ci.mean: (Bootstrap) Confidence Intervals for Arithmetic Means and...
In misty: Miscellaneous Functions 'T. Yanagida'
ci.mean R Documentation
(Bootstrap) Confidence Intervals for Arithmetic Means and Medians
Description
The function ci.mean computes and plots confidence intervals for
arithmetic means with known or unknown population standard deviation or
population variance and the function ci.median computes confidence
intervals for medians, optionally by a grouping and/or split variable. These
functions also supports six types of bootstrap confidence intervals (e.g.,
bias-corrected (BC) percentile bootstrap or bias-corrected and accelerated
(BCa) bootstrap confidence intervals) and plots the bootstrap samples with
histograms and density curves.
Usage
ci.mean(data, ..., sigma = NULL, sigma2 = NULL, adjust = FALSE,
boot = c("none", "norm", "basic", "stud", "perc", "bc", "bca"),
R = 1000, seed = NULL, sample = TRUE,
alternative = c("two.sided", "less", "greater"),
conf.level = 0.95, group = NULL, split = NULL, sort.var = FALSE,
na.omit = FALSE, digits = 2, as.na = NULL,
plot = c("none", "ci", "boot"), point.size = 2.5, point.shape = 19,
errorbar.width = 0.3, dodge.width = 0.5, hist = TRUE,
binwidth = NULL, bins = NULL, hist.alpha = 0.4, fill = "gray85", density = TRUE,
density.col = "#0072B2", density.linewidth = 0.5, density.linetype = "solid",
point = TRUE, point.col = "#CC79A7", point.linewidth = 0.6,
point.linetype = "solid", ci = TRUE, ci.col = "black",
ci.linewidth = 0.6, ci.linetype = "dashed", line = FALSE, intercept = 0,
linetype = "solid", line.col = "gray65", xlab = NULL, ylab = NULL,
xlim = NULL, ylim = NULL, xbreaks = ggplot2::waiver(),
ybreaks = ggplot2::waiver(), axis.title.size = 11, axis.text.size = 10,
strip.text.size = 11, title = NULL, subtitle = NULL, group.col = NULL,
plot.margin = NA, legend.title = "",
legend.position = c("right", "top", "left", "bottom", "none"),
legend.box.margin = c(-10, 0, 0, 0), facet.ncol = NULL, facet.nrow = NULL,
facet.scales = "free", filename = NULL, width = NA, height = NA,
units = c("in", "cm", "mm", "px"), dpi = 600, write = NULL,
append = TRUE, check = TRUE, output = TRUE)
ci.median(data, ..., boot = c("none", "norm", "basic", "stud", "perc", "bc", "bca"),
R = 1000, seed = NULL, sample = TRUE,
alternative = c("two.sided", "less", "greater"),
conf.level = 0.95, group = NULL, split = NULL, sort.var = FALSE,
na.omit = FALSE, digits = 2, as.na = NULL, plot = c("none", "ci", "boot"),
point.size = 2.5, point.shape = 19, errorbar.width = 0.3, dodge.width = 0.5,
hist = TRUE, binwidth = NULL, bins = NULL, hist.alpha = 0.4, fill = "gray85",
density = TRUE, density.col = "#0072B2", density.linewidth = 0.5,
density.linetype = "solid", point = TRUE, point.col = "#CC79A7",
point.linewidth = 0.6, point.linetype = "solid", ci = TRUE, ci.col = "black",
ci.linewidth = 0.6, ci.linetype = "dashed", line = FALSE, intercept = 0,
linetype = "solid", line.col = "gray65", xlab = NULL, ylab = NULL,
xlim = NULL, ylim = NULL, xbreaks = ggplot2::waiver(),
ybreaks = ggplot2::waiver(), axis.title.size = 11, axis.text.size = 10,
strip.text.size = 11, title = NULL, subtitle = NULL, group.col = NULL,
plot.margin = NA, legend.title = "",
legend.position = c("right", "top", "left", "bottom", "none"),
legend.box.margin = c(-10, 0, 0, 0), facet.ncol = NULL, facet.nrow = NULL,
facet.scales = "free", filename = NULL, width = NA, height = NA,
units = c("in", "cm", "mm", "px"), dpi = 600, write = NULL, append = TRUE,
check = TRUE, output = TRUE)
Arguments
data
a numeric vector or data frame with numeric
variables, i.e., factors and character variables are
excluded from data before conducting the
analysis.
