.-
help for ^swboot^                                         (JMGarrett 12/10/99)
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Bootstrap stepwise linear or logistic regression models  
-------------------------------------------------------

   ^swboot^ yvar xvars [^if^ exp] [^in^], [^r^eps^(^#^) pe(^#^) pr(^#^) for^war
> d ^n(^#^)^
                                        ^mod^el ^roc gof^]
 

^swboot^ uses bootstrap samples of size _N (based on number of observations
   without missing values) to validate the choice of variables in stepwise
   procedures for linear or logistic regression; variables selected are
   displayed for each sample drawn; a summary at the end counts the total
   number of times each variable is selected; backward stepwise algorithm is
   assumed unless "forward" option is specified

 
Variables required
------------------

    yvar -- dependent variable

      If yvar is continuous, defaults to linear regression
      If yvar is binary (0,1), defaults to logistic regression

    xvars -- list of independent variables

        
Options 
-------

   ^reps(^#^)^ -- number of samples drawn and stepwise models repeated (default
> =1)
   ^pe(^#^)^ -- sign. level for a variable to enter the model (default=.05)
   ^pr(^#^)^ -- sign. level for a variable to remain in the model (default=.10)
   ^forward^ -- forward (rather than backward) stepwise regression
   ^n(^#^)^ -- bootstrap sample size; if not specified, defaults to whole data
           set; if specified, can't be larger than original (based on
           observations with no missing values for the variables listed)
   ^model^ -- displays the model for each rep (default: selected variable list)
   ^roc^ -- displays the area under the ROC curve for each rep
   ^gof^ -- performs the Hosmer-Lemeshow goodness-of-fit test for each rep


Examples
--------

 . ^swboot hpt age race sex educ ses smk bmi chl, reps(50)^
      Select 50 samples and run stepwise logistic regression to choose sets
      of predictors of hypertension; pr defaults to .1 and pe defaults to .05

 . ^swboot chol age race sex ses smk, reps(100) forward pr(.05) pe(.01)^
      Select 100 samples and run stepwise linear regression to choose sets of 
      predictors of cholesterol; uses a foward stepwise method