Hmisc (version 2.0-3)

smean.sd: Compute Summary Statistics on a Vector

Description

A number of statistical summary functions is provided for use with summary.formula and summarize (as well as tapply and by themselves). smean.cl.normal computes 3 summary variables: the sample mean and lower and upper Gaussian confidence limits based on the t-distribution. smean.sd computes the mean and standard deviation. smean.sdl computes the mean plus or minus a constant times the standard deviation. 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. 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

x

for summary functions smean.*, smedian.hilow, a numeric vector from which NAs will be removed automatically

na.rm

defaults to TRUE unlike built-in S-Plus functions, so that by default NAs are automatically removed

mult

for 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 smean.sdl, mult is th

conf.int

for smean.cl.normal and smean.cl.boot specifies the confidence level (0-1) for interval estimation of the population mean. For smedian.hilow, conf.int is the coverage probability the outer quantiles sho

B

number of bootstrap resamples for smean.cl.boot

reps

set to TRUE to have smean.cl.boot return the vector of bootstrapped means as the reps attribute of the returned object

Value

• a vector of summary statistics

See Also

summarize, 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

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