...
an expression indicating the variable names in data
e.g., ci.mean(x1, x2, data = dat). Note that the
operators ., +, -, ~,
:, ::, and ! can also be used
to select variables, see 'Details' in the
df.subset function.
sigma
a numeric vector indicating the population standard
deviation when computing confidence intervals for the
arithmetic mean with known standard deviation Note
that either argument sigma or argument
sigma2 is specified and it is only possible to
specify one value for the argument sigma even
though multiple variables are specified in data.
sigma2
a numeric vector indicating the population variance
when computing confidence intervals for the arithmetic
mean with known variance. Note that either argument
sigma or argument sigma2 is specified
and it is only possible to specify one value for the
argument sigma2 even though multiple variables
are specified in data.
adjust
logical: if TRUE, difference-adjustment for
the confidence intervals for the arithmetic mean
(Baguley, 2012) is applied.
boot
a character string specifying the type of bootstrap
confidence intervals (CI), i.e., "none"
(default) for not conducting bootstrapping, "norm"
for the bias-corrected normal approximation bootstrap CI,
"basic" for the basic bootstrap CI, "stud"
for the studentized bootstrap CI, "perc", for
the percentile bootstrap CI "bc" for the bias-corrected
(BC) percentile bootstrap CI (without acceleration),
and "bca" for the bias-corrected and accelerated
(BCa) bootstrap CI, see 'Details' in the
ci.cor function.
R
a numeric value indicating the number of bootstrap
replicates (default is 1000).
seed
a numeric value specifying seeds of the pseudo-random
numbers used in the bootstrap algorithm when conducting
bootstrapping.
sample
logical: if TRUE (default), the univariate
sample skewness and kurtosis is computed, while the
population skewness and kurtosis is computed when
sample = FALSE.
alternative
a character string specifying the alternative hypothesis,
must be one of "two.sided" (default),
"greater" or "less".
conf.level
a numeric value between 0 and 1 indicating the confidence
level of the interval.
group
either a character string indicating the variable name
of the grouping variable in data,
or a vector representing the grouping variable. The grouping variable
is excluded from the data frame specified
in data. Notethat a grouping variable can only be used when computing
confidence intervals with unknown population standard
deviation and population variance.
split
either a character string indicating the variable name
of the split variable in data,
or a vector representing the split variable. The split variable
is excluded from the data frame specified
in data.Note that a grouping variable can only be used when computing
confidence intervals with unknown population standard
deviation and population variance.
sort.var
logical: if TRUE, output table is sorted by
variables when specifying group.
na.omit
logical: if TRUE, incomplete cases are removed
before conducting the analysis (i.e., listwise deletion)
when specifying more than one outcome variable.
digits
an integer value indicating the number of decimal
places to be used.
as.na
a numeric vector indicating user-defined missing
values, i.e. these values are converted to NA
before conducting the analysis. Note that as.na()
function is only applied to data, but not to
group or split.
plot
a character string indicating the type of the plot
to display, i.e., "none" (default) for not
displaying any plots, "ci" for displaying
confidence intervals for the arithmetic mean or median,
"boot" for displaying bootstrap samples with
histograms and density curves when the argument
"boot" is other than "none".
point.size
a numeric value indicating the size argument
in the geom_point function for controlling the
size of points when plotting confidence intervals
(plot = "ci").
point.shape
a numeric value between 0 and 25 or a character string
as plotting symbol indicating the shape argument
in the geom_point function for controlling the
symbols of points when plotting confidence intervals
(plot = "ci").
errorbar.width
a numeric value indicating the width argument
in the geom_errorbar function for controlling
the width of the whiskers in the geom_errorbar
function when plotting confidence intervals
(plot = "ci").
dodge.width
a numeric value indicating the width argument
controlling the width of the geom elements to
be dodged when specifying a grouping variable using
the argument group and plotting confidence
intervals (plot = "ci").
hist
logical: if TRUE (default), histograms are
drawn when plotting bootstrap samples
(plot = "boot").
binwidth
a numeric value or a function for specifying the
binwidth argument in the geom_histogram
function for controlling the width of the bins when
plotting bootstrap samples (plot = "boot").
