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.