help for ^swboot^                                         (JMGarrett 12/10/99)

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