bins
a numeric value for specifying the bins argument
in the geom_histogram function for controlling
the number of bins when plotting bootstrap samples
(plot = "boot").
hist.alpha
a numeric value between 0 and 1 for specifying the
alpha argument in the geom_histogram
function for controlling the opacity of the bars
when plotting bootstrap samples (plot = "boot").
fill
a character string specifying the fill argument
in the geom_histogram function controlling the
fill aesthetic when plotting bootstrap samples
(plot = "boot"). Note that this argument applied
only when no grouping variable was specified
group = NULL.
density
logical: if TRUE (default), density curves are
drawn when plotting bootstrap samples (plot = "boot").
density.col
a character string specifying the color argument
in the geom_density function controlling the
color of the density curves when plotting bootstrap
samples (plot = "boot"). Note that this argument
applied only when no grouping variable was specified
group = NULL.
density.linewidth
a numeric value specifying the linewidth
argument in the geom_density function controlling
the line width of the density curves when plotting
bootstrap samples (plot = "boot").
density.linetype
a numeric value or character string specifying the
linetype argument in the geom_density
function controlling the line type of the density
curves when plotting bootstrap samples
(plot = "boot").
point
logical: if TRUE (default), vertical lines
representing the point estimate of the arithmetic
mean or median are drawn when plotting bootstrap samples
(plot = "boot").
point.col
a character string specifying the color argument
in the geom_vline function for controlling the
color of the vertical line displaying the arithmetic
mean or median when plotting bootstrap samples
(plot = "boot"). Note that this argument
applied only when no grouping variable was specified
group = NULL.
point.linewidth
a numeric value specifying the linewdith
argument in the geom_vline function for
controlling the line width of the vertical line
displaying the arithmetic mean or median when plotting
bootstrap samples (plot = "boot").
point.linetype
a numeric value or character string specifying the
linetype argument in the geom_vline
function controlling the line type of the vertical
line displaying the arithmetic mean or median when
plotting bootstrap samples (plot = "boot").
ci
logical: if TRUE (default), vertical lines
representing the bootstrap confidence intervals of
the arithmetic mean or median are drawn when
plotting bootstrap samples (plot = "boot").
ci.col
character string specifying the color argument
in the geom_vline function for controlling the
color of the vertical line displaying bootstrap
confidence intervals when plotting bootstrap samples
(plot = "boot"). Note that this argument applied
only when no grouping variable was specified
group = NULL.
ci.linewidth
a numeric value specifying the linewdith argument
in the geom_vline function for controlling the
line width of the vertical line displaying bootstrap
confidence intervals when plotting bootstrap samples
(plot = "boot").
ci.linetype
a numeric value or character string specifying the
linetype argument in the geom_vline
function controlling the line type of the vertical
line displaying bootstrap confidence intervals when
plotting bootstrap samples (plot = "boot").
line
logical: if TRUE, a horizontal line
is drawn when plot = "ci" or a vertical line
is drawn when plot = "boot"
intercept
a numeric value indicating the yintercept or
xintercept argument in the geom_hline
or geom_vline function controlling the position
of the horizontal or vertical line when plot = "ci"
and line = TRUE or when plot = "boot"
and line = TRUE. By default, the horizontal or
vertical line is drawn at 0.
linetype
a character string indicating the linetype
argument in the geom_hline or geom_vline
function controlling the line type of the horizontal
or vertical line (default is linetype = "dashed").
line.col
a character string indicating the color argument
in the geom_hline or geom_vline function
for controlling the color of the horizontal or vertical
line.
xlab
a character string indicating the name argument
in the scale_x_continuous function for labeling
the x-axis. The default setting is xlab = NULL
when plot = "ci" and xlab = "Arithmetic Mean"
or xlab = "Median" when plot = "boot".
ylab
a character string indicating the name argument
in the scale_y_continuous function for labeling
the y-axis. The default setting is ylab = "Arithmetic Mean"
or ylab = "Median" when plot = "ci" and
ylab = "Probability Density, f(x)" when plot = "boot".
xlim
a numeric vector with two elements indicating the
limits argument in the scale_x_continuous
function for controlling the scale range of the x-axis.
ylim
a numeric vector with two elements indicating the
limits argument in the scale_y_continuous
function for controlling the scale range of the y-axis.
