Stata 15 help for whatsnew8to9

What's new in release 9.0 (compared with release 8)

This file lists the changes corresponding to the creation of Stata release 9.0:

+---------------------------------------------------------------+ | help file contents years | |---------------------------------------------------------------| | whatsnew Stata 15.0 and 15.1 2017 to present | | whatsnew14to15 Stata 15.0 new release 2017 | | whatsnew14 Stata 14.0, 14.1, and 14.2 2015 to 2017 | | whatsnew13to14 Stata 14.0 new release 2015 | | whatsnew13 Stata 13.0 and 13.1 2013 to 2015 | | whatsnew12to13 Stata 13.0 new release 2013 | | whatsnew12 Stata 12.0 and 12.1 2011 to 2013 | | whatsnew11to12 Stata 12.0 new release 2011 | | whatsnew11 Stata 11.0, 11.1, and 11.2 2009 to 2011 | | whatsnew10to11 Stata 11.0 new release 2009 | | whatsnew10 Stata 10.0 and 10.1 2007 to 2009 | | whatsnew9to10 Stata 10.0 new release 2007 | | whatsnew9 Stata 9.0, 9.1, and 9.2 2005 to 2007 | | this file Stata 9.0 new release 2005 | | whatsnew8 Stata 8.0, 8.1, and 8.2 2003 to 2005 | | whatsnew7to8 Stata 8.0 new release 2003 | | whatsnew7 Stata 7.0 2001 to 2002 | | whatsnew6to7 Stata 7.0 new release 2000 | | whatsnew6 Stata 6.0 1999 to 2000 | +---------------------------------------------------------------+

Most recent changes are listed first.

--- more recent updates -------------------------------------------------------

See whatsnew9.

--- Stata 9.0 release 22apr2005 -----------------------------------------------


Some of the important new additions include

1. New matrix programming language Mata.

2. New survey features, including balanced repeated replications (BRR) and jackknife variance estimates, complete support for multistage designs, and poststratification.

3. Estimation of linear mixed models, including standard errors and confidence intervals for all variance components.

4. Estimation of multinomial probit models, including support for several correlation structures and for user-defined structures.

5. New multivariate analysis, including multidimensional scaling, correspondence analysis, and Procrustean analysis, along with the ability to analyze proximity matrices as well as raw data.

6. Improved GUI, including multiple Do-file Editors, multiple Viewers, and multiple Graph windows; multiple windowing preferences; dockable windows; and much more.

There are other major features, and it will take us another 30 pages to mention everything.

What's new is presented under the headings

New matrix language

Survey statistics

Longitudinal/panel data Time-series statistics

Multivariate statistics

Survival analysis

General-purpose statistics

New ML features

Functions and expressions

Data management


User interface



What's new: New matrix language

Stata has an all-new matrix language called Mata, which is the subject of its own manual, [M] The Mata Reference Manual. Mata can be used by those who want to think in matrix terms and perform matrix calculations interactively, and it can be used by programmers who want to add features to Stata.

Mata has been used to implement many of the new features found in this release. Mata is compiled, optimized, and fast.

Stata's previously existing matrix command continues to be documented. There is an admittedly uneasy relationship between the two, but matrix continues to have its uses. For serious computation, however, you will definitely want to use the new language.

See [M-0] intro -- or help mata -- which provides an introduction and organized reading list. The first thing you will read is [M-1] first.

What's new: Survey statistics

Stata 9 substantially extends Stata's survey-analysis and correlated-data-analysis facilities by adding the remaining two methods of computing standard errors -- Balanced Repeated Replications (BRR) and survey jackknife.

Stata 9 also adds complete support for multistage sampling and poststratification.

A new, unified syntax is used for declaring the design of survey data and for fitting models. For an overview of all survey facilities, see [SVY] survey.

All the old syntax continues to work under version control, although the survey estimation commands do not require that, but if you use old syntax, the new features will not be available.

1. Existing command svyset for declaring the survey design has new syntax that supports a host of new features in Stata's survey-analysis facilities:

a. BRR and jackknife variance estimators have been added to the previously available linearization variance estimator. Moreover, use of BRR or jackknife (or linearization) can now be specified when you svyset or at estimation time.

b. Multistage designs can now be declared, and they may have primary, secondary, and lower-stage sampling units. The linearization variance estimator takes complete advantage of the information in multistage designs.

c. Stratification is now allowed in all stages, making variance estimates more efficient wherever stratification can be exploited.

d. Poststratification is now available and, like stratification, also makes variance estimates more efficient. Poststratification adjusts weights, improves variance estimates, and accounts for biases when demographic or other groupings are known.

e. Finite-population corrections are now allowed in all stages.

f. Sampling weights are handled under all three variance estimators.

For details, see [SVY] svyset. The previous svyset syntax continues to work under version control.

2. New prefix command svy: is how you tell estimators you have survey data. You no longer type svyregress; you type svy: regress. This is not just a matter of style; svy really is a prefix command, and in fact, you can even use it as a prefix on estimation commands you write. In addition, svy: provides a standard, unified syntax for accessing Stata's survey features. svy: is easy to use because it automatically applies everything you have previously svyset, including the design.

The following estimators can be used with svy: prefix:

Descriptive statistics

svy: mean Population and subpopulation means svy: proportion Population and subpopulation proportions svy: ratio Population and subpopulation ratios svy: total Population and subpopulation totals

svy: tabulate oneway One-way tables for survey data svy: tabulate twoway Two-way tables for survey data

Regression models

svy: regress Linear regression svy: ivreg Instrumental variables regression svy: intreg Interval and censored regression

svy: logistic Logistic regression, reporting odds ratios svy: logit Logistic regression, reporting coefficients svy: probit Probit regression

svy: mlogit Multinomial logistic regression svy: ologit Ordered logistic regression svy: oprobit Ordered probit models

svy: poisson Poisson regression svy: nbreg Negative binomial regression svy: gnbreg Generalized negative binomial regression

svy: heckman Heckman selection model svy: heckprob Probit model with selection

Previously existing survey-estimation commands, such as svyregress, svymean, and svypoisson, continue to work as they did before, but only if your survey design is declared using version 8: svyset or if you are working with an old Stata 8 dataset. For a mapping from old estimation commands to the new syntax, see svy8. (The new prefix svy: works with datasets that were svyset under an earlier release of Stata.)

In addition to the three variance estimators and support for multistage sampling, the new svy: prefix provides other enhancements, including

a. Option subpop() allows more flexible selection of subpopulations, meaning that more general if conditions are now allowed.

b. Strata with only one sampling unit (sometimes called singleton PSUs) are now handled better -- the coefficients are now reported, but with missing standard errors. svydes can now be used to find and describe these strata; see [SVY] svydes.

c. With BRR variance estimation, a Hadamard matrix can be used in place of BRR weights, and Fay's adjustment may be specified; see [SVY] brr_options.

3. New command svy: proportion replaces svyprop. (By the way, new command proportion can be used without the svy: prefix; see [R] proportion.) Unlike svyprop, svy: proportion is an estimation command and computes a full covariance matrix for all the estimated proportions, allowing postestimation features, such as tests of linear and nonlinear combinations of proportions (test and testnl) or creation of linear and nonlinear combinations with confidence intervals (lincom and nlcom).

4. New commands ratio, total, and mean, used with the svy: prefix, use casewise deletion and estimate full covariance matrices for the estimates.

5. New command svy: tabulate oneway addresses a missing feature. Previously, anyone wanting a one-way tabulation had to create a constant and perform two-way survey tabulation with that constant.

6. New command estat computes and reports additional statistics and information after estimation with svy: prefix:

a. estat svyset reports complete information on the survey design.

b. estat effects computes and reports the design effects -- DEFF and DEFT -- and the misspecification effects -- MEFF and MEFT -- in any combination for each estimated parameter.

c. estat effects can also compute DEFF and DEFT for subpopulations using simple random-sample estimates from either the overall population or from the subpopulation. estat effects replaces and extends the deff, deft, meff, and meft options previously available on survey estimators.

d. estat lceffects computes and reports the survey design effects and misspecification effects for any linear combination of estimated parameters.

e. estat size reports the sample and population sizes for each subpopulation after svy: mean, svy: proportion, svy: ratio, and svy: total.

For details on estat after survey estimation, see [SVY] estat.

7. Existing command svydes has several new features and options:

a. New option stage() lets you select the sampling stage for which sample statistics are to be reported.

b. New option generate() identifies strata with a single sampling unit.

c. New option finalstage replaces bypsu and reports observation sample statistics by sampling unit in the final stage.

8. New options stdize() and stdweight() on commands svy: mean, svy: ratio, svy: proportion, svy: tabulate oneway, and svy: tabulate twoway allow direct standardization of means, ratios, proportions, and tabulations using any of the three survey variance estimators.

9. Programmers of estimation commands can get full support for estimation with survey and correlated data almost automatically. This support includes correct treatment of multistage designs, weighting, stratification, poststratification, and finite-population corrections, as well as access to all three variance estimators. See [P] program properties.

10. The [SVY] manual now has a glossary that defines commonly used terms in survey analysis and explains how these terms are used in the manual; see [SVY] Glossary.

What's new: Longitudinal/panel data

1. The big news is new command xtmixed -- Stata now fits linear mixed models, also known as hierarchical models or multilevel models.

Mixed models include what social scientists call random-effects models, including one-way, two-way, multi-way, and hierarchical models, and it includes random-coefficient models.

