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Re: st: Stata 12 Announcement

From   Roger Newson <>
Subject   Re: st: Stata 12 Announcement
Date   Mon, 27 Jun 2011 11:09:19 +0100

Thanks in advance to all at StataCorp for developing Stata 12. And sorry to be an awkward customer in these exciting times. Is Stata 12 going to have the capability for users to write their own automatically-installed .pdf manuals (accessible by hypertext from their own on-line help files) for their own user-written packages? (As indicated by Alan Riley on Statalist at
shortly after the announcement of Stata 11.)

Best wishes


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
Tel: +44 (0)20 7352 8121 ext 3381
Fax: +44 (0)20 7351 8322
Web page:
Departmental Web page:

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

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

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

     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

     -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

         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


     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

-- Bill
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