## File: smean.sd.Rd

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hmisc 4.2-0-1
 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109 \name{smean.sd} \alias{smean.cl.normal} \alias{smean.sd} \alias{smean.sdl} \alias{smean.cl.boot} \alias{smedian.hilow} \title{ Compute Summary Statistics on a Vector } \description{ A number of statistical summary functions is provided for use with \code{summary.formula} and \code{summarize} (as well as \code{tapply} and by themselves). \code{smean.cl.normal} computes 3 summary variables: the sample mean and lower and upper Gaussian confidence limits based on the t-distribution. \code{smean.sd} computes the mean and standard deviation. \code{smean.sdl} computes the mean plus or minus a constant times the standard deviation. \code{smean.cl.boot} is a very fast implementation of the basic nonparametric bootstrap for obtaining confidence limits for the population mean without assuming normality. These functions all delete NAs automatically. \code{smedian.hilow} computes the sample median and a selected pair of outer quantiles having equal tail areas. } \usage{ smean.cl.normal(x, mult=qt((1+conf.int)/2,n-1), conf.int=.95, na.rm=TRUE) smean.sd(x, na.rm=TRUE) smean.sdl(x, mult=2, na.rm=TRUE) smean.cl.boot(x, conf.int=.95, B=1000, na.rm=TRUE, reps=FALSE) smedian.hilow(x, conf.int=.95, na.rm=TRUE) } \arguments{ \item{x}{ for summary functions \code{smean.*}, \code{smedian.hilow}, a numeric vector from which NAs will be removed automatically } \item{na.rm}{ defaults to \code{TRUE} unlike built-in functions, so that by default \code{NA}s are automatically removed } \item{mult}{ for \code{smean.cl.normal} is the multiplier of the standard error of the mean to use in obtaining confidence limits of the population mean (default is appropriate quantile of the t distribution). For \code{smean.sdl}, \code{mult} is the multiplier of the standard deviation used in obtaining a coverage interval about the sample mean. The default is \code{mult=2} to use plus or minus 2 standard deviations. } \item{conf.int}{ for \code{smean.cl.normal} and \code{smean.cl.boot} specifies the confidence level (0-1) for interval estimation of the population mean. For \code{smedian.hilow}, \code{conf.int} is the coverage probability the outer quantiles should target. When the default, 0.95, is used, the lower and upper quantiles computed are 0.025 and 0.975. } \item{B}{ number of bootstrap resamples for \code{smean.cl.boot} } \item{reps}{ set to \code{TRUE} to have \code{smean.cl.boot} return the vector of bootstrapped means as the \code{reps} attribute of the returned object } } \value{ a vector of summary statistics } \author{ Frank Harrell \cr Department of Biostatistics \cr Vanderbilt University \cr \email{f.harrell@vanderbilt.edu} } \seealso{ \code{\link{summarize}}, \code{\link{summary.formula}} } \examples{ set.seed(1) x <- rnorm(100) smean.sd(x) smean.sdl(x) smean.cl.normal(x) smean.cl.boot(x) smedian.hilow(x, conf.int=.5) # 25th and 75th percentiles # Function to compute 0.95 confidence interval for the difference in two means # g is grouping variable bootdif <- function(y, g) { g <- as.factor(g) a <- attr(smean.cl.boot(y[g==levels(g)[1]], B=2000, reps=TRUE),'reps') b <- attr(smean.cl.boot(y[g==levels(g)[2]], B=2000, reps=TRUE),'reps') meandif <- diff(tapply(y, g, mean, na.rm=TRUE)) a.b <- quantile(b-a, c(.025,.975)) res <- c(meandif, a.b) names(res) <- c('Mean Difference','.025','.975') res } } \keyword{nonparametric} \keyword{htest} \concept{bootstrap}