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From |
Roger Newson <r.newson@imperial.ac.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Stata 12 Announcement |

Date |
Mon, 27 Jun 2011 11:09:19 +0100 |

http://www.stata.com/statalist/archive/2009-06/msg00963.html shortly after the announcement of Stata 11.) Best wishes Roger Roger B Newson BSc MSc DPhil Lecturer in Medical Statistics Respiratory Epidemiology and Public Health Group National Heart and Lung Institute Imperial College London Royal Brompton Campus Room 33, Emmanuel Kaye Building 1B Manresa Road London SW3 6LR UNITED KINGDOM Tel: +44 (0)20 7352 8121 ext 3381 Fax: +44 (0)20 7351 8322 Email: r.newson@imperial.ac.uk Web page: http://www.imperial.ac.uk/nhli/r.newson/ Departmental Web page: http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/ Opinions expressed are those of the author, not of the institution. On 27/06/2011 00:07, 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/

**References**:**st: Stata 12 Announcement***From:*"William Gould, StataCorp LP" <wgould@stata.com>

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