xbreaks
a numeric vector indicating the breaks argument
in the scale_x_continuous function for controlling
the x-axis breaks. The default setting is
xbreaks = NULL when plot = "ci"
and xbreaks = seq(-1, 1, by = 0.25) when
plot = "boot".
ybreaks
a numeric vector indicating the breaks argument
in the scale_y_continuous function for controlling
the y-axis breaks. The default setting is
ybreaks = seq(-1, 1, by = 0.25) when
plot = "ci" and ybreaks = NULL when
plot = "boot".
axis.title.size
a numeric value indicating the size argument
in the element_text function for specifying the
function controlling the font size of the axis title,
i.e., theme(axis.title = element_text(size = axis.text.size)).
axis.text.size
a numeric value indicating the size argument
in the element_text function for specifying the
function controlling the font size of the axis text,
i.e., theme(axis.text = element_text(size = axis.text.size)).
strip.text.size
a numeric value indicating the size argument
in the element_text function for specifying the
function controlling the font size of the strip text,
i.e., theme(strip.text = element_text(size = strip.text.size)).
title
a character string indicating the title argument
in the labs function for the subtitle of the plot.
subtitle
a character string indicating the subtite argument
in the labs function for the subtitle of the plot.
group.col
a character vector indicating the color argument
in the scale_color_manual and scale_fill_manual
functions when specifying a grouping variable using
the argument group.
plot.margin
a numeric vector with four elements indicating the
plot.margin argument in the theme function
controlling the plot margins . The default setting
is c(5.5, 5.5, 5.5, 5.5), but switches
to c(5.5, 5.5, -2.5, 5.5) when specifying a
grouping variable using the argument group.
legend.title
a character string indicating the color argument
in the labs function for specifying the legend
title when specifying a grouping variable using the
argument group.
legend.position
a character string indicating the legend.position
in the theme argument for controlling the
position of the legend function when specifying a
grouping variable using the argument group.
By default, the legend is placed at the bottom the
plot.
legend.box.margin
a numeric vector with four elements indicating the
legend.box.margin argument in the theme
function for controlling the margins around the full
legend area when specifying a grouping variable using
the argument group.
facet.ncol
a numeric value indicating the ncol argument
in the facet_wrap function for controlling
the number of columns when specifying a split variable
using the argument split.
facet.nrow
a numeric value indicating the nrow argument
in the facet_wrap function for controlling the
number of rows when specifying a split variable using
the argument split.
facet.scales
a character string indicating the scales argument
in the facet_wrap function for controlling the
scales shared across facets, i.e., "fixed",
"free_x", "free_y", or "free"
(default) when specifying a split variable using
the argument split.
filename
a character string indicating the filename
argument including the file extension in the ggsave
function. Note that one of ".eps", ".ps",
".tex", ".pdf" (default),
".jpeg", ".tiff", ".png",
".bmp", ".svg" or ".wmf" needs
to be specified as file extension in the file
argument. Note that plots can only be saved when
plot = "ci" or plot = "boot".
width
a numeric value indicating the width argument
(default is the size of the current graphics device)
in the ggsave function.
height
a numeric value indicating the height argument
(default is the size of the current graphics device)
in the ggsave function.
units
a character string indicating the units argument
(default is in) in the ggsave function.
dpi
a numeric value indicating the dpi argument
(default is 600) in the ggsave function.
write
a character string naming a file for writing the output
into either a text file with file extension ".txt"
(e.g., "Output.txt") or Excel file with file
extension ".xlsx" (e.g., "Output.xlsx").
If the file name does not contain any file extension,
an Excel file will be written.
append
logical: if TRUE (default), output will be
appended to an existing text file with extension
.txt specified in write, if FALSE
existing text file will be overwritten.
check
logical: if TRUE (default), argument specification
is checked.
output
logical: if TRUE (default), output is shown
on the console.
Value
Returns an object of class misty.object, which is a list with following
entries:
call
function call
type
type of analysis
data
list with the input specified in data, group, and split
args
specification of function arguments
boot
data frame with bootstrap replicates of the arithmetic mean of median when bootstrapping was requested
plot
ggplot2 object for plotting the results and the data frame used for plotting
result
result table
Note
Bootstrap confidence intervals are computed using the R package boot
by Angelo Canty and Brain Ripley (2024).
