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.
| Examples | Illustrates | Datasets Used |
|---|---|---|
| WTADJUST Example 1 | MODEL, WTMIN, WTMAX, LOWERBD, UPPERBD | See SAS code within the example |
| WTADJUST Example 2 | REFLEVEL, WTMAX, SETENV, UPPERBD, LOWERBD | samadult.SAS7bdat |
| WTADJUST Example 3 | CENTER, POSTWGT, WTMIN, WTMAX, CLASS | demo_d2.xpt |
| WTADJUST Example 4 | VPAIRWISE, CLASS, PREDSTAT, NEST2, MODEL | See SAS code within the example |
WTADJX Examples
(includes standard errors from WTADJUST and nonresponse adjustment with RLOGIST)
New in Release 11. 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.
| Examples | Illustrates | Datasets Used |
|---|---|---|
|
WTADJX Example 1 (also includes standard errors for WTADJUST) |
Raking, raking to a size variable, quasi-optimal calibration, standard-error estimation after calibration weighting; ADJUST = POST, CALVARS, PSTWGT, CLASS, VAR. | dawn.SAS7bdat |
|
WTADJX Example 2 (also includes nonresponse adjustment and resulting standard-errors using RLOGIST and WTADJUST) |
Adjusting for nonresponse when nonresponse may not be missing at random, standard-error estimation after nonresponse adjustment; ADJUST = NONRESPONSE, ADJUST = POST, VARNONADJ, NUMER, DENOM, MAXITER, BESTIM, VDIFFVAR. | dawn.SAS7bdat |
IMPUTE Examples
Performs the weighted sequential hot deck and, new in Release 11, cell mean, and regression-based (linear and logistic) methods of imputation for
item nonresponse.
| Examples | Illustrates | Datasets Used |
|---|---|---|
| IMPUTE Example 1 | WSHD, CELLMN, LINEAR, and LOGISTIC Imputations. IMPBY, IMPVAR, CLASS, PRINT and OUTPUT | WIC.SAS7bdat |
| IMPUTE Example 2 | WSHD Multiple Imputations, NOTSORTED option, IMPBY, IMPVAR (MULTIMP option), IMPNAME, PRINT and OUTPUT | WICWAGE.SAS7bdat |
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.
| Examples | Illustrates | Datasets Used |
|---|---|---|
| Crosstab Example 1 | SETENV optional statement, CHISQ, LLCHISQ, PRINT, RFORMAT, SEWGT option | NHANES3S3.SAS7bdat |
| Crosstab Example 2 | SETENV optional statement, CMH test (Cochran-Mantel-Haenszel), PRINT, RFORMAT, SEWGT | NHANES3S3.SAS7bdat |
| Crosstab Example 3 | Breslow-Day Test Odds Ratio or Relative Risk, Breslow-Day Test of Homogeneity of "Odds Ratios", Prevalence Ratio, Risk Statement | NHANES3S3.SAS7bdat |
| Crosstab Example 4 | TEST, PRINT STEST option, SUBPOPX, SETENV, RFORMAT | NHANES3S3.SAS7bdat |
| Crosstab Example 5 | Accounting for multiple imputation of variables, Taylor series linearization method, BRR method with Fay's adjustment, SUBPOPX, SETENV | NHANES3S3.SAS7bdat |
| Crosstab Example 6 | Small Percentage Confidence Interval (SPCI), ROWPER, POWSPCI, STYLE option, RFORMATE, SUBPOPX | NHANES3S3.SAS7bdat |
| Crosstab Example 7 | Goodness-of-fit (GOF) Test, GOF Test using GOFIT statement, Wald-F (WALDF) Test, Satterthwaite-adjusted Chi-square (SATADJCHI) Test, SUBPOPX statement | NHANES3S3.SAS7bdat |
| Crosstab Example 8 | Stratum-specific Chi-square (CHISQ) Test, Stratum-adjusted Cochran-Mantel-Haenszel (CMH) Test, ANOVA-type (ACMH) Test, ALL Test option, DISPLAY option | NHANES3S3.SAS7bdat |
| Crosstab Example 9 | Kappa measure of agreement, AGREEE statement, TABLE statement, MERGHI option, WSUM option | NHANES3S3.SAS7bdat |
RATIO Examples
Computes estimates, standard errors, and confidence limits of generalized ratios of the form Σi wixi / Σi wiyi.
