smean.sd.RdA 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.
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)for summary functions smean.*, smedian.hilow, a numeric vector
from which NAs will be removed automatically
defaults to TRUE unlike built-in functions, so that by
default NAs are automatically removed
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.
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.
number of bootstrap resamples for smean.cl.boot
set to TRUE to have smean.cl.boot return the vector of bootstrapped
means as the reps attribute of the returned object
a vector of summary statistics
set.seed(1)
x <- rnorm(100)
smean.sd(x)
#> Mean SD
#> 0.109 0.898
smean.sdl(x)
#> Mean Lower Upper
#> 0.109 -1.688 1.905
smean.cl.normal(x)
#> Mean Lower Upper
#> 0.1089 -0.0693 0.2871
smean.cl.boot(x)
#> Mean Lower Upper
#> 0.1089 -0.0582 0.2770
smedian.hilow(x, conf.int=.5) # 25th and 75th percentiles
#> Median Lower Upper
#> 0.114 -0.494 0.692
# 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
}