Estimates are obtained using maximum likelihood (ML), restricted maximum likelihood (REML), or expectation maximization (EM). Covariances among random effects are estimated and may be independent (no covariance), exchangeable (common covariance), or unstructured (unique covariance for each pair of effects).

xtmixed estimates standard errors and confidence intervals for the fixed parameters, and it estimates the standard deviations (variances) and correlations (covariances) of the random effects and the full VCE matrix among them.

For details, see [XT] xtmixed.

After estimation with xtmixed,

a. estat recovariance reports the estimated variance-covariance matrix of the random effects for each level.

b. estat group summarizes the composition of the nested groups, providing minimum, average, and maximum group size for each level in the model.

predict after xtmixed can compute best linear unbiased predictions (BLUPs) for each random effect. It can also compute the linear predictor, the standard error of the linear predictor, the fitted values (linear predictor plus contributions of random effects), the residuals, and the standardized residuals.

2. New features have been added to the maximum-likelihood estimators that do not have closed-form solutions and require numeric evaluation of the likelihood. These estimators include xtlogit, xtprobit, xtpoisson, xtcloglog, xtintreg, and xttobit.

a. The likelihood may now be approximated using adaptive Gauss-Hermite quadrature (the new default) or nonadaptive quadrature (the previous default). Adaptive quadrature substantially increases the accuracy of the approximation, particularly on difficult problems such as data with large panel sizes or data with a large variance for the random effects.

b. Linear constraints may now be imposed using the new option constraints(). Constraints are specified the standard way; see [R] constraint.

c. New option intpoints() replaces old option quad(), although quad() continues to work. The new name is more meaningful, especially when used with estimators that integrate likelihoods using methods other than quadrature.

3. Existing command xtreg now allows options robust and cluster() when estimating fixed-effects (FE) and random-effects (RE) models; see [XT] xtreg.

4. Most [XT] commands that previously did not allow time-series operators now support them. These commands include xtgls, xtreg, xtsum, xtcloglog, xtintreg, xtlogit, xtpoisson, xtprobit, xttobit, and xtgee.

5. New command xtrc is old command xtrchh, renamed, and with new features. New option beta reports the best linear predictors (BLUPs) for the group-specific coefficients, along with their standard errors and confidence intervals. For details, see [XT] xtrc.

6. predict after xtrc has the new option group() to compute the BLUPs of the dependent variable using the BLUPs of the coefficients.

7. New command xtline plots panel data and allows either overlaid or separate graphs for each panel; see [XT] xtline

8. New section [XT] Glossary defines commonly used terms and how they are used by us.

What's new: Time-series statistics

1. Existing command arima can now estimate multiplicative seasonal ARIMA (SARIMA) models; see new options sarima(), mar(), and mma() in [TS] arima.

2. New command rolling performs rolling-window or recursive estimations, including regressions, and collects statistics from the estimation on each window; see [TS] rolling.

3. The [TS] manual now has a glossary that defines commonly used terms in time-series analysis and explains how we use them in the manual; see [TS] Glossary.

4. Many existing commands that previously did not allow time-series operators now do. These commands include areg, binreg, biprobit, boxcox, cloglog, cnsreg, glm, heckman, heckprob, hetprob, impute, intreg, logistic, logit, lowess, mvreg, nbreg, orthog, pcorr, poisson, probit, pwcorr, rreg, testparm, treatreg, truncreg, xtcloglog, xtgls, xtintreg, xtlogit, xtpoisson, xtprobit, xtgee, xtreg, xtsum, and xttobit.

5. Many commands requiring time-series data will now work on a single panel from a panel dataset when that panel is selected using an if expression or an in qualifier. Those commands include ac, corrgram, cumsp, dfgls, dfuller, pac, pergram, pperron, wntestb, wntestq, and xcorr. New commands estat archlm, estat bgodfrey, estat dwatson, and estat durbinalt, which replace commands archlm, bgodfrey, dwstat, and durbina, also work on a single panel from a panel dataset.

6. The dialogs for analyzing IRF results are much improved. The dialogs now populate lists of models and variables from the current IRF results that may be chosen for producing tables and graphs. The improved dialogs include db irf cgraph, db irf ctable, db irf graph, db irf ograph, and db irf table.

7. Existing command dfuller has new option drift for testing the null hypothesis of a random walk with drift. The algorithm for calculating MacKinnon's approximate p-values is also now more accurate in cases where the p-value is relatively large; see [TS] dfuller.

8. Existing commands corrgram and pac have new option yw that computes partial autocorrelations using the Yule-Walker equations instead of the default regression-based method; see [TS] corrgram.

9. Time-series operators are now better displayed in estimation and other result tables.

10. New command estat -- used after regress -- brings together what was previously done by commands dwstat, durbina, bgodfrey, and archlm. The new commands are estat dwatson, estat durbina, estat bgodfrey, and estat archlm. See [R] regress postestimation time series.

11. The ability of arima and arch to estimate standard errors using either the observed information matrix (OIM) or the outer product of gradients (OPG) has been consolidated under the new vce() option.

(What follows was first released in Stata 8.2.)

12. New command vec fits cointegrated vector error-correction models (VECMs) using Johansen's method; see [TS] vec.

13. New command vecrank produces statistics used to determine the number of cointegrating vectors in a VECM, including Johansen's trace and maximum-eigenvalue tests for cointegration; see [TS] vecrank.

14. New command fcast -- which replaces old command varfcast -- produces and graphs dynamic forecasts of the dependent variables after fitting a VAR, SVAR, or VECM; see [TS] fcast.

15. New command irf -- which replaces the old command varirf -- does everything the old command did and more. irf estimates the impulse-response functions, cumulative impulse-response functions, orthogonalized impulse-response functions, structural impulse-response functions, and forecast error-variance decompositions after fitting a VAR, SVAR, or VECM. irf can also make graphs and tables of the results. See [TS] irf.

varirf continues to work but is no longer documented. irf accepts .vrf result files created by varirf.

16. Existing command varsoc can now be used to obtain lag-order selection statistics for VECMs, as well as VARs; see [TS] varsoc.

17. New command veclmar computes Lagrange-multiplier statistics for autocorrelation after fitting a VECM; see [TS] veclmar.

18. New command vecnorm tests whether the disturbances in a VECM are normally distributed. For each equation and for all equations jointly, three statistics are computed: a skewness statistic, a kurtosis statistic, and the Jarque-Bera statistic. See [TS] vecnorm.

19. New command vecstable checks the eigenvalue stability condition after fitting a VECM; see [TS] vecstable.

20. New command vecstable and the existing command varstable have a new graph option for presenting the stability results. See [TS] vecstable and [TS] varstable.

21. The output of the following commands has been standardized to improve formatting: var, svar, vargranger, varlmar, varnorm, varsoc, varstable, and varwle.

22. New command haver makes it easy to load and analyze economic and financial databases available from Haver Analytics; see [TS] haver.

What's new: Multivariate statistics

Stata has four all-new methods for analyzing multivariate data and many more extensions to existing methods. In addition, most methods now support direct analysis of matrices as well as raw data.

Be sure you check the postestimation documentation for the multivariate estimators you use; many important new features are documented there. In particular, all the multivariate commands make extensive use of new command estat for providing additional statistics and results after estimation.

1. New commands mds, mdslong, and mdsmat perform classic metric multidimensional scaling: mds performs the scaling with respect to the distances (dissimilarities) between observations, mdslong performs the scaling on a long dataset where each observation represents the distance between two points or objects, and mdsmat performs the scaling on a matrix of distances. See [MV] mds, [MV] mdslong, and [MV] mdsmat.

mds supports all 33 similarity/dissimilarity measures available in Stata; see [MV] measure_option.

The following new estat commands work after mds, mdslong, or mdsmat and provide additional statistics and results:

a. estat config reports the coordinates of the approximating configuration.

b. estat correlations reports the Pearson and Spearman correlations between the dissimilarities and the approximating distances for each object.

c. estat pairwise reports a set of statistics for each pairwise comparison; it reports the dissimilarities, the approximating distances, and the raw residuals.

d. estat quantiles reports the quantiles of the residuals for each observation (after mds) or object (after mdslong or mdsmat).

e. estat stress reports the Kruskal stress (loss) measure between the transformed dissimilarities and fitted distances per object.

See [MV] mds postestimation for more information.

In addition, there are two new commands for graphing results from a multidimensional scaling:

a. mdsconfig plots the approximating Euclidean configuration of the first two dimensions; see [MV] mds postestimation plots.

b. mdsshepard produces a Shepard diagram of the dissimilarities against the approximating Euclidean distances; see [MV] mds postestimation plots.

predict after any multidimensional-scaling command will produce

a. variables containing the approximating configuration (predict newvarlist, config);

b. variables containing the dissimilarity, distance, and raw residuals (predict newvarlist, pairwise)

See [MV] mds postestimation for more information.

2. New commands ca and camat perform two-way correspondence analysis using any of several available forms of normalization. ca performs the analysis on the cross-tabulation of two categorical variables; camat performs the analysis on a matrix of counts; see [MV] ca for more information on both.

The following new estat commands work after ca and camat and provide additional statistics and results

a. estat coordinates reports the coordinates in both the row space and the column space.

b. estat distances reports the chi-squared distances between the row profiles and between the column profiles, including the distances to the marginal distributions (commonly called centers). Both observed or fitted profiles are available.

c. estat inertia reports the inertia contributions of the individual cells.

d. estat profiles reports the row profiles and column profiles -- the conditional distributions, given the other dimension.

e. estat summarize reports summary information of the row and column variables over the estimation sample.

f. estat table reports the fitted correspondence table, the observed "correspondence" table, or the expected table under the assumption of independence.

See [MV] ca postestimation for more information.