Computes standardized estimates and tests single-degree-of-freedom contrasts among levels of a categorical variable.
| Examples | Illustrates | Datasets Used |
|---|---|---|
| RATIO Example 1 | NEWVAR, DENOM, NUMBER, SETENV, WEIGHT | NHANES3S3.SAS7bdat |
| RATIO Example 2 | POLY statement replaces, TABLES statement, Test of trends, NEST, WEIGHT, NEWVAR | NHANES3S3.SAS7bdat |
| RATIO Example 3 | DENOM, DENCAT, NUMBER, NUMCAT, SUBPOPX | SADLTST3.SSD |
| RATIO Example 4 | Compares PROC CROSSTAB versus PROC RATIO, SUBPOPX, LEVELS, SUBGROUP, SETENV | SADLTST3.SSD |
| RATIO Example 5 | REPWGT, WEIGHT, ADJFAY option, NUMBER, DENOM | DESCRPTT.XPT |
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.
| Examples | Illustrates | Datasets Used |
|---|---|---|
| DESCRIPT Example 1 | SUBPOPN, NEST, RFORMAT, LEVELS, WEIGHT | NHANES3S3.SAS7bdat |
| DESCRIPT Example 2 | PERCENTILE, VAR, WEIGHT, SUBPOPN, RFORMAT | NHANES3S3.SAS7bdat |
| DESCRIPT Example 3 | SUBPOPN, SETENV, Design effect (DEFT2) option, STYLE option, NEST | NHANES3S3.SAS7bdat |
| DESCRIPT Example 4 | CONTRAST, PAIRWISE, DIFFVAR, SUBPOPN, SETENV | NHANES3S3.SAS7bdat |
| DESCRIPT Example 5 | CATLEVEL, VAR, DEFT1 option, RLABEL, RFORMAT | NHANES3S3.SAS7bdat |
| DESCRIPT Example 6 | VAR, CATLEVEL, NOMARG option, SETENV, RFORMAT | NHANES3S3.SAS7bdat |
| DESCRIPT Example 7 | VAR, CATLEVEL, CONTRAST, SETENV, RFORMAT | NHANES3S3.SAS7bdat |
| DESCRIPT Example 8 | TOTPER option, NSUM option, WEIGHT, LEVELS, SUBGROUP | NHANES3S3.SAS7bdat |
| DESCRIPT Example 9 | MI_COUNT option, SETENV, RFORMAT, NEST, WEIGHT |
VARGEN Examples
New in Release 11. 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.
| Examples | Illustrates | Datasets Used |
|---|---|---|
| VARGEN Example 1 | XPER, PARAMETER, SUBPOPX, CLASS, DIFFVAR | DA32722P1.SAS7bdat |
| VARGEN Example 2 | XMEAN, XRATIO, XSUM, PARAMETER, SUBPOPX | DA32722P1.SAS7bdat |
| VARGEN Example 3 | XMEAN, XRATIO, XSUM, PARAMETER, SUBPOPX | DA32722P1.SAS7bdat |
| VARGEN Example 4 | XRATIO, XMEAN, PARAMETER, SUBPOPX, NOTSORTED | DA32722P1.SAS7bdat |
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.
| Examples | Illustrates | Datasets Used |
|---|---|---|
| SURVIVAL Example 1 | Accounting for intracluster correlation in survival analysis, EVENT, CLASS, EFFECTS, REFLEVEL | IRONSUD.SSD |
| SURVIVAL Example 2 | Cox proportional hazards model TIES option, WALDCHI (WALD chi-square test) option, SATADJCHI (Satterthwaite-adjusted chi-square test) option, EFFECTS | EXERCISE.SAS7bdat |
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.
