test.t | R Documentation |
This function performs one-sample, two-sample, and paired-sample t-tests and provides descriptive statistics, effect size measure, and a plot showing bar plots with error bars for (difference-adjusted) confidence intervals.
test.t(x, ...)
## Default S3 method:
test.t(x, y = NULL, mu = 0, paired = FALSE,
alternative = c("two.sided", "less", "greater"), conf.level = 0.95,
hypo = TRUE, descript = TRUE, effsize = FALSE, weighted = FALSE,
cor = TRUE, ref = NULL, correct = FALSE, digits = 2, p.digits = 3,
as.na = NULL, plot = FALSE, bar = TRUE, point = FALSE, ci = TRUE,
line = TRUE, jitter = FALSE, adjust = TRUE, point.size = 4, errorbar.width = 0.1,
xlab = NULL, ylab = NULL, ylim = NULL, ybreaks = ggplot2::waiver(),
linetype = 3, linewidth = 0.8, jitter.size = 1.25, jitter.width = 0.05,
jitter.height = 0, jitter.alpha = 0.1, title = "",
subtitle = "Confidence Interval", filename = NULL, width = NA, height = NA,
units = c("in", "cm", "mm", "px"),
dpi = 600, write = NULL, append = TRUE, check = TRUE, output = TRUE, ...)
## S3 method for class 'formula'
test.t(formula, data, alternative = c("two.sided", "less", "greater"),
conf.level = 0.95, hypo = TRUE, descript = TRUE, effsize = FALSE, weighted = FALSE,
cor = TRUE, ref = NULL, correct = FALSE, digits = 2, p.digits = 3, as.na = NULL,
plot = FALSE, bar = TRUE, point = FALSE, ci = TRUE, line = TRUE,
jitter = FALSE, adjust = TRUE, point.size = 4, errorbar.width = 0.1, xlab = NULL,
ylab = NULL, ylim = NULL, ybreaks = ggplot2::waiver(), linetype = 3,
linewidth = 0.8, jitter.size = 1.25, jitter.width = 0.05, jitter.height = 0,
jitter.alpha = 0.1, title = "", subtitle = "Confidence Interval", filename = NULL,
width = NA, height = NA, units = c("in", "cm", "mm", "px"), dpi = 600,
write = NULL, append = TRUE, check = TRUE, output = TRUE, ...)
x |
a numeric vector of data values. |
... |
further arguments to be passed to or from methods. |
y |
a numeric vector of data values. |
mu |
a numeric value indicating the population mean under the
null hypothesis. Note that the argument |
paired |
logical: if |
alternative |
a character string specifying the alternative hypothesis,
must be one of |
hypo |
logical: if |
descript |
logical: if |
effsize |
logical: if |
weighted |
logical: if |
cor |
logical: if |
ref |
character string |
correct |
logical: if |
conf.level |
a numeric value between 0 and 1 indicating the confidence level of the interval. |
digits |
an integer value indicating the number of decimal places to be used for displaying descriptive statistics and confidence interval. |
p.digits |
an integer value indicating the number of decimal places to be used for displaying the p-value. |
as.na |
a numeric vector indicating user-defined missing values,
i.e. these values are converted to |
plot |
logical: if |
bar |
ogical: if |
point |
logical: if |
ci |
logical: if |
jitter |
logical: if |
line |
logical: if |
adjust |
logical: if |
point.size |
a numeric value indicating the |
errorbar.width |
a numeric value indicating the horizontal bar width of the error bar. |
xlab |
a character string specifying the labels for the x-axis. |
ylab |
a character string specifying the labels for the y-axis. |
ylim |
a numeric vector of length two specifying limits of the limits of the y-axis. |
ybreaks |
a numeric vector specifying the points at which tick-marks are drawn at the y-axis. |
linetype |
an integer value or character string specifying the line type for the line representing the population mean under the null hypothesis, i.e., 0 = blank, 1 = solid, 2 = dashed, 3 = dotted, 4 = dotdash, 5 = longdash, 6 = twodash. |
linewidth |
a numeric value indicating the |
jitter.size |
a numeric value indicating the |
jitter.width |
a numeric value indicating the amount of horizontal jitter. |
jitter.height |
a numeric value indicating the amount of vertical jitter. |
jitter.alpha |
a numeric value between 0 and 1 for specifying the
|
title |
a character string specifying the text for the title for the plot. |
subtitle |
a character string specifying the text for the subtitle for the plot. |
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 text file with file extension
|
append |
logical: if |
check |
logical: if |
output |
logical: if |
formula |
in case of two sample t-test (i.e., |
data |
a matrix or data frame containing the variables in the
formula |
By default, Cohen's d based on the non-weighted
standard deviation (i.e., weighted = FALSE
) which does not assume homogeneity
of variance is computed (see Delacre et al., 2021) when requesting an effect size
measure (i.e., effsize = TRUE
). Cohen's d based on the pooled standard
deviation assuming equality of variances between groups can be requested by
specifying weighted = TRUE
.