In addition, there are two new commands for graphing results from a correspondence analysis:

a. cabiplot produces a biplot of each row category and each column category; see [MV] ca postestimation plots.

b. caprojection produces a graph that shows the ordering of row categories and column categories on each principal dimension of the analysis. Each principal dimension is represented by a vertical line; markers are plotted on the lines where the row categories and column categories project onto the dimensions; see [MV] ca postestimation plots.

predict after ca and camat computes fitted values and row or column scores for any dimension; see [MV] ca postestimation.

3. The new command procrustes performs Procrustean analysis for comparing and measuring the similarity between two sets of variables: source and target. Two datasets can also be compared if the datasets are first merged by record.

The following new estat commands work after procrustes and provide additional statistics and results:

a. estat compare reports fit statistics of the three transformations available in Procrustean analysis: orthogonal, oblique, and unrestricted.

b. estat mvreg reports the multivariate regression that is related to the current Procrustean analysis.

c. estat summarize reports summary information of the two sets of variables over the estimation sample.

See [MV] procrustes postestimation for more information.

New command procoverlay after procrustes creates an overlay graph comparing the target variables to the fitted values derived from the source variables; see [MV] procrustes postestimation.

predict after procrustes produces fitted values for all variables, residuals for all variables, or residual sums of squares for a specified target variable; see [MV] procrustes postestimation.

4. New command biplot performs a biplot analysis of a dataset and produces a two-dimensional biplot of the results. A biplot simultaneously displays the observations (rows) and the relative positions of the variables (columns). Observations are projected to two dimensions such that the distance between the observations is approximately preserved. The variables are plotted as arrows, with the cosine of the angle between arrows approximating the correlation between the variables. See [MV] biplot.

5. New command tetrachoric computes a tetrachoric correlation matrix for a set of binary variables. tetrachoric is documented in [R] but will often be used in multivariate analyses; see [R] tetrachoric.

tetrachoric results can be used in subsequent factor analyses or principal component analyses using the new factormat and pcamat commands. See [MV] factor and [MV] pca.

6. Existing command canon now allows analysis and presentation of more than one linear combination and has new options for reporting the raw or standardized coefficients and for reporting significance tests of the canonical correlations; see [MV] canon.

The following new estat commands work after canon and provide additional statistics and results:

a. estat correlations reports the correlations among all variables.

b. estat loadings reports the matrices of canonical loadings.

See [MV] canon postestimation for more information.

7. Existing command cluster dendrogram has many new features, including horizontal dendrograms and the ability to label branch counts. The look of the graph can now be changed (titles, axes, colors, etc.); see [MV] cluster dendrogram.

8. The existing hierarchical cluster commands have new option measure() that specifies the proximity measure to use in computing dissimilarities between observations. Any of 33 measures may be specified; see [MV] measure_option. Previously most of the measures were available under other option names; those options continue to work but are undocumented. See [MV] cluster.

9. Existing command cluster stop has new option varlist() that specifies alternative variables to use when computing the stopping rules; see [MV] cluster stop.

What's new: Analysis of proximity matrices

All of Stata's multivariate analysis facilities that rely on pairwise comparisons of distance, similarity, dissimilarity, covariance, correlation, or other proximity measures can now work directly with proximity matrices that you compute or obtain from other sources.

Previously, all of these facilities worked only with raw datasets. The new commands implement analyses on matrices. They share the common ability to accept either full matrices or vectors representing the lower or upper triangle of a symmetric proximity matrix.

10. New command clustermat extends all of Stata's hierarchical clustering facilities to the analysis of matrices of a dissimilarity measure (sometimes called a distance or proximity measure). This includes all seven linkage methods and the ability to create dendrograms of the results; see [MV] clustermat.

11. New command factormat performs factor analysis on a matrix of correlations, extending all the new and previously available capabilities of the existing command [MV] factor to precomputed matrices of correlations; see [MV] factor.

12. New command pcamat performs principal component analysis on an existing correlation or covariance matrix; see [MV] pca.

13. New matrix subcommand dissimilarity computes similarity, dissimilarity, or distance matrices using any of 19 proximity measures for continuous data and 14 measures for binary data; see [MV] measure_option and see [MV] matrix dissimilarity.

What's new: Factor and principal component analysis additions

In addition to allowing direct analysis of correlation and covariance matrices using factormat and pcamat, Stata's factor analysis and principal components analysis (PCA) methods have been expanded, particularly through the addition of postestimation commands for reporting and graphing results.

14. Command factor has new reporting option altdivisor, that specifies the trace of the correlation matrix be used as the divisor for proportions, rather than the default (the sum of all eigenvalues).

15. New estat commands for use after factor and factormat provide additional statistics and results:

a. estat common reports the correlation matrix of the common factors and is more of interest after oblique rotations.

b. estat factors reports model-selection criteria (AIC and BIC) over all the factors retained in an analysis.

c. estat rotatecompare reports the unrotated factor loadings next to the most-recent rotated loadings.

d. estat structure reports the factor structure -- the correlations between the variables and the common factors.

See [MV] factor postestimation for more information.

16. Existing command pca allows several new options:

a. Option vce(normal) computes the VCE of the eigenvalues and eigenvectors, assuming multivariate normality.

This gives you access to many of Stata's postestimation facilities for analyzing estimation results, including tests of eigenvalue and eigenvector significance, tests of linear and nonlinear combinations ([R] test and [R] testnl), linear and nonlinear combinations with confidence intervals ([R] lincom and [R] nlcom), and nonlinear predictions with confidence intervals ([R] predictnl).

vce(normal) also produces the ingredients for adding confidence intervals to screeplots; see [MV] screeplot.

b. Options level(), blanks(), novce, and norotated allow more flexible control of the displayed results.

c. Option components(#) specifies the number of components to retain and is a synonym for old option factor().

d. Options tol() and ignore provide advanced control for computationally difficult problems.

See [MV] pca for more information.

17. New estat commands for use after pca and pcamat provide additional statistics and results:

a. estat loadings reports the component loading matrix in any of several available normalizations of the columns (eigenvectors).

b. estat rotatecompare reports the unrotated (principal) components next to the most recent rotated components.

See [MV] pca postestimation for more information.

18. New estat commands for use after any factor analysis or any principal components analysis (that is, after factor or factormat or after pca or pcamat) provide additional statistics and results:

a. estat anti reports the anti-image correlation and anti-image covariance matrices.

b. estat kmo reports the Kaiser-Meyer-Olkin measure of sampling adequacy.

c. estat residuals reports the difference between the observed correlation or covariance matrix and the fitted (reproduced) matrix using the retained factors.

d. estat smc reports the squared multiple correlations (SMC) between each variable and all other variables. SMC is a theoretical lower bound for communality, so it is an upper bound for the unexplained variance.

See [MV] factor postestimation and [MV] pca postestimation for more information.

19. Three new graphs are available after any factor analysis (factor and factormat) or after any principal components analysis (pca and pcamat):

a. scoreplot graphs scatterplots comparing each pair of factors or components; see [MV] scoreplot.

b. loadingplot graphs scatterplots comparing loadings for each pair of factors or components; see [MV] scoreplot.

c. screeplot plots the eigenvalues of a covariance or correlation matrix; see [MV] screeplot. (screeplot replaces greigen and has more features; greigen continues to work but is undocumented.)

20. New command rotate performs orthogonal and oblique rotations after factor, factormat, pca, and pcamat. Available rotations include varimax, quartimax, equamax, parsimax, minimum entropy, Comrey's tandem 1 and 2, promax power, biquartimax, biquartimin, covarimin, oblimin, factor parsimony, Crawford-Ferguson family, Bentler's invariant pattern, oblimax, quartimin, and target and partial-target matrices; see [MV] rotate.

New command rotatemat performs these same linear transformations (rotations) on any Stata matrix.

What's new: Survival analysis

1. The [ST] manual now has a glossary that defines commonly used terms in survival (or duration) analysis and often explains how these terms are used in the manual; see [ST] Glossary.

2. New command estat can be used after stcox and streg. In addition to the standard estat statistics -- information criteria, estimation sample summary, and formatted variance-covariance matrix (VCE) -- statistics specific to the proportional hazards estimator are available after stcox. These include

a. estat concordance computes Harrell's C and Somer's D statistics measuring concordance -- agreement of predictions with observed failure order.

b. estat phtest replaces the existing stphtest for computing tests and graphs of the proportional hazards assumption. stphtest continues to work.

See [ST] stcox postestimation and [ST] streg postestimation.

3. Existing command sts graph has new options cihazard and per(#). cihazard draws pointwise confidence bands around the smoothed hazard function, and per() specifies the units used to report the survival or failure rate. See [ST] sts.

4. Existing command stcurve now plots over an evenly spaced grid, producing smooth curves, even in small samples; see [ST] stcurve.

5. Existing command sts graph has new options atriskopts() and lostopts() that let you control how the labels for at-risk and lost observations look (their color, font size, etc.); see [ST] sts.

6. Existing command stci has new options for controlling how the plotted survival line looks (color, thickness, etc.) and for adding titles, controlling legends, and all other characteristics of the graph; see [ST] stci.

What's new: General-purpose statistics

1. New estimation command asmprobit fits multinomial probit (MNP) models to categorical data and is frequently used in choice-based modeling. asmprobit allows several correlation structures for the alternatives, including completely unstructured, where all possible correlations are estimated. It also allows for either heteroskedastic or homoskedastic variances among the alternatives and allows arbitrary patterns within the alternative variances or correlations. asmprobit's syntax makes specifying both case-specific and alternative-in-case-specific regressors easy.