Author(s)
Takuya Yanagida takuya.yanagida@univie.ac.at
References
Baguley, T. S. (2012). Serious stats: A guide to advanced statistics for
the behavioral sciences. Palgrave Macmillan.
Canty, A., & Ripley, B. (2024). boot: Bootstrap R (S-Plus) Functions.
R package version 1.3-31.
Rasch, D., Kubinger, K. D., & Yanagida, T. (2011). Statistics in psychology
- Using R and SPSS. John Wiley & Sons.
See Also
test.z, test.t, ci.mean.diff,
ci.cor, ci.prop, ci.var,
ci.sd, descript
Examples
#----------------------------------------------------------------------------
# Confidence Interval (CI) for the Arithmetic Mean
# Example 1a: Two-Sided 95% CI
ci.mean(mtcars)
# Example 1b: Two-Sided 95% Difference-Adjusted CI
ci.mean(mtcars, adjust = TRUE)
# Example 1c: Two-Sided 95% CI with known population standard deviation
ci.mean(mtcars, mpg, sigma = 6)
# Alternative specification without using the '...' argument
ci.mean(mtcars$mpg, sigma = 6)
#----------------------------------------------------------------------------
# Confidence Interval (CI) for the Median
# Example 2a: Two-Sided 95% CI
ci.median(mtcars)
# Example 2b: One-Sided 99% CI
ci.median(mtcars, alternative = "less", conf.level = 0.99)
## Not run:
#----------------------------------------------------------------------------
# Bootstrap Confidence Interval (CI)
# Example 3a: Bias-corrected (BC) percentile bootstrap CI
ci.mean(mtcars, boot = "bc")
# Example 3b: Bias-corrected and accelerated (BCa) bootstrap CI,
# 5000 bootstrap replications, set seed of the pseudo-random number generator
ci.mean(mtcars, boot = "bca", R = 5000, seed = 123)
#----------------------------------------------------------------------------
# Grouping and Split Variable
# Example 4a: Grouping variable
ci.mean(mtcars, mpg, cyl, disp, group = "vs")
# Alternative specification without using the '...' argument
ci.mean(mtcars[, c("mpg", "cyl", "disp")], group = mtcars$vs)
# Example 4b: Split variable
ci.mean(mtcars, mpg, cyl, disp, split = "am")
# Alternative specification
ci.mean(mtcars[, c("mpg", "cyl", "disp")], split = mtcars$am)
# Example 4c: Grouping and split variable
ci.mean(mtcars, mpg, cyl, disp, group = "vs", split = "am")
# Alternative specification
ci.mean(mtcars[, c("mpg", "cyl", "disp")], group = mtcars$vs, split = mtcars$am)
#----------------------------------------------------------------------------
# Write Output
# Example 5a: Text file
ci.mean(mtcars, write = "CI_Mean_Text.txt")
# Example 5b: Excel file
ci.mean(mtcars, write = "CI_Mean_Excel.xlsx")
#----------------------------------------------------------------------------
# Plot Confidence Intervals
# Example 6a: Two-Sided 95
ci.mean(mtcars, disp, hp, plot = "ci")
# Example 6b: Grouping variable
ci.mean(mtcars, disp, hp, group = "vs", plot = "ci")
# Example 6c: Split variable
ci.mean(mtcars, disp, hp, split = "am", plot = "ci")
# Example 6d: Save plot as PDF file
ci.mean(mtcars, disp, hp, plot = "ci", saveplot = "CI_Mean.pdf",
width = 9, height = 6)
# Example 6e: Save plot as PNG file
ci.mean(mtcars, disp, hp, plot = "ci", saveplot = "CI_Mean.png",
width = 9, height = 6)
#----------------------------------------------------------------------------
# Example 7: Plot Bootstrap Samples
# Example 7a: Two-Sided 95
ci.mean(mtcars, disp, hp, boot = "bc", plot = "boot")
# Example 7b: Grouping variable
ci.mean(mtcars, disp, hp, group = "vs", boot = "bc", plot = "boot")
# Example 7c: Split variable
ci.mean(mtcars, disp, hp, split = "am", boot = "bc", plot = "boot")
# Example 7d: Save plot as PDF file
ci.mean(mtcars, disp, hp, boot = "bc", plot = "boot", saveplot = "CI_Mean_Boot.pdf",
width = 12, height = 7)
# Example 7e: Save plot as PNG file
ci.mean(mtcars, disp, hp, boot = "bc", plot = "boot", saveplot = "CI_Mean_Boot.png",
width = 12, height = 7)
## End(Not run)
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| ci.mean | R Documentation |
(Bootstrap) Confidence Intervals for Arithmetic Means and Medians
Description
The function ci.mean computes and plots confidence intervals for
arithmetic means with known or unknown population standard deviation or
population variance and the function ci.median computes confidence
intervals for medians, optionally by a grouping and/or split variable. These
functions also supports six types of bootstrap confidence intervals (e.g.,
bias-corrected (BC) percentile bootstrap or bias-corrected and accelerated
(BCa) bootstrap confidence intervals) and plots the bootstrap samples with
histograms and density curves.
