MeanCI.Rd
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.
a (non-empty) numeric vector of data values.
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.
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.
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
.
confidence level of the interval.
a character string specifying the side of the confidence interval, must be one of "two.sided"
(default), "left"
or "right"
. "left"
would be analogue to a hypothesis of "greater"
in a t.test
.
You can specify just the initial letter.
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
.
The confidence intervals for the trimmed means use winsorized variances as described in the references.
a numeric vector with 3 elements:
mean
lower bound of the confidence interval
upper bound of the confidence interval
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
x <- d.pizza$price[1:20]
MeanCI(x, na.rm=TRUE)
#> mean lwr.ci upr.ci
#> 48.03037 37.16348 58.89726
MeanCI(x, conf.level=0.99, na.rm=TRUE)
#> mean lwr.ci upr.ci
#> 48.03037 33.14181 62.91892
MeanCI(x, sides="left")
#> mean lwr.ci upr.ci
#> NA NA Inf
# same as:
t.test(x, alternative="greater")
#>
#> One Sample t-test
#>
#> data: x
#> t = 9.2858, df = 18, p-value = 0.00000001379
#> alternative hypothesis: true mean is greater than 0
#> 95 percent confidence interval:
#> 39.06103 Inf
#> sample estimates:
#> mean of x
#> 48.03037
#>
MeanCI(x, sd=25, na.rm=TRUE)
#> mean lwr.ci upr.ci
#> 48.03037 36.78920 59.27153
# the different types of bootstrap confints
MeanCI(x, method="boot", type="norm", na.rm=TRUE)
#> mean lwr.ci upr.ci
#> 48.03037 38.17212 57.94576
MeanCI(x, trim=0.1, method="boot", type="norm", na.rm=TRUE)
#> mean lwr.ci upr.ci
#> 47.71441 37.21396 58.11883
MeanCI(x, trim=0.1, method="boot", type="basic", na.rm=TRUE)
#> mean lwr.ci upr.ci
#> 47.71441 38.04312 57.84600
MeanCI(x, trim=0.1, method="boot", type="stud", na.rm=TRUE)
#> mean lwr.ci upr.ci
#> 47.71441 37.83765 58.30641
MeanCI(x, trim=0.1, method="boot", type="perc", na.rm=TRUE)
#> mean lwr.ci upr.ci
#> 47.71441 37.22735 58.51682
MeanCI(x, trim=0.1, method="boot", type="bca", na.rm=TRUE)
#> mean lwr.ci upr.ci
#> 47.71441 36.97180 58.87290
MeanCI(x, trim=0.1, method="boot", type="bca", R=1999, na.rm=TRUE)
#> mean lwr.ci upr.ci
#> 47.71441 37.34747 58.90979
# Getting the MeanCI for more than 1 column
round(t(sapply(d.pizza[, 1:4], MeanCI, na.rm=TRUE)), 3)
#> 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