In addition to common postestimation commands, such as mfx for computing marginal effects, new command estat provides additional statistics and results:

a. estat alternatives reports summary statistics about each of the alternatives and provides a mapping between the index numbers labeling the alternatives and their associated values and labels in the dataset.

b. estat covariance computes and reports the estimated covariance matrix for the alternatives.

c. estat correlation reports the correlations among the alternatives in matrix form.

Predicted statistics after asmprobit include the linear predictor, the probability an alternative is selected, and the standard error of the linear predictor.

See [R] asmprobit, and [R] asmprobit postestimation.

2. New estimation command mprobit also fits multinomial probit models to categorical data but in the simplified situation of having only case-specific covariates (as with the multinomial logistic regression, mlogit). Maximizing the likelihood is much faster in such cases because the numeric approximation to the likelihood is simpler. See [R] mprobit.

3. New estimation command slogit fits the stereotype logistic regression model for categorical dependent variables. This model can be viewed as either a generalization of the multinomial logistic regression model (mlogit) or a generalization of the ordered logistic regression model (ologit) that relaxes the proportional-odds assumption. See [R] slogit.

Predicted statistics after slogit include the linear predictor, the probability of any or all outcomes, and the standard error of the linear predictor. See [R] slogit postestimation.

4. New estimation command ivprobit fits probit regression models of binary outcomes with endogenous regressors. Estimation can be performed by maximum likelihood estimation (MLE) or by Newey's minimum chi-squared two-step estimation, but some postestimation facilities, such as computing marginal effects with mfx, are available only after ML estimation -- the two-step estimator imposes a transformation that invalidates many postestimation results. See [R] ivprobit.

5. New estimation command ivtobit fits linear regression models with censored dependent variables by maximum likelihood estimation or by Newey's minimum chi-squared two-step estimation (but see the note about the two-step estimator in 4 above). See [R] ivtobit.

6. New estimation command ztp fits a zero-truncated Poisson model of event counts with truncation at zero.

Predicted statistics after ztp include the linear predictor and its standard error, the predicted number of events, the incidence rate, the conditional mean, and the likelihood score See [R] ztp and [R] ztp postestimation.

7. New estimation command ztnb fits a zero-truncated negative binomial model of event counts with truncation at zero and over or under dispersion.

Predicted statistics after ztnb include the linear predictor and its standard error, the predicted number of events, the incidence rate, the conditional mean, and the likelihood scores See [R] ztnb and [R] ztnb postestimation.

8. New estimation commands mean, ratio, proportion, and total estimate means, ratios, proportions, and totals over the entire sample or over groups within the sample. When estimating over groups, the entire covariance matrix (VCE) is estimated. These are full estimation commands that support a range of postestimation facilities, such as linear and nonlinear tests among the groups ( test and testnl) and linear and nonlinear combinations of group-level statistics (lincom and nlcom). All four commands support several SE and VCE estimates: robust, cluster-robust, bootstrap, jackknife, and observed information matrix (the default).

mean, ratio, and proportion also support direct standardization across strata (groups) using the stdize() and stdweight() options.

See [R] mean, [R] ratio, [R] proportion, and [R] total.

9. To avoid conflict with the new mean command, existing command means has been renamed ameans, with synonyms gmeans and hmeans.

10. Existing command nl has a new syntax that makes estimating nonlinear least-squares regressions easier. For most models, estimation is now as easy as typing the nonlinear expression. Full programmability has been retained for complex models, and the old syntax continues to work.

nl also now supports robust (Huber/white/sandwich) and cluster-robust SE and VCE estimates, including two popular adjustments that can dramatically improve the small-sample performance of robust SE and VCE estimates.

A number of new reporting and estimation options have also been added. See [R] nl.

11. New option vce() selects how standard errors (SEs) and covariance matrix of the estimated parameters are estimated by most estimation commands. Choices are vce(oim), vce(opg), vce(robust), vce(jackknife), and vce(bootstrap), although the choices can vary estimator by estimator. vce(robust) is a synonym for robust, and you can use either. What is new are vce(jackknife) and vce(bootstrap).

vce(bootstrap) specifies that the standard errors, significance tests, and confidence intervals be normal-based bootstrap estimates, rather than the default analytic estimates based on the observed information matrix. You can also produce percentile-based or bias-corrected confidence intervals after estimation using estat bootstrap; see [R] bootstrap postestimation.

vce(jackknife) specifies that the standard errors, significance tests, and confidence intervals be jackknife estimates.

Both vce(bootstrap) and vce(jackknife) will automatically perform either observation or cluster sampling, whichever is appropriate for the estimator.

Notably, both vce(bootstrap) and vce(jackknife) compute bootstrapped or jackknifed estimates of the complete VCE matrix. This means that many of Stata's postestimation commands are available. You can form linear and nonlinear combinations or functions of the parameters and obtain jackknife or normal-based bootstrap standard errors and confidence intervals for the combinations using [R] lincom and [R] nlcom. Similarly, you can perform linear and nonlinear tests using [R] test and [R] testnl.

12. New command estat centralizes the computing and reporting of additional statistics after estimation, just as predict does with predictions. estat allows subcommands. estat summarize, for instance, reports summary statistics for the estimation sample and can be used after any estimator. estat also allows subcommands that are specific to the estimation command. To find out what is available after a command, see the corresponding postestimation entry. For example, after [R] regress, see [R] regress postestimation; or after [XT] xtmixed, see [XT] xtmixed postestimation.

Existing postestimation commands have been brought into the estat framework:

Estimation Old New estat command command command -------------------------------------------------- regress ovtest estat ovtest hettest estat hettest szroeter estat szroeter vif estat vif imtest estat imtest regress dwstat estat dwatson (time series) durbina estat durbinalt bgodfrey estat bgodfrey archlm estat archlm anova ovtest estat ovtest hettest estat hettest logit and lstat estat classification(*) logistic lfit estat gof(*) poisson poisgof estat gof stcox stphtest estat phtest

xtgee xtcorr estat wcorrelation -------------------------------------------------- (*) The new command works after probit, as well as logit and logistic; the old command worked after logit and logistic only.

The original commands continue to work but are undocumented.

Three estat subcommands are available after almost all estimators:

a. estat ic reports Akaike's and Schwarz's Bayesian information criteria (AIC and BIC).

b. estat summarize reports summary statistics on the variables in the estimation model for the estimation sample.

c. estat vce reports the covariance (VCE) or correlation matrix estimates. (estat vce replaces the old vce command and has more features.)

13. Stata has many new prefix commands (commands that behave like by: and xi:). New prefix commands include statsby:, bootstrap:, jackknife:, permute:, simulate:, stepwise:, svy:, and rolling:. For instance, to obtain the standard error and confidence interval of the mean, you might type

. jackknife: mean earnings

or to obtain survey-adjusted estimates, you might type

. svy: mean earnings

after svysetting your data.

See [R] bootstrap, [R] jackknife, [R] permute, [TS] rolling, [R] simulate, [R] stepwise, [D] statsby, and [SVY] svy.

14. New prefix commands bootstrap: and jackknife: replace old commands bs and jknife, and in addition to having better syntax, they also provide new features:

a. They handle and report of expressions better.

b. They post their results as estimation results with a complete VCE. Most postestimation facilities may now be used after them and will be based on the bootstrap or jackknife VCE. These include

adjust adjusted predictions estimates cataloging estimation results lincom linear combinations with SEs, tests, and CIs nlcom nonlinear combinations with SEs, tests, and CIs mfx computing marginal effects and elasticities predict predictions, residuals, probabilities, etc. predictnl generalized nonlinear predictions with SEs and CIs test Wald tests of simple and composite linear hypotheses testnl Wald tests of nonlinear hypotheses

c. They produce a model test when applied to the coefficients of estimation commands.

d. They allow option seed(#) to set the random-number seed.

e. They allow option reject(exp) to reject replicates that explicitly match exp.

f. bootstrap: uses the normal distribution instead of the Student's t distribution to compute the normal-approximation confidence intervals.

g. jackknife: now allows fweights to be specified.

See [R] bootstrap and [R] jackknife.

15. New prefix command statsby: replaces old command statsby (not a prefix) and provides enhanced handling and reporting of expressions, allows weights, and allows string variables in the option by(). See [D] statsby.

16. New prefix command stepwise: replaces old command sw and, in addition to working with all the previous estimators, also works with [R] intreg and [R] scobit.

17. Existing prefix command xi: has new option noomit that prevents it from omitting a category when generating category indicators for group variables. See [R] xi.

18. New command tetrachoric computes a tetrachoric correlation matrix for a set of binary variables. See [R] tetrachoric.

19. Existing command suest, which combines estimation results for subsequent testing, is easier to use and has new features:

a. Scores are now computed for the models you combine; you no longer need to save scores when estimating.

b. suest, used after svy: estimation, now accounts for your survey design.

c. suest now works more smoothly with certain estimation commands that previously required special treatment, including regress, ologit, and oprobit.

d. suest now works with all models estimated by clogit, rather than only those with a single positive outcome per group.

See [R] suest.

20. Existing command clogit has new features:

a. Robust and cluster-robust SE and VCE estimates are now supported through options robust and cluster().

b. Linear constraints on the parameters are now implemented via option constraints().

c. New option vce() allows SE and VCE estimates to be computed using OIM (the default), OPG, bootstrap, and jackknife.

See [R] clogit.

21. Option level() now allows noninteger confidence levels to be specified. See [R] estimation options.

22. Existing command predict now generates equation-level scores after most maximum-likelihood estimation commands; see the documentation of predict in the postestimation entry for each estimation command.

