MeanDiffCI | R Documentation |
Calculates the confidence interval for the difference of two means either the classical way or with the bootstrap approach.
MeanDiffCI(x, ...)
## Default S3 method:
MeanDiffCI(x, y, method = c("classic", "norm", "basic", "stud", "perc", "bca"),
conf.level = 0.95, sides = c("two.sided", "left", "right"), paired = FALSE,
na.rm = FALSE, R = 999, ...)
## S3 method for class 'formula'
MeanDiffCI(formula, data, subset, na.action, ...)
x |
a (non-empty) numeric vector of data values. |
y |
a (non-empty) numeric vector of data values. |
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 |
paired |
a logical indicating whether you want confidence intervals for a paired design. Defaults to |
na.rm |
logical. Should missing values be removed? Defaults to |
R |
the number of bootstrap replicates. Usually this will be a single positive integer. For importance resampling, some resamples may use one set of weights and others use a different set of weights. In this case R would be a vector of integers where each component gives the number of resamples from each of the rows of weights.
See |
formula |
a formula of the form |
data |
an optional matrix or data frame (or similar: see |
subset |
an optional vector specifying a subset of observations to be used. |
na.action |
a function which indicates what should happen when the data contain |
... |
further argument to be passed to or from methods. |
This function collects code from two sources. The classical confidence interval is calculated by means of t.test
.
The bootstrap intervals are strongly based on the example in boot
.
a numeric vector with 3 elements:
meandiff |
the difference: mean(x) - mean(y) |
lwr.ci |
lower bound of the confidence interval |
upr.ci |
upper bound of the confidence interval |
Andri Signorell <andri@signorell.net>
MeanCI
, VarCI
, MedianCI
,
boot.ci
x <- d.pizza$price[d.pizza$driver=="Carter"]
y <- d.pizza$price[d.pizza$driver=="Miller"]
MeanDiffCI(x, y, na.rm=TRUE)
MeanDiffCI(x, y, conf.level=0.99, na.rm=TRUE)
# the different types of bootstrap confints
MeanDiffCI(x, y, method="norm", na.rm=TRUE)
MeanDiffCI(x, y, method="basic", na.rm=TRUE)
# MeanDiffCI(x, y, method="stud", na.rm=TRUE)
MeanDiffCI(x, y, method="perc", na.rm=TRUE)
MeanDiffCI(x, y, method="bca", na.rm=TRUE)
# the formula interface
MeanDiffCI(price ~ driver, data=d.pizza, subset=driver %in% c("Carter","Miller"))
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