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Re: st: Stata 12 Announcement
From
Roger Newson <[email protected]>
To
[email protected]
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
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: [email protected]
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
[email protected]
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* http://www.ats.ucla.edu/stat/stata/