| Examples | Illustrates | Datasets Used |
|---|---|---|
| KAPMEIER Example 1 | Kaplan-Meier survival probability curve, STRHAZ, DESIGN, EVENT, TIME | EXERCISE.SAS7bdat |
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.
| Examples | Illustrates | Datasets Used |
|---|---|---|
| REGRESS Example 1 | TEST, SUBPOPX, REFLEVEL, COND_EFF, LSMEANS | NHANES_C_3.SAS7bdat |
| REGRESS Example 2 | GEE linear regression, Delete-1 Jackknife variance estimation, Binder robust variance estimator, TEST, CONDMARG | BORIC.SSD |
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.
| Examples | Illustrates | Datasets Used |
|---|---|---|
| Logistic Example 1 | EFFECTS, RFORMAT, RLABEL, REFLEVEL, EXP option on MODEL statement, Hosmer-Lemeshow Test | BRFWGT.SAS7bdat |
| Logistic Example 2 | Zeger and Liang's SE method, Naive SE method, Conditional marginals, REFLEVEL, SETENV | BRFWGT.SAS7bdat |
| Logistic Example 3 | PREDMARG (predicted marginal proportion), CONDMARG (conditional marginal proportion), PRED_EFF pairwise comparison, COND_EFF pairwise comparison, SUBPOPX | SAMADULTED.SAS7bdat |
| Logistic Example 4 | SEs by replicate method, REPWGT, EFFECTS, EXP option, REFLEVEL | |
| Logistic Example 5 | Modeling 2 interation terms, Test for "chunk interations", EFFECTS, SUBPOPX, REFLEVEL | SAMADULTED.SAS7bdat |
| Logistic Example 6 | PRED_EFF, PREDMARG, EFFECTS, SUBPOPX, REFLEVEL | SAMADULTED.SAS7bdat |
| Logistic Example 7 | EFFECTS, UNITS option, EXP option, SUBPOPX, REFLEVEL | SAMADULTED.SAS7bdat |
| Logistic Example 8 | Calculates R-indicator and propensity statistics, PREDSTAT, PSTD, PVAR, PMEAN, PRSTD | ELS.SAS7bdat |
| Logistic Example 9 | Calculation of response rates and standard errors, PREDSTAT, RESPRATE, SETENV, NEST | ELS.SAS7bdat |
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.
| Examples | Illustrates | Datasets Used |
|---|---|---|
| MULTILOG Example 1 | Logistic regression modeling, R and SEMETHOD options, CONDMARG, ADJRR option, CATLEVEL | DARE.SSD |
| MULTILOG Example 2 | GEE model-fitting with multinomial outcomes, Correlations, R options, Standard error, SEMETHOD option, CUMLOGIT option, CONDMARG | CROSS.SSD |
| MULTILOG Example 3 | REFLEVEL, CUMLOGIT option, SETENV, LEVELS, WEIGHT | IRONSUD.SSD |
| MULTILOG Example 4 | EFFECTS, CUMLOGIT option, SUBGROUP, LEVELS, SETENV | IRONSUD.SSD |
| MULTILOG Example 5 | PREDMARG, ADJRR option, GENLOGIT option, PRED_EFF, SUBPOPX |
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.
| Examples | Illustrates | Datasets Used |
|---|---|---|
| LOGLINK Example 1 | Log-linear regression modeling, MODEL, TEST, SUBPOPN, EFFECTS | EPIL.SAS7bdat |
| LOGLINK Example 2 | Log-linear regression modeling, SEMETHOD, REFLEVEL, EFFECTS, PREDMARG | PERSONSX.SAS7bdat |
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.
| Examples | Illustrates | Datasets Used |
|---|---|---|
| RECORDS Example 1 | DATA option, FILETYPE option, COUNTREC option, CONTENTS optin, NOPRINT option | EXERCISE.SAS7bdat |
| RECORDS Example 2 | SORTBY, OUTPUT, REPLACE option, PRINT | HANES3S3.SAS7bdat |