23. Existing command cumul has a new option equal to create equal cumulative values for ties. See [R] cumul.

24. Existing command estimates table now allows you to specify more models, and the command wraps the table if necessary. Also allowed are new options

a. equations(), which matches equations by number rather than by name.

b. coded, which displays the table in a compact, symbolic format.

c. modelwidth(), which sets the number of characters for displaying model names.

See [R] estimates.

25. test after anova and manova has two new options for performing Wald tests:

a. mtest(), which implements three methods to adjust for multiple tests: Bonferroni, Holm, and Sidak.

b. test(), which makes specifying contrasts easier by accepting a matrix containing the contrast.

See [R] anova postestimation.

26. Commands ci and cii have new options exact, wilson, agresti, jeffreys, and wald for computing different types of binomial confidence intervals. See [R] ci.

27. Command hausman has new option df() for controlling the degrees of freedom. See [R] hausman.

28. predict after ivreg has the new score option for returning equation-level scores. See [R] ivreg postestimation.

29. Command mfx is now faster and has new option varlist() for computing effects of specific variables. See [R] mfx.

30. Commands tabulate and tabi with the exact option are now significantly faster.

31. In existing command mlogit, option basecat has been renamed baseoutcome() for better consistency with the terminology of choice models. See [R] mlogit.

32. Existing commands spearman and ktau now allow more than two variables to be specified and have more flexible output. See [R] spearman.

33. Existing command bsample for sampling with replacement (bootstrap sampling) now supports weighted bootstrap resampling using the new weight() option. See [R] bsample.

34. Existing command bstat for reporting bootstrap results has a number of new reporting options. In addition, bstat previously computed percentile and other confidence intervals. This is now handled by estat bootstrap, which can be used after any bootstrap estimation, including bstat. See [R] bstat and [R] bootstrap postestimation.

35. Most maximum likelihood estimators now test for convergence using the Hessian-scaled gradient, g*inv(H)*g'. This criterion ensures that the gradient is close to zero when scaled by the Hessian (the curvature of the likelihood or pseudolikelihood surface at the optimum) and provides greater assurance of convergence for models whose likelihoods tend to be difficult to optimize, such as those for arch, asmprobit, and scobit. You can set the tolerance level for this test with new option nrtolerance(), show the Hessian-scaled gradient in the iteration log with option shownrtol, and turn the test off with option nonrtolerance. See [R] maximize.

36. Existing command set has new setting maxiter -- default value 16000 -- that specifies the maximum number of iterations to be performed by all estimation commands. You change this setting by typing set maxiter #, and you may add option permanently to retain the setting in future Stata sessions.

What's new: New ML features

Command ml, for implementing user-written maximum-likelihood estimators, has many new features:

1. New option technique() sets the optimization technique. BHHH, DFP, and BFGS optimization techniques are now available; the default technique remains modified Newton-Raphson.

2. New option vce() sets the type of covariance-matrix calculations that will be made.

vce(oim) specifies the observed information matrix (OIM), also called the Hessian-based estimator; this is (and always has been) the default.

vce(opg) specifies the outer product of the gradients (OPG). This is new.

vce(robust) specifies Taylor-series linearization, also known as the Huber or White estimator and, in Stata, as simply robust.

3. Most estimators written with ml now support estimation with survey data and correlated data with no additional programming. This support includes correct treatment of multistage designs, weighting, stratification, poststratification, and finite-population corrections, as well as access to linearization, jackknife, and bootstrap variance estimators. For a discussion, see [P] program properties.

4. ml has always allowed linear constraints to be applied using the option constraints() with no additional programming. It now handles irrelevant constraints more elegantly. Irrelevant constraints are those that have no impact on the model. Previously, irrelevant constraints caused an error message. Now they are flagged and ignored.

5. When linear constraints are imposed, ml now applies a Wald test for the overall fit of the model, rather than attempting a likelihood-ratio (LR) test, which is often inappropriate.

6. ml has new subcommand score for generating scores after fitting a model.

7. ml has new option diparm_options() that automatically performs transformations of ancillary parameters.

8. ml now saves the gradient vector in e(gradient).

9. ml has new option search(norescale) that prevents rescaling when searching for starting values.

10. ml honors the new setting for maximum iterations, set maxiter #, and will iterate a maximum of # iterations, even if convergence has not been achieved.

11. ml now displays a prominent message in the footer of the estimation results when convergence is not achieved. This message continues to be shown on redisplay of estimation results.

12. ml has new option nofootnote to suppress printing the new message warning if convergence is not achieved.

13. ml tests for convergence using the Hessian-scaled gradient -- g*inv(H)*g'. This is a true convergence criterion that ensures that the gradient is close to zero when scaled by the Hessian (the curvature of the likelihood or pseudolikelihood surface at the optimum). This new criterion is particularly important when maximizing difficult likelihoods to prevent stopping the maximization too soon.

14. New option nrtolerance() lets you change the tolerance for the Hessian-scaled gradient convergence criterion; the default is nrtolerance(1e-5).

15. New option shownrtolerance displays the criterion value of the Hessian-scaled gradient at each iteration.

16. New undocumented command mlmatbysum helps you compute the Hessian of panel-data likelihoods and is of interest to those seeking the speed that comes with programming your own second-derivative calculations; see mlmatbysum.

17. ml has two new undocumented subcommands -- ml hold and ml unhold -- to assist in solving nested optimization problems, see ml_hold.

See [R] ml for more information on these features. Anyone programming estimators using ml should read the book Maximum Likelihood Estimation with Stata, 2nd Edition (Gould, Pitblado, and Sribney 2003). Many of the features mentioned above are discussed and applied to real problems in the book.

What's new: Functions and expressions

1. The limit for the number of dyadic operators has been increased from 200 to 500; see limits.

2. The default matrix size (matsize) for Intercooled Stata is now 200, rather than 40. The default for Stata/SE remains 400, and for Small Stata it is 40.

3. The following new functions have been added in the context of expressions, such as generate newvar = exp or if exp:

name purpose ---------------------------------------------- binormal() bivariate normal cumulative atan2() two-argument arc tangent

regexm() regular expression matching regexr() regular expression replacement regexs() regular subexpressions

indexnot() first string s1 not in s2 ----------------------------------------------

See [FN] Functions by category or type help followed by the function name, such as help binormal().

In addition, a host of new functions are available through Mata; see [M-4] intro.

4. The following existing functions have been renamed:

old name new name -------------------------------------- index() strpos() binorm() binormal() match() strmatch() norm() normal() invnorm() invnormal() normd() normalden() lnfact() lnfactorial() issym() issymmetric() syminv() invsym() --------------------------------------

Old names continue to work. Functions were renamed because the new name is better and because Mata uses the new name, and you want to be able to use the same names in both environments.

5. The following existing functions now have two names, and you can use either:

Name 1 Name 2 -------------------------------------- lower() strlower() upper() strupper() proper() strproper() ltrim() strltrim() rtrim() strrtrim() trim() strtrim() reverse() strreverse() string() strofreal() int() trunc() length() strlen() --------------------------------------

In this case, throughout the Stata documentation, we use name 1, but you can use name 1 or name 2 in your Stata expressions. Name 2 matches the name of the Mata function that does the same thing, so you may want to standardize on name 2.

6. The following egen functions have been renamed:

old name new name ------------------------ any() anyvalue() eqany() anymatch() neqany() anycount() rfirst() rowfirst() rlast() rowlast() rmean() rowmean() rmin() rowmin() rmiss() rowmiss() robs() rownonmiss() rsd() rowsd() rsum() rowtotal() sum() total() ------------------------

The new names are more consistent. Old names continue to work but are not documented.

What new: Data management

1. There is a new manual [D] Data management, and the data-management commands have been moved from [R] to [D]. See [D] intro for an expanded what's new for data-management capabilities.

2. Existing command set type now has a permanently option. You can now permanently set the default datatype to either float (the factory default) or double.

3. New commands xmlsave and xmluse save and restore datasets in Extended Markup Language (XML) format. Data may be saved or used in either Stata dta XML format or Microsoft Excel's SpreadsheetML format. See [D] xmlsave.

4. New commands fdasave, fdause, and fdadescribe save, use, and describe files in the format required by the U.S. Food and Drug Administration (FDA) for new drug and device applications (NDAs). These commands are designed to assist people making submissions to the FDA, but the commands are general enough for use in transferring data between SAS and Stata. The FDA format is identical to the SAS XPORT Transport format. See [D] fdasave.

5. Value labels may now be up to 32,000 characters long.

6. Existing command label has a new subcommand language that lets you create and use datasets containing different variable, value, and data labels, which might be in different languages. See [D] label language.

7. Datasets from the examples in the Stata manuals can now be browsed, described, and used. Type help dta contents, or select File > Example datasets... from the Stata menu.

8. statsby is now a prefix command; see [U] 11.1.10 Prefix commands. For information on its new syntax, see [D] statsby. Enhancements to statsby include

a. Rather than requiring a list of expressions for the statistics to collect, statsby now collects a default set.

b. Expressions to be computed and saved can now be grouped together as equations; see exp_list.

c. String variables are now allowed.

d. Weights are now allowed.

e. New option force forces statsby to work with survey estimators. By default, this is prevented because the method statsby uses to select subsamples will generally not produce appropriate standard-error estimates with survey data (the subpop option must be used with survey data).

f. Dots showing the progress of computations are now shown by default.

g. New option nolegend suppresses the table reporting on what statsby is running.

9. New command filefilter copies an input file to an output file while converting a specified ASCII or binary pattern to another pattern; see [D] filefilter.

10. New command expandcl replicates clusters of unique observations, much like an expand, but for clustered data; see [D] expandcl.

