NOTE: Beginning in SAS 9, you can use the ODS GRAPHICS ON; statement and the PLOTS=SCATTER(ELLIPSE=MEAN) or PLOTS=SCATTER(ELLIPSE=PREDICTED) option in the PROC CORR statement to get confidence ellipse plots about the mean or individual values.
PURPOSE:
The %CONELIP macro generates confidence ellipses for bivariate normal data. It can either create ellipses for the data or ellipses about the mean.
The %JACK and %BOOT macros do jackknife and bootstrap analyses for simple random samples, computing approximate standard errors, bias-corrected estimates, and confidence intervals assuming a normal sampling distribution.
The %JACK macro does jackknife analyses for simple random samples, computing approximate standard errors, bias-corrected estimates, and confidence intervals assuming a normal sampling distribution.
The %BOOT macro does elementary nonparametric bootstrap analyses for simple random samples, computing approximate standard errors, bias-corrected estimates, and confidence intervals assuming a normal sampling distribution. Also, for regression models, the %BOOT macro can resample either observations or residuals.
The %BOOTCI macro computes several varieties of confidence intervals that are suitable for sampling distributions that are not normal.
The %CumIncid macro for estimating and plotting cumulative incidence functions with competing risks is discussed.
This version of the CUMINCID macro applies only to SAS 9.1 which is available on the Downloads tab. For SAS 9.2 and later, refer to the Autocall macro library.
The CUMINCID macro computes the crude cumulative-incidence function estimates for homogeneous (no covariates) survival data whose endpoints are subjected to competing risks: see Kalbfleish and Prentice(1980). Standard errors and pointwise confidence limits are also computed. The estimated crude cumulative-incidence curve is displayed as a step function using ODS Graphics.
The NLEstimate macro allows you to estimate one or more linear or nonlinear combinations of parameters from any model for which you can save the model parameters and their variance-covariance matrix. Most modeling procedures which offer ESTIMATE, CONTRAST, or LSMEANS statements only provide for estimating or testing linear combinations of model parameters. However, common estimation problems often involve nonlinear combinations, particularly in generalized models with nonidentity link functions such as logistic and Poisson models.
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The %INTRACC macro calculates reliabilities for intraclass correlations. The macro calculates the six intraclass correlations discussed in Shrout and Fleiss (1979). Additionally it calculates two intraclass correlations using formulae from Winer (1971) which are identical to two of the six from Shrout and Fleiss. It also calculates the reliability of the mean of nrater ratings, where nrater is a parameter of the macro, using the Spearmen-Brown prophecy formula so that one can examine the effect obtaining more raters would have on the reliability of a mean.
NOTE: This macro is obsolete beginning with SAS 8.0. Use the STDIZE procedure in SAS/STAT software beginning in that release.
PURPOSE:
The %STDIZE macro standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure. A variety of location and scale measures are provided, including estimates that are resistant to outliers and clustering
NOTE: The MVN macro is obsolete. Beginning in SAS 9.2, use the RANDNORMAL function in SAS/IML software or PROC SIMNORMAL in SAS/STAT software to generate multivariate normal data.
PURPOSE:
The %MVN macro generates multivariate normal data using the Cholesky root of the variance-covariance matrix. Bivariate normal data can be generated using the DATA step.
Nonparametric comparison of areas under correlated ROC curves. Provides point and confidence interval estimates of each curve's area and of the pairwise differences among the areas. Tests of the pairwise differences are also given. Any contrast among the areas may be estimated and tested.