MeanCI {DescTools}  R Documentation 
Confidence Intervals for the Mean
Description
Collection of several approaches to determine confidence intervals for the mean. Both, the classical way and bootstrap intervals are implemented for both, normal and trimmed means.
Usage
MeanCI(
x,
sd = NULL,
trim = 0,
conf.level = 0.95,
sides = c("two.sided", "left", "right"),
method = c("classic", "boot"),
na.rm = FALSE,
...
)
Arguments
x 
a (nonempty) numeric vector of data values. 
sd 
the standard deviation of x. If provided it's interpreted as sd of
the population and the normal quantiles will be used for constructing the
confidence intervals. If left to 
trim 
the fraction (0 to 0.5) of observations to be trimmed from each
end of 
conf.level 
confidence level of the interval. 
sides 
a character string specifying the side of the confidence
interval, must be one of 
method 
A vector of character strings representing the type of
intervals required. The value should be any subset of the values

na.rm 
a logical value indicating whether 
... 
further arguments are passed to the 
Details
The confidence intervals for the trimmed means use winsorized variances as described in the references.
Value
a numeric vector with 3 elements:
mean 
mean 
lwr.ci 
lower bound of the confidence interval 
upr.ci 
upper bound of the confidence interval 
Author(s)
Andri Signorell andri@signorell.net
References
Wilcox, R. R., Keselman H. J. (2003) Modern robust data analysis methods: measures of central tendency Psychol Methods, 8(3):25474
Wilcox, R. R. (2005) Introduction to robust estimation and hypothesis testing Elsevier Academic Press
See Also
Mean
, t.test
, MeanDiffCI
,
MedianCI
, VarCI
, MeanCIn
Examples
x < d.pizza$price[1:20]
MeanCI(x, na.rm=TRUE)
MeanCI(x, conf.level=0.99, na.rm=TRUE)
MeanCI(x, sides="left")
# same as:
t.test(x, alternative="greater")
MeanCI(x, sd=25, na.rm=TRUE)
# the different types of bootstrap confints
MeanCI(x, method="boot", type="norm", na.rm=TRUE)
MeanCI(x, trim=0.1, method="boot", type="norm", na.rm=TRUE)
MeanCI(x, trim=0.1, method="boot", type="basic", na.rm=TRUE)
MeanCI(x, trim=0.1, method="boot", type="stud", na.rm=TRUE)
MeanCI(x, trim=0.1, method="boot", type="perc", na.rm=TRUE)
MeanCI(x, trim=0.1, method="boot", type="bca", na.rm=TRUE)
MeanCI(x, trim=0.1, method="boot", type="bca", R=1999, na.rm=TRUE)
# Getting the MeanCI for more than 1 column
round(t(sapply(d.pizza[, 1:4], MeanCI, na.rm=TRUE)), 3)