11. New command tostring converts numeric variables to string; see [D] tostring.

12. Existing command codebook now allows if and in qualifiers; see [D] codebook.

13. New command rmdir removes an existing directory (folder); see [D] rmdir.

14. New command clonevar makes an identical copy of an existing variable; see [D] clonevar.

15. Existing commands icd9 and icd9p have been updated to use the V21 codes; see [D] icd9 and [D] icd9p.

16. Existing command encode has new option noextend that prevents adding new value label mappings; see [D] encode.

17. Existing command odbc for accessing Open DataBase Connectivity (ODBC) data sources has the following enhancements:

a. ODBC is now supported under Mac OS X and Linux systems that use the iODBC Driver Manager. For more information on configuring ODBC for Mac and Linux, see the FAQ at

b. odbc has new subcommands odbc insert and odbc exec for writing data to an ODBC data source. Positioned updates can be performed using the odbc exec command.

c. odbc has a new subcommand sqlfile for batch processing SQL instructions.

d. odbc load has a new option sqlshow for debugging SQL communication with ODBC drivers.

e. odbc load has new options allstring and datestring, which import either all data or just dates as strings.

See [D] odbc.

18. Existing command merge has the following new features:

a. It now accepts multiple using files.

b. New option nosummary suppresses creating variables that summarize how the records were merged.

c. New option sort option sorts the master and using datasets if they are not already sorted.

d. Existing options unique, uniqmaster, and uniqusing now require you to specify matching variables.

e. Warning messages are now given when matching variables do not uniquely identify observations.

See [D] merge.

19. Existing commands merge and append now incorporate all notes from the using dataset that do not already appear in the master dataset, unless new option nonotes is specified; see [D] merge and [D] append.

20. Existing command contract has new options cfreq(), percent(), cpercent(), float, and format() to create frequency and percentage variables; see [D] contact.

21. Existing commands corr2data and drawnorm now support triangular specification of the correlation or covariance matrix; see [D] corr2data and [D] drawnorm.

22. Existing command separate has new option shortlabel to specify that shorter variable labels be created; see [D] separate.

23. Existing command outfile has new option missing that preserves both standard and extended missing values when the comma option is also specified; see [D] outfile.

24. Existing command clear now performs mata: mata clear in addition to everything else; see [D] clear.

What's new: Graphics

1. Stata now allows multiple Graph windows. The existing name() option now creates a named graph and displays it in its own window. See What's new: User interface below.

2. New command sunflower draws sunflower density-distribution plots; see [R] sunflower.

3. graph twoway has two new plottypes for plotting time-series data, tsline and tsrline; see [TS] tsline and [G] graph twoway tsline.

4. Graphs have better axis labels when graphing dates.

5. graph twoway has seven new options that are useful when plotting time-formatted variables: tscale(), tlabel(), tmlabel(), ttick(), tmtick(), tline(), and ttext(); see [G] axis_options, [G] added_line_options, and [G] added_text_options.

6. graph twoway has seven new plottypes for plotting paired-coordinate data -- data with 4 variables, where two variables form a starting x-y point and the other two variables form an ending x-y point. The new plottypes are

plottype Description -------------------------------------------------------------------- pcarrow plots a directional arrow for each observation's paired coordinates pcbarrow plots a two-headed arrow for each observation's paired coordinates pcspike plots a line or spike for each observation pccapsym plots a line with symbols at each end for each observation pcscatter plots both pairs of x-y variables as a scatter, using a common style pci immediate form of paired-coordinate plots; plots the specified coordinate pairs pcarrowi immediate form of pcarrow --------------------------------------------------------------------

See [G] graph twoway pcarrow, [G] graph twoway pcbarrow, [G] graph twoway pcspike, [G] graph twoway pccapsym, [G] graph twoway pcscatter, [G] graph twoway pci, and [G] graph twoway pcarrowi.

7. graph twoway, graph bar, graph box, and graph dot have new option aspectratio() that controls the aspect ratio of a plot region; see [G] aspect option.

8. graph display has new option scale() that allows all text, symbols, and line widths to be rescaled when a graph is redisplayed; see [G] graph display.

9. graph export supports new export formats TIFF, PNG (portable network graphics), and TIFF previews for EPS files. See [G] graph export.

10. New option preview() with graph export embeds a preview of the graph so that it can be viewed in publishing applications; see [G] graph export and [G] eps options.

11. graph now supports CMYK output to Postscript and Encapsulated Postscript (EPS) files. CMYK stands for Cyan-Magenta-Yellow-blacK and is popular in the printing industry. See [G] graph export and [G] ps_options.

palette color has the new option cmyk, specifying that color values be reported in CMYK; see [G] palette.

12. graph box can now label outside values using option marker(); see [G] graph box and [G] marker label options.

13. graph bar has new options over(, reverse) and yvaroptions(reverse) to specify that the categorical scale be reversed, that it run from maximum to minimum; see [G] graph bar.

14. graph twoway has new option pcycle() that specifies the maximum number of plots that may appear on a graph before the pstyles recycle to the first style; see [G] advanced_options.

15. graph combine has new option altshrink that provides alternate sizing of the text, markers, line thickness, and line patterns on the individual combined graphs; see [G] graph combine.

16. graph has improved control over whether the largest and smallest possible grid lines are drawn. This control is provided by improving the actions of the existing suboptions [no]gmin and [no]gmax; see [G] axis_label_options.

17. graph bar, graph dot, graph box, and graph pie have new option allcategories specifying that the legend include all over() groups, not just groups in the sample specified by if and in. See, for example, [G] graph bar.

18. graph, and all other commands that draw graphs, have new options for changing the color of objects and changing the appearance of lines:

a. Options lstyle(), lcolor(), lwidth(), and lpattern() are now accepted anywhere cl<attribute> and the bl<attribute> were allowed. Specifically, the new options replace the following original options:

new options original options --------------------------------------- lstyle() clstyle(), blstyle() lcolor() clcolor(), blcolor() lwidth() clwidth(), blwidth() lpattern() clpattern(), blpattern() ---------------------------------------

The new options can be applied to all lines -- lines connecting points, lines outlining bars, lines around text boxes, etc. The original option names continue to work but are undocumented.

b. New option fcolor() changes area fill colors and can be used anywhere bfcolor() or afcolor() were allowed. bfcolor() and afcolor() continue to work but are undocumented.

c. New option color(arg) sets all of a plot's colors; it is the equivalent of specifying mcolor(arg), lcolor(arg), and fcolor(arg).

19. The syntax of the ROC curve commands is now consistent across all the ROC commands -- roctab, roccomp, rocgold, and rocplot -- with some new options added and some old options changing names. The original options continue to work but are undocumented. See [R] roctab and [R] rocfit postestimation.

20. Existing commands fracplot and lowess have new option lineopts() that replaces the confusingly named rlopts().

21. Option plot(), available on many graph commands, has been renamed addplot(). addplot() allows twoway plots, such as scatters, lines, or function plots to be added to most statistical graph commands.

22. Command kdensity has new option epan2 providing an alternate Epanechnikov kernel; see [R] kdensity. Accordingly, sts graph and stcurve now allow kernel(epan2) for specifying this new kernel.

23. The base margin for histogram graphs is now zero.

What's new: User interface

Stata 9 has a number of new features in the graphical user interface (GUI) that are shared across all platforms, such as multiple Viewer and Graph windows. There are also some significant improvements that affect only Windows, such as dockable windows. Most GUI features are documented in the Getting Started manual.

1. New versions of Stata are available:

a. Stata for Intel Itanium-based PCs running 64-bit Windows.

b. Stata for x86-64 standard systems, including those based on AMD Opteron chips, Athlon-64 chips and Intel Xeon emt64 chips running 64-bit Windows.

c. Stata for Intel Itanium-based PCs running 64-bit Linux.

d. Stata for x86-64 standard systems running 64-bit Linux.

2. Stata for Windows and Stata for Mac now have automatic update checking (nothing is ever downloaded without your confirmation). The first time you start Stata and every 7th day afterward, you will be prompted whether to check for updates.

To control how often you are prompted, or to turn the feature off, select Prefs > General Preferences, and select Internet; or you can type set update_interval # or set update_query off at the Stata prompt; see [R] update.

3. Stata now allows multiple Viewer windows so that you can, for example, simultaneously view the help for several commands and the results from several logs or search queries.

There are several ways to open another Viewer window.

a. While viewing something in a Viewer, hold down the shift key, and click on any link. A new Viewer will appear displaying the contents of the link.

b. Right-click on the link, and choose Open Link in New Viewer. That does the same thing.

c. Click with the middle mouse button on the link. That also does the same thing.

d. Right-click anywhere in an open Viewer, and choose Open New Viewer. This will open a new Viewer displaying help contents.

See 5. Using the Viewer in the Getting Started manual.

4. The Viewer also has the following new features:

a. It supports links within documents, including help files. You will see this feature used extensively in Stata's online help.

b. It has the ability to search for text within the window. Click on the find icon that looks like a pair of binoculars at the top right of the Viewer.

c. It now remembers its position in the document when you click Refresh.

In addition, both the Viewer and Results windows no longer underline links when they are displayed on a white background. You can change this by selecting Prefs > General Preferences.

5. Stata now allows multiple Graph windows. The existing name() option of [G] graph now creates a named graph and displays it in its own window of the same name.

Graph-management commands do what you would expect with the named windows; graph drop drops the graph and closes its window; graph rename renames both the graph and its window; and so on. Note that closing a Graph window does not delete the underlying graph and the graph can be redisplayed with graph display.

