MeanCI {DescTools} | R Documentation |
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.
MeanCI(x, sd = NULL, trim = 0, method = c("classic", "boot"),
conf.level = 0.95, sides = c("two.sided", "left", "right"),
na.rm = FALSE, ...)
x |
a (non-empty) 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 |
method |
A vector of character strings representing the type of intervals required. The value should be any subset of the values |
conf.level |
confidence level of the interval. |
sides |
a character string specifying the side of the confidence interval, must be one of |
na.rm |
a logical value indicating whether |
... |
further arguments are passed to the |
The confidence intervals for the trimmed means use winsorized variances as described in the references.
a numeric vector with 3 elements:
mean |
mean |
lwr.ci |
lower bound of the confidence interval |
upr.ci |
upper bound of the confidence interval |
Andri Signorell <andri@signorell.net>
Wilcox, R. R., Keselman H. J. (2003) Modern robust data analysis methods: measures of central tendency Psychol Methods, 8(3):254-74
Wilcox, R. R. (2005) Introduction to robust estimation and hypothesis testing Elsevier Academic Press
t.test
, MeanDiffCI
, MedianCI
, VarCI
, MeanCIn
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)