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| From | David Souther <davidsoutheremail@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: Stata 12 Announcement |
| Date | Sun, 26 Jun 2011 21:20:36 -0500 |
Very exciting, but really hope theyve upped the limit in the number of letters allowed in a string variable (and variable label) - especially since they are allowing for more import/export to excel (which supports much, much longer strings in cells). DS On Mon, Jun 27, 2011 at 2:54 AM, John Antonakis <John.Antonakis@unil.ch> wrote: > Fantastic; particularly the SEM module. It's really helpful to be able to do > simultaneous equations with latent variables in Stata. I hope the Stata SEM > module will be developed further (the benchmark SEM program at this point > being MPlus, though you probably are a step ahead in some aspects, or could > be, e.g., limited information estimation of structural equation models using > 2SLS, Hausman tests, etc.; if those capabilities are not in there now I > really hope that they will be added too someday)! > > Well done, Stata! Really well done! > > John. > > __________________________________________ > > Prof. John Antonakis > Faculty of Business and Economics > Department of Organizational Behavior > University of Lausanne > Internef #618 > CH-1015 Lausanne-Dorigny > Switzerland > Tel ++41 (0)21 692-3438 > Fax ++41 (0)21 692-3305 > http://www.hec.unil.ch/people/jantonakis > > Associate Editor > The Leadership Quarterly > __________________________________________ > > > On 27.06.2011 01:07, William Gould, StataCorp LP wrote: >> >> Following long tradition, we are informing Statalist first: >> >> Stata 12 begins shipping Monday, July 25. >> >> Orders are now being accepted at http://www.stata.com. >> >> Below are some highlights. >> >> >> --------------------------- >> Automatic memory management >> --------------------------- >> >> Automatic memory management means that you no longer have to >> -set memory- and never again will you be told that there is no >> room because you set too little! Stata automatically adjusts its >> memory usage up or down according to current requirements. >> >> The memory manager is tunable. You can set a maximum if you wish. >> Old do-files can still -set memory-. Stata merely responds, "-set >> memory- ignored". >> >> We have tested the memory manager on systems with 1 TB (the largest >> currently available), and it is designed to scale to even more >> memory. >> >> >> ----------------------------------------------------------- >> Import Excel files, export PDFs, and new interface features >> ----------------------------------------------------------- >> >> Importing Excel files is easy. And the new Import Preview Tool >> lets you see the file's contents and adjust import settings before >> you import it. >> >> You can now directly export PDFs of graphs and logs. >> >> Stata's windows are now laid out to fit wider screens better. You >> can still get back the old layout from Edit -> Preferences. >> >> A new Properties window -- always available -- lets you manage >> your variables, including their names, labels, value labels, >> notes, formats, and storage types. >> >> The Viewer is now tabbed, and it has buttons at the top to access >> dialogs, to jump within the document, and to jump to Also See >> documents. >> >> The Data Editor also has a new Properties window; has another tool >> that lets you Hide, Show, Filter, and Reorder the variables; and >> has the new Clipboard Preview tool, which lets you see and prepare >> your raw data before pasting. >> >> >> ---------------------------------- >> Structural equation modeling (SEM) >> ---------------------------------- >> >> -sem- is a new estimation command, itself the subject of >> an entire manual. >> >> If you are new to SEM, you should be interested if you fit linear >> regressions, multivariate regressions, seemingly unrelated >> regressions, or simultaneous systems, or if you're interested in >> generalized method of moments (GMM). And if you think you are >> still not interested, take a look anyway. SEM is a remarkably >> flexible framework. >> >> If you know about SEM, you will be more interested in path >> analysis models, single- and multiple-factor measurement models, >> MIMIC models, latent growth models, correlated uniqueness models, >> and more, all of which can be fit by -sem-. You will also be >> interested in -sem-'s standardized and unstandardized coefficients, >> direct and indirect effects, goodness-of-fit statistics, >> modification indices, predicted values and factor scores, and >> groupwise analysis with tests of invariance. >> >> You can use the GUI or command language to specify your model. >> The command language is a variation on standard path notation. >> You can type >> >> . sem (L1 -> m1 m2 m3) >> (L2 -> m4 m5) >> (L1 -> L2) >> >> In -sem-, lowercase names refer to variables in the data and >> uppercase names are latent variables. The above corresponds to >> >> m1 = a1 + b1*L1 + e1 >> m2 = a2 + b2*L1 + e2 >> m3 = a3 + b3*L1 + e3 >> >> m4 = a4 + b4*L2 + e4 >> m5 = a5 + b5*L2 + e5 >> >> L2 = c1 + d1*L1 + e6 >> >> Maximum likelihood (ML) and asymptotic distribution free (ADF) >> estimation methods are provided. ADF is generalized method of >> moments (GMM). Robust estimates of standard errors and SEs for >> clustered samples are available, as is full support for survey >> data via the -svy:- prefix. Missing at random (MAR) data are >> supported via FIML. >> >> >> ---------------------------------------- >> Survey, cluster robust, and mixed models >> ---------------------------------------- >> >> -xtmixed- now supports sampling weights and robust and cluster- >> robust standard errors for use with survey data, although you do >> *NOT* use the -svy:- prefix as you might have expected. >> >> That is because multilevel models with survey data differ from >> standard models in that sampling weights need to be specified at >> each modeling level rather than just at the observation level. >> Sampling weights must reflect selection probability conditional on >> selection at the next highest level. >> >> Thus, -xtmixed- expects you to specify a weight for each level in >> your model and warns you if you do not. >> >> >> ------------------- >> Multiple imputation >> ------------------- >> >> -mi impute- now supports >> >> 1. Chained equations. >> Chained equations are used to impute missing values when >> variables may be of different types and missing-value >> patterns are arbitrary. The first variable could be >> imputed using logit, the second using linear regression, >> and the third using multinomial logistic regression. >> >> 2. Conditional imputation. >> Conditional imputation is customized imputation within >> group when group itself might be imputed. You can >> restrict imputation of number of pregnancies to females >> even when female itself contains missing values and so is >> being imputed. >> >> 3. Imputation by groups. >> Australians could have their missing values imputed using >> data from other Australians only. >> >> -mi estimate- now >> >> 1. Supports panel-data and multilevel models, so you can use >> -mi- with -xtreg- or -xtmixed-. >> >> 2. Allows you to measure the amount of simulation error in >> your final model, so you can decide whether you need more >> imputations. >> >> -mi predict- and -mi predictnl- create linear and nonlinear >> predictions in the original (m=0) data, and not just for complete >> observations but also for observations with missing values. >> >> >> ----------- >> Time series >> ----------- >> >> Check out the >> >> 1. New estimators for >> a. GARCH >> b. ARFIMA >> c. UCM >> >> 2. New postestimation command -psdensity- to estimate the >> spectral density of a stationary process using the >> parameters of a previously estimated parametric model. >> >> 3. New command -tsfilter-, which filters a series to keep only >> selected periodicities (frequencies) and which can be used >> to separate a series into trend and cyclical components. >> >> Multivariate GARCH deals with models of time-varying volatility in >> multiple series. These models allow the conditional covariance >> matrix of the dependent variables to follow a flexible dynamic >> structure and the conditional mean to follow a >> vector-autoregressive (VAR) structure. >> >> ARFIMA is a generalization of the ARMA and ARIMA models. ARMA >> models assume short memory. ARIMA models assume shocks are >> permanent. ARFIMA provides the middle ground. ARFIMA stands for >> autoregressive, fractionally integrated moving average. >> >> UCM stands for unobserved component model and decomposes a series >> into trend, seasonal, cyclic, and idiosyncratic components after >> controlling for optional exogenous variables. >> >> >> ------------------ >> Business calendars >> ------------------ >> >> There is a new %t format: %tb. The b stands for business >> calendars. Business calendars allow you to define your own >> calendars so that dates display correctly and lags and leads work >> as they should. >> >> You could create file lse.stbcal that records the days the London >> Stock Exchange is open (or closed) and then Stata would understand >> format %tblse just as it understands the usual date format %td. >> >> Once you define a calendar, Stata deeply understands it. You can, >> for instance, easily convert between %tblse and %td values. >> >> >> ----------------------------------- >> Constrasts and pairwise comparisons >> ----------------------------------- >> >> We were tempted to call this "Stata for Experimentalists" except >> that the features are useful to Stata users of all disciplines. >> >> Contrasts, pairwise comparisons, and margins plots are about >> understanding and communicating results from your model. How does >> a covariate affect the response? Is the effect nonlinear? Does >> the effect depend on other covariates? >> >> New commands -contrast-, -pwcompare-, and -marginsplot- join >> -margins-. >> >> 1. -contrast- compares effects of factor variables and their >> interactions. It can perform ANOVA-style tests of main >> effects, simple effects, interactions, and nested effects. >> It also decomposes these effects into comparisons against >> reference categories, comparisons of adjacent levels, >> comparisons against the grand mean, orthogonal >> polynomials, and such. >> >> In addition to predefined standard contrasts, user-defined >> contrasts are also supported. Consider >> >> . contrast ar.educ >> >> The -ar.- out front is one of the new, predefined contrast >> operators. -ar.- stands for "adjacent, reversed", and >> -contrast ar.educ- compares adjacent levels of education, >> for instance, high school to some college, some college to >> college graduate, etc. >> >> 2. -pwcompare- performs all (or subsets) of the pairwise >> comparisons. This can be done for all levels of a single >> factor variable or for interactions or interactions with >> continuous variables. >> >> 3. -margins- now allows the new contrast operators and has a >> -pwcompare- option to perform pairwise comparisons. >> >> 4. -marginsplot- graphs results from -margins-. >> >> >> --------------------------- >> ROC adjusted for covariates >> --------------------------- >> >> New command -rocreg- is like regression for ROC. You can model >> how sensitivity and specificity depend on covariates, and you >> can draw graphs. >> >> >> ------------- >> Contour plots >> ------------- >> >> You just have to see one. Visit >> http://www.stata.com/stata12/contour-plots/ >> >> >> ---- >> More >> ---- >> >> There's more. For instance -rename- has a new syntax that allows >> you to rename groups of variables. >> >> . rename (vara varb varc) (varc varb vara) >> >> swaps the names around. >> >> . rename jan* *1 >> >> renames all variables starting with jan to instead end in 1. >> >> . rename v# stat# >> >> renames v1 to be stat1, v2 to be stat2, and so on. >> >> . rename v# v(##) >> >> renames v1 to be v01, v2 to be v02, ... >> >> . rename (a b c) v#, addnumber >> >> rename a to be v1, b to be v2, and c to be v3. >> >> . rename v# (a b c) >> >> does the reverse. >> >> >> >> There really is a lot more. See http://www.stata.com/stata12. >> >> >> -- Bill >> wgould@stata.com >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/statalist/faq >> * http://www.ats.ucla.edu/stat/stata/ > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/