MeanDiffCI {DescTools}  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 (nonempty) numeric vector of data values. 
y 
a (nonempty) 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"))