# smean.sd

##### Compute Summary Statistics on a Vector

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.

- Keywords
- htest, nonparametric

##### 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 functions, so that by default`NA`

s 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 the multiplier of the standard deviation used in obtaining a coverage interval about the sample mean. The default is`mult=2`

to use plus or minus 2 standard deviations.- 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 should target. When the default, 0.95, is used, the lower and upper quantiles computed are 0.025 and 0.975.- 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

##### Examples

```
# NOT RUN {
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
}
# }
```

*Documentation reproduced from package Hmisc, version 4.3-1, License: GPL (>= 2)*