DescTools (version 0.99.19)

MeanCI: Confidence Interval 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, method = c("classic", "boot"), conf.level = 0.95, sides = c("two.sided", "left", "right"), na.rm = FALSE, ...)

Arguments

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 NULL (default) the sample sd(x) will be calculated and used in combination with the t-distribution.

trim

the fraction (0 to 0.5) of observations to be trimmed from each end of x before the mean is computed. Values of trim outside that range are taken as the nearest endpoint.

method

A vector of character strings representing the type of intervals required. The value should be any subset of the values "classic", "boot". See boot.ci.

conf.level

confidence level of the interval.

sides

a character string specifying the side of the confidence interval, must be one of "two.sided" (default), "left" or "right". You can specify just the initial letter. "left" would be analogue to a hypothesis of "greater" in a t.test.

na.rm

a logical value indicating whether NA values should be stripped before the computation proceeds. Defaults to FALSE.

...

further arguments are passed to the boot function. Supported arguments are type ("norm", "basic", "stud", "perc", "bca"), parallel and the number of bootstrap replicates R. If not defined those will be set to their defaults, being "basic" for type, option "boot.parallel" (and if that is not set, "no") for parallel and 999 for R.

Value

Details

The confidence intervals for the trimmed means use winsorized variances as described in the references.

Use do.call, rbind and lapply for getting a matrix with estimates and confidence intervals for more than 1 column. (See examples!)

References

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

See Also

t.test, MeanDiffCI, MedianCI, VarCI

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( do.call("rbind", lapply(d.pizza[, 1:4],  MeanCI, na.rm=TRUE)), 3)