Usage
ci.mean(data, ..., sigma = NULL, sigma2 = NULL, adjust = FALSE,
boot = c("none", "norm", "basic", "stud", "perc", "bc", "bca"),
R = 1000, seed = NULL, sample = TRUE,
alternative = c("two.sided", "less", "greater"),
conf.level = 0.95, group = NULL, split = NULL, sort.var = FALSE,
na.omit = FALSE, digits = 2, as.na = NULL,
plot = c("none", "ci", "boot"), point.size = 2.5, point.shape = 19,
errorbar.width = 0.3, dodge.width = 0.5, hist = TRUE,
binwidth = NULL, bins = NULL, hist.alpha = 0.4, fill = "gray85", density = TRUE,
density.col = "#0072B2", density.linewidth = 0.5, density.linetype = "solid",
point = TRUE, point.col = "#CC79A7", point.linewidth = 0.6,
point.linetype = "solid", ci = TRUE, ci.col = "black",
ci.linewidth = 0.6, ci.linetype = "dashed", line = FALSE, intercept = 0,
linetype = "solid", line.col = "gray65", xlab = NULL, ylab = NULL,
xlim = NULL, ylim = NULL, xbreaks = ggplot2::waiver(),
ybreaks = ggplot2::waiver(), axis.title.size = 11, axis.text.size = 10,
strip.text.size = 11, title = NULL, subtitle = NULL, group.col = NULL,
plot.margin = NA, legend.title = "",
legend.position = c("right", "top", "left", "bottom", "none"),
legend.box.margin = c(-10, 0, 0, 0), facet.ncol = NULL, facet.nrow = NULL,
facet.scales = "free", filename = NULL, width = NA, height = NA,
units = c("in", "cm", "mm", "px"), dpi = 600, write = NULL,
append = TRUE, check = TRUE, output = TRUE)
ci.median(data, ..., boot = c("none", "norm", "basic", "stud", "perc", "bc", "bca"),
R = 1000, seed = NULL, sample = TRUE,
alternative = c("two.sided", "less", "greater"),
conf.level = 0.95, group = NULL, split = NULL, sort.var = FALSE,
na.omit = FALSE, digits = 2, as.na = NULL, plot = c("none", "ci", "boot"),
point.size = 2.5, point.shape = 19, errorbar.width = 0.3, dodge.width = 0.5,
hist = TRUE, binwidth = NULL, bins = NULL, hist.alpha = 0.4, fill = "gray85",
density = TRUE, density.col = "#0072B2", density.linewidth = 0.5,
density.linetype = "solid", point = TRUE, point.col = "#CC79A7",
point.linewidth = 0.6, point.linetype = "solid", ci = TRUE, ci.col = "black",
ci.linewidth = 0.6, ci.linetype = "dashed", line = FALSE, intercept = 0,
linetype = "solid", line.col = "gray65", xlab = NULL, ylab = NULL,
xlim = NULL, ylim = NULL, xbreaks = ggplot2::waiver(),
ybreaks = ggplot2::waiver(), axis.title.size = 11, axis.text.size = 10,
strip.text.size = 11, title = NULL, subtitle = NULL, group.col = NULL,
plot.margin = NA, legend.title = "",
legend.position = c("right", "top", "left", "bottom", "none"),
legend.box.margin = c(-10, 0, 0, 0), facet.ncol = NULL, facet.nrow = NULL,
facet.scales = "free", filename = NULL, width = NA, height = NA,
units = c("in", "cm", "mm", "px"), dpi = 600, write = NULL, append = TRUE,
check = TRUE, output = TRUE)
Arguments
data |
a numeric vector or data frame with numeric
variables, i.e., factors and character variables are
excluded from |
... |
an expression indicating the variable names in |
sigma |
a numeric vector indicating the population standard
deviation when computing confidence intervals for the
arithmetic mean with known standard deviation Note
that either argument |
sigma2 |
a numeric vector indicating the population variance
when computing confidence intervals for the arithmetic
mean with known variance. Note that either argument
|
adjust |
logical: if |
boot |
a character string specifying the type of bootstrap
confidence intervals (CI), i.e., |
R |
a numeric value indicating the number of bootstrap replicates (default is 1000). |
seed |
a numeric value specifying seeds of the pseudo-random numbers used in the bootstrap algorithm when conducting bootstrapping. |
sample |
logical: if |
alternative |
a character string specifying the alternative hypothesis,
must be one of |
conf.level |
a numeric value between 0 and 1 indicating the confidence level of the interval. |