Returns an object of class misty.object
, which is a list with following
entries:
call |
function call |
type |
type of analysis |
sample |
type of sample, i.e., one-, two-, or paired sample |
formula |
formula |
data |
data frame with the outcome and grouping variable |
args |
specification of function arguments |
plot |
ggplot2 object for plotting the results |
result |
result table |
Takuya Yanagida takuya.yanagida@univie.ac.at
Rasch, D., Kubinger, K. D., & Yanagida, T. (2011). Statistics in psychology - Using R and SPSS. John Wiley & Sons.
Delacre, M., Lakens, D., Ley, C., Liu, L., & Leys, C. (2021). Why Hedges' g*s based on the non-pooled standard deviation should be reported with Welch's t-test. https://doi.org/10.31234/osf.io/tu6mp
aov.b
, aov.w
, test.welch
, test.z
,
test.levene
, cohens.d
, ci.mean.diff
,
ci.mean
#----------------------------------------------------------------------------
# One-Sample Design
# Example 1a: Two-sided one-sample t-test, population mean = 20
test.t(mtcars$mpg, mu = 20)
# Example 1b: One-sided one-sample t-test, population mean = 20, print Cohen's d
test.t(mtcars$mpg, mu = 20, alternative = "greater", effsize = TRUE)
# Example 1c: Two-sided one-sample t-test, population mean = 20, plot results
test.t(mtcars$mpg, mu = 20, plot = TRUE)
## Not run:
# Example 1d: Two-sided one-sample t-test, population mean = 20, save plot
test.t(mtcars$mpg, mu = 20, plot = TRUE, filename = "One-sample_t-test.png",
width = 4, height = 5)
## End(Not run)
#----------------------------------------------------------------------------
# Two-Sample Design
# Example 2a: Two-sided two-sample t-test
test.t(mpg ~ vs, data = mtcars)
# Example 2b: Two-sided two-sample t-test, alternative specification
test.t(c(3, 1, 4, 2, 5, 3, 6, 7), c(5, 2, 4, 3, 1))
# Example 2c: One-sided two-sample t-test, print Cohen's d with weighted pooled SD
test.t(mpg ~ vs, data = mtcars, alternative = "greater", effsize = TRUE)
# Example 2d: Two-sided two-sample t-test, plot results
test.t(mpg ~ vs, data = mtcars, plot = TRUE)
## Not run:
# Example 2e: Two-sided two-sample t-test, plot results
test.t(mpg ~ vs, data = mtcars, plot = TRUE, filename = "Two-sample_t-test.png",
width = 5, height = 6)
## End(Not run)
#----------------------------------------------------------------------------
# Paired-Sample Design
# Example 3a: Two-sided paired-sample t-test
test.t(mtcars$drat, mtcars$wt, paired = TRUE)
# Example 3b: One-sided paired-sample t-test,
# print Cohen's d based on the SD of the difference scores
test.t(mtcars$drat, mtcars$wt, paired = TRUE, alternative = "greater",
effsize = TRUE)
# Example 3c: Two-sided paired-sample t-test, plot results
test.t(mtcars$drat, mtcars$wt, paired = TRUE, plot = TRUE)
## Not run:
# Example 3d: Two-sided paired-sample t-test, save plot
test.t(mtcars$drat, mtcars$wt, paired = TRUE, plot = TRUE,
filename = "Paired-sample_t-test.png", width = 4, height = 5)
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.