6. The Window menu now supports multiple Viewer and Graph windows:

a. You can switch to specific Viewers or Graphs from this menu.

b. Menu item Window > Viewer > Close All Viewers closes the Viewers.

c. Menu item Window Graph > Close All Graphs closes the graphs.

7. There are a number of enhancements to the toolbar:

a. The Open button now has a menu that shows recently opened datasets and allows you to reopen those datasets with a click. This even includes datasets loaded over the web from File > Example Datasets... or with webuse.

b. The Print button has a new menu that lets you select the window to print.

c. The Viewer button lets you switch to any Viewer or close all Viewers.

d. The Graph button lets you switch to any Graph or close all Graphs.

e. The Do-file Editor button lets you switch to any Do-file Editor (Windows and Mac).

8. A number of new features and improvements are available under the File menu:

a. Recently opened datasets can now be reopened by selecting File > Open Recent, and recently opened do-files or ado-files can likewise be reopened from within the Do-file Editor by selecting File > Open Recent.

b. File > Print lets you select the window to print.

c. All the datasets shipped with Stata and all the datasets used in the examples in the manuals can be browsed and loaded by selecting File > Example Datasets...

9. Stata now allows multiple Do-file editors under Windows and Mac. See 14. Using the Do-file Editor in the Getting Started manual.

10. Contextual menus for common tasks, such as setting preferences, copying to the clipboard, and printing, are now available in all windows; right-click in the window to access them.

11. You can now define multiple windowing preferences and switch easily among those preferences. For example, you might use small fonts and large Review and Variables windows for your normal work, but use large fonts with hidden Review and Variables windows for presentation. Access this new feature by selecting Prefs > Manage Preferences.

12. The Data Editor has several enhancements:

a. The contents of string variables and variables with value labels are now shown in different colors so that they can be easily distinguished.

b. Variables with value labels can now be displayed as either the value of the variable or the label.

c. For variables with value labels, you now may change the value of the variable by right-clicking on the cell and selecting Select Value from Value Label. You may then select the value and label from a list.

d. You may now associate an existing value label with a variable by right-clicking on the variable's column and selecting a value label from Assign Value Label to Variable.

e. You may now define or modify value labels from within the Data Editor by right-clicking and selecting Define/Modify Value Labels....

f. You can now access and modify the preferences for the Data Editor by right-clicking in the editor and selecting Preferences....

13. Dialogs have new features:

a. Keyboard shortcuts for Copy, Paste, and Cut now work.

b. Anywhere that you need to select a variable or variables for a varlist, you may now select those variables from a drop-down list (Windows and Mac).

c. The new copy button will copy the command built by the dialog to the clipboard. The button appears just right of the refresh button at the bottom left of each dialog. It works just like Submit, but rather than executing the command, it pastes the command.

d. Pressing the Return key now works the same as clicking OK; pressing Shift+Return works the same as clicking Submit. Pressing the Escape key works the same as clicking Cancel.

e. Pressing the space bar when the keyboard focus is on a radio button works the same as clicking on the radio button.

f. Keyboard arrow keys now work with dialog spinner controls.

g. Estimation-command dialogs are laid out better, with the model specification always appearing on the Model tab. You can also now select standard error (SE) types with a single click in the SE/Robust tab (which includes bootstrap and jackknife SEs as options for most estimators).

h. The twoway graph dialog boxes are laid out better, with easier selection of the plottype (scatter, line, range bar, etc.) and the addition of the new paired-coordinate time-series plottypes.

In addition, the printed manual and online documentation do a better job of describing the options and controls available on a dialog. The option entries in the manual and online are grouped into categories that match the tabs on the dialog box.

14. Stata for Windows has vastly improved flexibility for managing your work environment:

a. Most windows -- the dockable ones -- can now be docked with the main Stata window or with each other. By dragging a dockable window over another dockable window, you may create either a single-paned window, containing both the original windows with a separator in between, or a single window with tabs for each of the original windows. The Viewer, Command, Review, and Variables windows are all dockable.

In addition, any of these windows can either be attached (docked) to the main Stata window or detached and made free floating. Each also has a pin icon in the title bar that makes the window always shown, or makes it roll up into its title bar when undocked, or makes it shown only as a tab when docked. For an overview of these features, see 4. The Stata user interface in the Getting Started manual.

b. Most windows can be moved outside the main Stata window. These include the Graph, Viewer, Browse, and Edit windows, and include all dialogs.

c. The toolbar can be detached and repositioned.

d. Double-clicking the Results window, when it is docked, merges it with the main Stata window as the primary document. This saves some screen real estate, and we suggest that you try it. Double-click again to undo it.

e. A number of new window preferences available from the Windowing tab under Prefs > General Preferences... let you control how windows behave and how they dock. You can lock paned windows so that they cannot be resized, turn on or off docking, turn on or off the docking guides, make all windows floating, make the contents of Viewers persistent so that they maintain their contents between Stata sessions, and even turn off all the advanced windowing features to lock your current settings.

f. As with Stata on all other platforms, you can now save multiple windowing preferences and choose the one most appropriate for what you are doing, for example, working at home, giving a presentation, etc.

If you are fond of the way Stata for Windows worked prior to Stata 9, or you like to maximize your Stata window, we suggest that you select from the menu Prefs > Manage Preferences > Load Preferences > Maximized. Even so, we recommend that you try using the new layout without maximizing the Stata window.

15. You may now copy the Review window to the Clipboard. Right-click in the window to access the contextual menu.

16. help now displays in the Viewer window; new command chelp displays in the Results window. help also has two new options:

a. nonew displays help in the topmost viewer rather than in a new one.

b. name(viewername) displays the help in the specified viewer. If that name does not exist, a new viewer will be created with that name.

17. You may now define and access notes for a variable by right-clicking on the variable name in the Variables window. Right-clicking on an empty space allows you to define and access notes for the dataset.

18. The Do-file Editor has a new SMCL preview button on its toolbar that displays the current file in the Viewer as rendered SMCL.

19. (Windows and Mac) You can now copy selected text as an HTML table using Edit > Copy Table as HTML.

20. (Unix) The minimize keyboard shortcut <Ctrl>-m has been added to all windows.

21. (Unix) You can now use the Window menu's keyboard shortcuts from any window.

22. (Mac) You can now increase or decrease the font size in a window by pressing Apple + and Apple -.

23. (Mac) The ability to undo or redo multiple actions has been added to the Do-file editor.

24. (Mac) You can now have Stata automatically bring all windows to the front when it is active by selecting Prefs > General Preferences....

25. (Mac) You can now have Stata automatically snap windows to the edge of the main Stata window or to the edge of the screen when you move or resize them by selecting Prefs > General Preferences....

26. (Mac) You can move all of Stata's currently open windows simultaneously by holding down the Control key while dragging one of the windows. This will also bring all Stata's open windows to the foreground.

27. (Mac) The toolbar may be a floating window or may be anchored to the menubar. The advantage of making the toolbar float is that it takes up less room on the screen and can be moved. Access this feature using Window > Toolbar.

What's new: Programming

1. Mata, Stata's new matrix-programming language can by used to code ado-file subroutines; see [M-1] ado.

2. New command viewsource displays official and user-written source code. viewsource searches for the specified file along the adopath and displays the file in the Viewer. This works not only for ado programs, but also for Mata functions that are programmed themselves in Mata. See [P] viewsource.

3. Programmers of estimation commands or commands that work with estimation results can tie postestimation analysis facilities into estat, making their postestimation facilities behave just like those shipped with Stata; see estat programming.

4. New command matlist provides extensive format control for displaying a matrix; see [P] matlist.

5. Macro-extended functions that work on matrices will now work on the matrices stored in r() and e(), including e(b) and e(V). These extended functions include rownames, colnames, roweq, coleq, rowfullnames, and colfullnames. See [P] matrix.

6. c() (c-class returned values) has the following new items:

item description ----------------------------------------------------- c(Wdays) "Sun Mon ... Sat" c(Weekdays) "Sunday Monday Tuesday ... Saturday" c(alpha) "a b c d e f h j ... x y z" c(ALPHA) "A B C D E F H J ... X Y Z" c(Mons) "Jan Feb ... Dec" c(Months) "January February March ... December" c(tracehilite) pattern to be highlighted in trace log c(maxiter) maximum iterations for maximum likelihood estimators c(varabbrev) whether variable abbreviation is on -----------------------------------------------------

7. A program can now be assigned properties when the program is declared, and those properties can be checked using macro-extended functions. Specifically,

a. program has the new option properties(), which attaches properties to programs; see [P] program.

b. A new properties macro-extended function allows programmers to obtain the list of properties attached to a program; see [P] macro.

To learn more, see [P] program properties.

8. Estimation results can now be assigned properties using new option properties() of ereturn post and ereturn repost. These property settings can be checked with the new function has_eprop(). See [P] ereturn and [FN] Programming functions.

9. ereturn post now allows posting results without a beta vector, e(b), or a covariance matrix, e(V).

10. version has new option born() to prevent the program from running if the date of the Stata executable is earlier than the specified date. version also issues more descriptive error messages. See [P] version.

11. On Microsoft Windows and Unix platforms, the new command window manage maintitle allows you to reset the main title of the Stata window; see manage maintitle under [P] window programming.

12. New command levelsof displays a sorted list of the distinct values of a variable. This is especially useful for looping over the values of a variable with, say, foreach. See [P] levelsof.

13. Plugins (also known as DLLs or shared objects) written in C can now be incorporated into Stata to create new Stata commands; see [P] plugin.

14. The maximum number of description lines in a stata.toc file has been increased from 10 to 50; see [R] net

15. New undocumented command _coef_table is a programmer's tool for displaying coefficient tables; see _coef_table.

16. trace has new setting set tracehilite to highlight a specified pattern in the trace output; see [P] trace.