group |
either a character string indicating the variable name
of the grouping variable in |
split |
either a character string indicating the variable name
of the split variable in |
sort.var |
logical: if |
na.omit |
logical: if |
digits |
an integer value indicating the number of decimal places to be used. |
as.na |
a numeric vector indicating user-defined missing
values, i.e. these values are converted to |
plot |
a character string indicating the type of the plot
to display, i.e., |
point.size |
a numeric value indicating the |
point.shape |
a numeric value between 0 and 25 or a character string
as plotting symbol indicating the |
errorbar.width |
a numeric value indicating the |
dodge.width |
a numeric value indicating the |
hist |
logical: if |
binwidth |
a numeric value or a function for specifying the
|
bins |
a numeric value for specifying the |
hist.alpha |
a numeric value between 0 and 1 for specifying the
|
fill |
a character string specifying the |
density |
logical: if |
density.col |
a character string specifying the |
density.linewidth |
a numeric value specifying the |
density.linetype |
a numeric value or character string specifying the
|
point |
logical: if |
point.col |
a character string specifying the |
point.linewidth |
a numeric value specifying the |
point.linetype |
a numeric value or character string specifying the
|
ci |
logical: if |
ci.col |
character string specifying the |
ci.linewidth |
a numeric value specifying the |
ci.linetype |
a numeric value or character string specifying the
|
line |
logical: if |
intercept |
a numeric value indicating the |
linetype |
a character string indicating the |
line.col |
a character string indicating the |
xlab |
a character string indicating the |
ylab |
a character string indicating the |
xlim |
a numeric vector with two elements indicating the
|
ylim |
a numeric vector with two elements indicating the
|
xbreaks |
a numeric vector indicating the |
ybreaks |
a numeric vector indicating the |
axis.title.size |
a numeric value indicating the |
axis.text.size |
a numeric value indicating the |
strip.text.size |
a numeric value indicating the |
title |
a character string indicating the |
subtitle |
a character string indicating the |
group.col |
a character vector indicating the |
plot.margin |
a numeric vector with four elements indicating the
|
legend.title |
a character string indicating the |
legend.position |
a character string indicating the |
legend.box.margin |
a numeric vector with four elements indicating the
|
facet.ncol |
a numeric value indicating the |
facet.nrow |
a numeric value indicating the |
facet.scales |
a character string indicating the |
filename |
a character string indicating the |
width |
a numeric value indicating the |
height |
a numeric value indicating the |
units |
a character string indicating the |
dpi |
a numeric value indicating the |
write |
a character string naming a file for writing the output
into either a text file with file extension |
append |
logical: if |
check |
logical: if |
output |
logical: if |
Value
Returns an object of class misty.object, which is a list with following
entries:
call |
function call |
type |
type of analysis |
data |
list with the input specified in |
args |
specification of function arguments |
boot |
data frame with bootstrap replicates of the arithmetic mean of median when bootstrapping was requested |
plot |
ggplot2 object for plotting the results and the data frame used for plotting |
result |
result table |
Note
Bootstrap confidence intervals are computed using the R package boot
by Angelo Canty and Brain Ripley (2024).
Author(s)
Takuya Yanagida takuya.yanagida@univie.ac.at
References
Baguley, T. S. (2012). Serious stats: A guide to advanced statistics for the behavioral sciences. Palgrave Macmillan.
