------------------------ _Expr0 | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- weight | -.00067 .0003621 -1.85 0.069 -.0013932 .0000532 _Expr1 | 1.60e-07 5.74e-08 2.80 0.007 4.58e-08 2.75e-07 rep78 | .0963816 .0406237 2.37 0.021 .0152506 .1775127 _cons | 8.768601 .6107286 14.36 0.000 7.548892 9.98831 ------------------------------------------------------------------------------ . diest ------------------------------------------------------------------------------ _Expr0 log(price) | Coef. Std. Err. t P>|t| -----------------------------------------+------------------------------------ weight Weight (lbs.) | -.00067 .0003621 -1.850 0.069 _Expr1 weight^2 | 1.60e-07 5.74e-08 2.796 0.007 rep78 Repair record 1978 | .0963816 .0406237 2.373 0.021 _cons | 8.768601 .6107286 14.358 0.000 ------------------------------------------------------------------------------
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A minor but nice option would be to allow the xline and yline options in twoway to have suboptions that would result in the line(s) appearing atop the plotted information rather than beneath it.
In the following code I've used the standard approach and then generated the desired result using twoway function to draw the lines atop of the plotted information.
Code:cap preserve cap drop _all sysuse auto qui sum mpg loc m=r(mean) tw hist mpg, plotr(marg(zero)) xli(`m', lco(red)) saving(gsl1, replace) tw (hist mpg, plotr(marg(zero)) leg(off)) /// (function y=`m', horiz ra(0 .1) lco(red) lpa(solid) saving(gsl2, replace)) drop _all set seed 2345 set obs 2000 gen x=rnormal(0 1) gen y=rnormal(0 1) qui sum x loc mx=r(mean) loc mnx=r(min) loc mxx=r(max) qui sum y loc my=r(mean) loc mny=r(min) loc mxy=r(max) tw scatter y x, plotr(marg(zero)) xli(`mx', lco(red)) yli(`my',lco(red)) xla(-2(1)5) saving(gsl3, replace) tw (scatter y x, plotr(marg(zero)) xsc(r(`mnx' `mxx')) xla(-2(1)5) ysc(r(`mny' `mxy')) leg(off)) /// (function y=`my', ra(`mnx' `mxx') lco(red) lpa(solid) ) /// (function y=`mx', horiz ra(`mny' `mxy') lpa(solid) lco(red) saving(gsl4, replace)) cap restore
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I haven't checked if anyone else asked for this.
I've just started using frames, and it would be of great help if -frame put- had an option replace and an option change.
Kind regards
nhbComment
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LASSO for survival analysis (Cox)? Having the capability to do Tukey post hoc tests after ANOVA would be great too.Comment
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According to the Stata/MP performance report, many functions benefit from having multiple cores, at the exception of mgarch and mswitch; the problem with mgarch and mswitch is that they can take a very long time to run; my suggestion for Stata 18 would be to parallelize those, but I would like to ask the community first if it's even possible, does the algorithm underneath allows parallelization for those two commands?Comment
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I would love to see some built-in JSON functionality , despite its flexibility and non-rectangular philosophy. Currently , we depend on python libraries, via SFI, or community-packages, -jsonio- (https://wbuchanan.github.io/StataJSON/) and -insheetjson- (http://fmwww.bc.edu/RePEc/bocode/i/insheetjson.html)
A drop-down command in File section menu to convert it to a mata matrix or associative arrays, would be a possible first step towards.
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Bruce Weaver
Hi Bruce,
I also had to use Field's equations to figure out what was going on with the robust versions. I've wrote code awhile back to produce Brown-Forsythe's and Welch's F statistics, but it's much clunkier than yours - I'd never seen the statsby command before!
Cheers,
David.Comment
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