17. The functionality of macval() has been extended to macro dereferencing of values in a class. For example, `macval(.a.b.c)' causes the class reference .a.b.c to be macro expanded only once, rather than being recursively re-expanded when the result itself contained a macro reference.

18. Variable abbreviation can now be turned on and off using the new set varabbrev; see [R] set or type help set varabbrev.

19. Command syntax has new specifier syntax anything(everything) that specifies that anything include if, in, and using; see [P] syntax.

20. Command syntax has new option descriptor cilevel that restricts valid arguments to a standard confidence level and issues appropriate error messages for invalid entries; see help [P] syntax.

21. A number of new directives and extensions to existing directives have been added to SMCL. They are summarized below within broad categories; see smcl for complete documentation.

Jumping to marked locations in help or other files directive description -------------------------------------------------------------------- {marker pos1} marks the current position in a file as pos1 {help "regress##pos1"} opens the help file regress.hlp at the marked position pos1 {view "my.smcl##pos1"} opens the file my.smcl at the marked position pos1 --------------------------------------------------------------------

Opening help or other files in new or multiple Viewers directive description -------------------------------------------------------------------- {help "regress##|mywin"} opens the help file regress.hlp in a new Viewer window named mywin {help "regress##pos1|mywin"} opens the help file regress.hlp at the marked position pos1 in a new Viewer window named mywin {help "regress##|_new"} opens the help file regress.hlp in a new Viewer window

{view "my.smcl##|mywin"} opens the file my.smcl in a new Viewer window named mywin {view "my.smcl##pos1|mywin"} opens the file my.smcl at the marked position pos1 in a new Viewer window named mywin {view "my.smcl##|_new"} opens the file my.smcl in a new Viewer window --------------------------------------------------------------------

Special formatting of links to help files directive description -------------------------------------------------------------------- {helpb help_topic} creates link to help_topic.hlp, just like {help help_topic}, but displays the link in bold. {helpb help_topic:text} creates link to help_topic.hlp, just like {help help_topic:text}, but displays text in bold.

{manhelp help_topic R:text} displays [R] text and links to help_topic.hlp {manhelpi help_topic R:text} displays [R] text and links to help_topic.hlp --------------------------------------------------------------------

Two-column tables with indented wrapping of last column directive description -------------------------------------------------------------------- {p2colset # # # #} declares column spacing for ensuing table lines that use the {p2col:...} directive {p2colreset} restores default column spacing {p2col:text 1} displays text 1 in column 1 and enters paragraph mode in column2 for any text that follows until a paragraph end is signaled; an extended syntax allows columns to be specified {p2coldent:text 1} just like {p2col}, except text_1 is output with a standard indentation for syntax-diagram-option tables {p2line} draws a line the width of the table; extended syntax allows margins around the line --------------------------------------------------------------------

New documentation conventions for syntax-diagram-option tables directive description -------------------------------------------------------------------- {synoptset [#] [tabbed]} declares default column spacing for syntax-diagram-option tables {synopt[:option_text]]} displays option_text in column 1 and enters paragraph mode in column 2 for any text that follows until the paragraph terminates {syntab:text]} outputs text positioned as a subheading or "tab" in a syntax diagram option table {synopthdr[:column1_text]} displays a standard header for a syntax-diagram-option table. {synoptline} draws a horizontal line extending to the boundaries of the previous {synoptset} --------------------------------------------------------------------

New documentation conventions for variables and varlists directive description -------------------------------------------------------------------- {newvar} displays newvar while providing a link to help newvar; new convention for documenting that a command accepts a new variable {varname} displays varname while providing a link to help varname; new convention for documenting that a command accepts a variable {var} displays varname while providing a link to help varname; abbreviated form of {varname} {varlist} displays varlist while providing a link to help varlist; new convention for documenting that a command accepts a varlist {vars} displays varlist while providing a link to help varlist; abbreviated form of {varlist} {depvar} displays depvar while providing a link to help depvar; new convention for documenting that a command accepts a dependent variable {depvarlist} displays depvarlist while providing a link to help depvarlist; new convention for documenting that a command accepts a list of dependent variables {depvars} displays depvars while providing a link to help depvarlist; abbreviated form of {depvarlist} {indepvars} displays indepvars while providing a link to help varlist; new convention for documenting that a command accepts a list of independent variables -------------------------------------------------------------------- Note that the only change in convention is the addition of links to help files describing the syntax of variables and varlists.

Each of the above directives also accepts an optional argument that is displayed immediately after the standard display but does not otherwise change the link; for example, {varlist:_1} displays varlist_1 but continues to link to help varlist.

Other new documentation conventions directive description -------------------------------------------------------------------- {ifin} displays [if] [in] while providing links to if and in; new convention for documenting support for if and in in a syntax diagram {weight} displays [weight] while providing a link to help weight; new convention for documenting support for weights in a syntax diagram {dtype} displays [type] while providing a link to help datatypes; new convention for documenting that a command accepts an optional datatype in its syntax {dlgtab:text} displays text while giving it the appearance of labeled a dialog tab (extended forms support additional formatting) --------------------------------------------------------------------

Directives that simplify documenting options directive description -------------------------------------------------------------------- {opt optname} document options; equivalent to {cmd:optname}

{opt opt:name} document options that can be abbreviated; {opt my:opt} displays myopt

{opt my:opt(arg)} document options that take arguments; in this example, the option is named myopt and can be abbreviated my - myopt(arg); directive will correctly display arguments that are lists, such as a,b,... or a|b|c|...

{opth my:opt(arg)} like {opt my:opt(arg)} documents options that take arguments, but also provides a link to help for arg; for example, {opth my:opt(varlist)} displays myopt(varlist); extended syntax allows the linked help to differ from the displayed argument --------------------------------------------------------------------

Directives abbreviating standard paragraph forms directive description -------------------------------------------------------------------- {pstd} equivalent to {p 4 4 2} {psee} equivalent to {p 4 13 2} {phang} equivalent to {p 4 8 2} {pmore} equivalent to {p 8 8 2} {pin} equivalent to {p 8 8 2} {phang2} equivalent to {p 8 12 2} {pmore2} equivalent to {p 12 12 2} {pin2} equivalent to {p 12 12 2} {phang3} equivalent to {p 12 16 2} {pmore3} equivalent to {p 16 16 2} {pin3} equivalent to {p 16 16 2} --------------------------------------------------------------------

Other new directives and extensions to existing directives directive description -------------------------------------------------------------------- {mata args[:text]} like the {stata} directive, but for mata; displays text, and when text is clicked, executes the mata command args

{rcenter:text} places text one space to the right when there are unequal spaces left and right

{hline #} draws a horizontal line stopping # characters from the end of the line --------------------------------------------------------------------

22. Existing command window manage has the following changes and additions:

window manage close graph [{graphname | _all}] closes the Graph window named graphname, if it exists. Specifying _all, closes all Graph windows.

window manage forward graph [graphname] now brings the Graph window named graphname to the top of other windows and otherwise works as before.

window manage close viewer [{viewername | _all}] closes the Viewer window named viewername. Specifying _all closes all Viewer windows.

window manage forward viewer [viewername] now brings the Viewer window named viewername to the top of other windows and continues to work as before when no viewername is specified.

window manage minimize minimizes the main Stata window.

window manage restore restores the main Stata window, if it is minimized.

23. Existing command window menu now has the new subcommand append_recentfiles to add .dta or .gph files to the Open Recent menu.

24. Existing command confirm variable has new option exact that disallows variable abbreviations.

25. New command svymarkout resets the value of a supplied 0/1 variable to 0 when any of the survey-characteristic variables set by svyset contain missing values; see [SVY] svymarkout.

26. Help files now allow include files. Syntax is INCLUDE help helptopic to include file helptopic.ihlp.

27. String scalars are now supported, meaning that a scalar can contain either a numeric or string value. The maximum length of a string scalar is the same as the maximum length of a string -- 244 characters. See [P] scalar.

28. In addition to coding "local x : all scalars" to obtain a list of all defined scalars, you can now code "local x : all numeric scalars" and "local x : all string scalars" to obtain the list restricted to numeric or string scalars. See [P] macro.

29. In macro expansion, double backslash (\\) used to become single backslash (\). Now (but under version control) it becomes single backslash only if the second backslash precedes macro-expansion punctuation (` or $).

What's new: Documentation

1. There are new manuals: [D] Data management, [MV] Multivariate Statistics, and [M] Mata.

2. Documentation (printed and online) groups related options into categories. In addition, the categories match the tabs on dialog boxes.

3. For all estimation commands, there is now an entry called postestimation following the estimation command. For instance, following [R] regress is [R] regress postestimation. The postestimation entry documents command-specific postestimation facilities to further analyze the results and also directs you to other relevant postestimation features.

In the online help system, go to help for the estimation command, and click on postestimation in the upper-right corner.

4. There are now glossaries in the [M], [SVY], [TS], and [XT] manuals. The glossaries define commonly used terms and explain how these terms are used in the documentation.

5. Stata's help command and online help facility have new features:

a. Spaces and colons are now allowed in help topics, for example, help graph intro, help regress postestimation, or help svy: logistic (with or without the colon).

b. Typing help sqrt() now gives you help for Stata's sqrt() function. Typing help mata sqrt() gives you help for Mata's sqrt() function.

c. Many command abbreviations are now recognized; for example, help reg post is understood to mean help regress postestimation, and help tw con is understood to mean help graph twoway connected.

--- previous updates ----------------------------------------------------------

See whatsnew8.


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