MeanCI: Confidence Interval for the Mean

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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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.

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!)

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):254-74

Wilcox, R. R. (2005) Introduction to robust estimation and hypothesis testing Elsevier Academic Press

See Also

t.test, MeanDiffCI, MedianCI, VarCI

Examples

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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)

Example output

    mean   lwr.ci   upr.ci 
48.03037 37.16348 58.89726 
    mean   lwr.ci   upr.ci 
48.03037 33.14181 62.91892 
  mean lwr.ci upr.ci 
    NA     NA    Inf 

	One Sample t-test

data:  x
t = 9.2858, df = 18, p-value = 1.379e-08
alternative hypothesis: true mean is greater than 0
95 percent confidence interval:
 39.06103      Inf
sample estimates:
mean of x 
 48.03037 

    mean   lwr.ci   upr.ci 
48.03037 36.78920 59.27153 
    mean   lwr.ci   upr.ci 
48.03037 38.39504 58.20331 
    mean   lwr.ci   upr.ci 
47.71441 36.87307 58.31414 
    mean   lwr.ci   upr.ci 
47.71441 36.44935 57.91800 
    mean   lwr.ci   upr.ci 
47.71441 36.83194 58.99912 
    mean   lwr.ci   upr.ci 
47.71441 37.53741 58.37588 
    mean   lwr.ci   upr.ci 
47.71441 36.77244 59.03681 
    mean   lwr.ci   upr.ci 
47.71441 37.45178 58.30295 
             mean    lwr.ci    upr.ci
index     605.000   585.299   624.701
date    16145.260 16144.746 16145.774
week       11.403    11.327    11.479
weekday     4.441     4.325     4.556

DescTools documentation built on June 17, 2021, 5:12 p.m.