逐步回归代码.docx

2.逐步回归代码 stepfunction (object, scope, scale = 0, direction = c(both, backward, forward), trace = 1, keep = NULL, steps = 1000, k = 2, ...) {mydeviance - function(x, ...) {dev - deviance(x)if (!is.null(dev)) develseextractAIC(x, k = 0)[2L] }cut.string - function(string) {if (length(string) 1L) string[-1L] - paste0(\n, string[-1L])string }re.arrange - function(keep) {namr - names(k1 - keep[[1L]])namc - names(keep)nc - length(keep)nr - length(k1)array(unlist(keep, recursive = FALSE), c(nr, nc), list(namr, namc)) }step.results - function(models, fit, object, usingCp = FALSE) {change - sapply(models, [[, change)rd - sapply(models, [[, deviance)dd - c(NA, abs(diff(rd)))rdf - sapply(models, [[, df.resid)ddf - c(NA, diff(rdf)) AIC - sapply(models, [[, AIC)heading - c(Stepwise Model Path \nAnalysis of Deviance Table, \nInitial Model:,deparse(formula(object)), \nFinal Model:, deparse(formula(fit)), \n)aod - data.frame(Step = I(change), Df = ddf, Deviance = dd, `Resid.Df` = rdf, `Resid. Dev` = rd, AIC = AIC, check.names = FALSE)if (usingCp) {cn - colnames(aod)cn[cn == AIC] - Cpcolnames(aod) - cn }attr(aod, heading) - headingfit$anova - aodfit } Terms - terms(object)object$call$formula - object$formula - Termsmd - missing(direction)direction - match.arg(direction)backward - direction == both | direction == backwardforward - direction == both | direction == forwardif (missing(scope)) {fdrop - numeric()fadd - attr(Terms, factors)if (md) forward - FALSE }else {if (is.list(scope)) {fdrop - if (!is.null(fdrop - scope$lower)) attr(terms(update.formula(object, fdrop)), factors)else numeric()fadd - if (!is.null(fadd - scope$upper)) attr(terms(update.formula(object, fadd)), factors) }else {fadd - if (!is.null(fadd - scope)) attr(terms(update.formula(object, scope)), factors)fdrop - numeric() } }models - vector(list, steps)if (!is.null(keep)) keep.list - vector(list, steps) n - nobs(object, use.fallback = TRUE)fit -