# SUDAAN 11 Examples

__Weighting and Imputation Procedures__

WTADJUST Examples | Produces nonresponse and poststratification sample weight adjustments using a calibration approach to selection modeling (when there is nonresponse or known coverage errors) and sample balancing. |

(includes standard errors from WTADJUST and nonresponse adjustment with RLOGIST) |
As in WTADJUST, WTADJX produces nonresponse and post-stratification weighting adjustments using a calibration approach to selection modeling and sample balancing. In WTADJX, however, the variables used in the selection or weight-adjustment model need not be the same as the calibration variables.New in Release 11. |

IMPUTE Examples |
Performs the weighted sequential hot deck and, , cell mean, and regression-based (linear and logistic) methods of imputation for item nonresponse. new in Release 11 |

__Descriptive Procedures__

CROSSTAB Examples | Computes frequencies, percentage distributions, odds ratios, relative risks, and their standard errors (or confidence intervals) for user-specified cross-tabulations, as well as chi-square tests of independence and a series of Cochran-Mantel-Haenszel chi-square tests associated with stratified two-way tables. |

RATIO Examples |
Computes estimates, standard errors, and confidence limits of generalized ratios of the form Σ_{i} w_{i}x_{i } / Σ_{i} w_{i}yi. Computes standardized estimates and tests single-degree-of-freedom contrasts among levels of a categorical variable. |

DESCRIPT Examples |
Computes estimates of means, totals, proportions, percentages, geometric means, quantiles, and their standard errors and confidence limits; also computes standardized estimates and tests of single degree-of-freedom contrasts among levels of a categorical variable. |

VARGEN Examples |
. Computes point estimates, design-based variances, and contrast estimates for any user-defined parameter that can be expressed as a function of means, totals, proportions, ratios, population variances, population standard deviations, and correlations. New in Release 11 |

__Survival Procedures__

SURVIVAL Examples | Fits discrete and continuous proportional hazards models to failure time data; also estimates hazard ratios and their confidence intervals for each model parameter. Estimates exponentiated contrasts among model parameters (with confidence intervals). Includes facilities for time-dependent covariates, the counting process style of input, stratified baseline hazards, and Schoenfeld and Martingale residuals. Estimates conditional and predicted marginals and tests hypotheses about the marginals. |

KAPMEIER Examples | Fits the Kaplan-Meier model, also known as the product limit estimator, to survival data from sample surveys and other clustered data applications. KAPMEIER uses either discrete or continuous time variable to provide point estimates for the survival curve for failure time outcomes that may contain censored observations. |

__Regression Procedures__

REGRESS Examples | Fits linear regression models and performs hypothesis tests concerning the model parameters. Uses Generalized Estimating Equations (GEE) to efficiently estimate regression parameters with robust and model-based variance estimation. Estimates conditional and predicted marginals and tests hypotheses about the marginals. |

LOGISTIC Examples | Fits logistic regression models to binary data and computes hypothesis tests for model parameters; also estimates odds ratios and their confidence intervals for each model parameter; estimates exponentiated contrasts among model parameters (with confidence intervals); uses GEE to efficiently estimate regression parameters, with robust and model-based variance estimation. Estimates conditional and predicted marginals, ratios of marginals, and tests hypotheses about the marginals. |

MULTILOG Examples | Fits logistic and multinomial logistic regression models to ordinal and nominal categorical data and computes hypothesis tests for model parameters; estimates odds ratios and their confidence intervals for each model parameter; estimates exponentiated contrasts among model parameters (with confidence intervals), uses GEE to efficiently estimate regression parameters, with robust and model-based variance estimation. Estimates conditional and predicted marginals, ratios of marginals, and tests hypotheses about the marginals. |

LOGLINK Examples | Fits log-linear regression models to count data not in the form of proportions. Typical examples involve counts of events in a Poisson-like process where the upper limit to the number is infinite. Estimates incidence density ratios and confidence intervals for each model parameter. Estimates exponentiated contrasts among model parameters (with confidence intervals). Uses GEE to efficiently estimate regression parameters, with robust and model-based variance estimation. Estimates conditional and predicted marginals and tests hypotheses about the marginals. |

__Utility Procedure__

RECORDS Examples |
Prints observations from the input data set, obtains the contents of the input data set, and converts an input data set from one type to another. You can use the SUBPOPN or SUBPOPX statement to create a subset of a given data set, and you can use the SORTBY statement to sort your data. RECORDS is a non-analytic procedure. |