Canty, A., & Ripley, B. (2024). boot: Bootstrap R (S-Plus) Functions. R package version 1.3-31.
Rasch, D., Kubinger, K. D., & Yanagida, T. (2011). Statistics in psychology - Using R and SPSS. John Wiley & Sons.
See Also
test.z, test.t, ci.mean.diff,
ci.cor, ci.prop, ci.var,
ci.sd, descript
Examples
#----------------------------------------------------------------------------
# Confidence Interval (CI) for the Arithmetic Mean
# Example 1a: Two-Sided 95% CI
ci.mean(mtcars)
# Example 1b: Two-Sided 95% Difference-Adjusted CI
ci.mean(mtcars, adjust = TRUE)
# Example 1c: Two-Sided 95% CI with known population standard deviation
ci.mean(mtcars, mpg, sigma = 6)
# Alternative specification without using the '...' argument
ci.mean(mtcars$mpg, sigma = 6)
#----------------------------------------------------------------------------
# Confidence Interval (CI) for the Median
# Example 2a: Two-Sided 95% CI
ci.median(mtcars)
# Example 2b: One-Sided 99% CI
ci.median(mtcars, alternative = "less", conf.level = 0.99)
## Not run:
#----------------------------------------------------------------------------
# Bootstrap Confidence Interval (CI)
# Example 3a: Bias-corrected (BC) percentile bootstrap CI
ci.mean(mtcars, boot = "bc")
# Example 3b: Bias-corrected and accelerated (BCa) bootstrap CI,
# 5000 bootstrap replications, set seed of the pseudo-random number generator
ci.mean(mtcars, boot = "bca", R = 5000, seed = 123)
#----------------------------------------------------------------------------
# Grouping and Split Variable
# Example 4a: Grouping variable
ci.mean(mtcars, mpg, cyl, disp, group = "vs")
# Alternative specification without using the '...' argument
ci.mean(mtcars[, c("mpg", "cyl", "disp")], group = mtcars$vs)
# Example 4b: Split variable
ci.mean(mtcars, mpg, cyl, disp, split = "am")
# Alternative specification
ci.mean(mtcars[, c("mpg", "cyl", "disp")], split = mtcars$am)
# Example 4c: Grouping and split variable
ci.mean(mtcars, mpg, cyl, disp, group = "vs", split = "am")
# Alternative specification
ci.mean(mtcars[, c("mpg", "cyl", "disp")], group = mtcars$vs, split = mtcars$am)
#----------------------------------------------------------------------------
# Write Output
# Example 5a: Text file
ci.mean(mtcars, write = "CI_Mean_Text.txt")
# Example 5b: Excel file
ci.mean(mtcars, write = "CI_Mean_Excel.xlsx")
#----------------------------------------------------------------------------
# Plot Confidence Intervals
# Example 6a: Two-Sided 95
ci.mean(mtcars, disp, hp, plot = "ci")
# Example 6b: Grouping variable
ci.mean(mtcars, disp, hp, group = "vs", plot = "ci")
# Example 6c: Split variable
ci.mean(mtcars, disp, hp, split = "am", plot = "ci")
# Example 6d: Save plot as PDF file
ci.mean(mtcars, disp, hp, plot = "ci", saveplot = "CI_Mean.pdf",
width = 9, height = 6)
# Example 6e: Save plot as PNG file
ci.mean(mtcars, disp, hp, plot = "ci", saveplot = "CI_Mean.png",
width = 9, height = 6)
#----------------------------------------------------------------------------
# Example 7: Plot Bootstrap Samples
# Example 7a: Two-Sided 95
ci.mean(mtcars, disp, hp, boot = "bc", plot = "boot")
# Example 7b: Grouping variable
ci.mean(mtcars, disp, hp, group = "vs", boot = "bc", plot = "boot")
# Example 7c: Split variable
ci.mean(mtcars, disp, hp, split = "am", boot = "bc", plot = "boot")
# Example 7d: Save plot as PDF file
ci.mean(mtcars, disp, hp, boot = "bc", plot = "boot", saveplot = "CI_Mean_Boot.pdf",
width = 12, height = 7)
# Example 7e: Save plot as PNG file
ci.mean(mtcars, disp, hp, boot = "bc", plot = "boot", saveplot = "CI_Mean_Boot.png",
width = 12, height = 7)
## End(Not run)
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