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#' Confidence Intervals for Regression models
#'
#' Calculates and prints the confidence intervals for the fitted model.
#'
#'
#' @param fit an object of class{lm}, i.e. the output from \code{\link{lm}}.
#' @param conf.level confidence level of the intervals.
#' @param print.out if \code{TRUE}, print out the output on the screen.
#' @return The function returns a two-column matrix containing the upper and
#' lower endpoints of the intervals.
#' @seealso \code{\link{lm}}, \code{\link{summary}}, \code{\link{anova}}.
#' @keywords htest
#' @examples
#'
#' ##Peruvian Indians data
#' data(peru.df)
#' fit=lm(BP ~ age + years + weight + height, data = peru.df)
#' ciReg(fit)
#'
#' @export ciReg
ciReg = function(fit, conf.level = 0.95, print.out = TRUE) {
if (!inherits(fit, "lm"))
stop("Input is not an \"lm\" object")
if (names(fit$coef[1]) != "(Intercept)")
cat("\n(There is no intercept in this regression model!)\n")
lmsummary = summary(fit)$coefficients
df = fit$df
tt = 1 - (1 - conf.level)/2
C.I.upper = coef(fit) + lmsummary[, 2] * qt(tt, df)
C.I.lower = coef(fit) - lmsummary[, 2] * qt(tt, df)
level = conf.level * 100
ci = cbind(C.I.lower, C.I.upper)
dimnames(ci) = list(names(lmsummary[, 1]), c(paste(level, "% C.I.lower"), paste(" ", level, "% C.I.upper")))
if (print.out) {
print(round(ci, 5))
}
invisible(ci)
}
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