misty (version 0.7.1)

test.t: t-Test

Description

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.

Usage

test.t(x, ...)
# S3 method for default
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 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, ...)

Value

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

Arguments

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 mu is only used when computing a one sample t-test.

paired

logical: if TRUE, paired-samples t-test is computed.

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".

hypo

logical: if TRUE (default), null and alternative hypothesis are shown on the console.

descript

logical: if TRUE (default), descriptive statistics are shown on the console.

effsize

logical: if TRUE, effect size measure Cohen's d is shown on the console, see cohens.d function.

weighted

logical: if TRUE, the weighted pooled standard deviation is used to compute Cohen's d for a two-sample design (i.e., paired = FALSE), while standard deviation of the difference scores is used to compute Cohen's d for a paired-sample design (i.e., paired = TRUE).

cor

logical: if TRUE (default), paired = TRUE, and weighted = FALSE, Cohen's d for a paired-sample design while controlling for the correlation between the two sets of measurement is computed. Note that this argument is only used in a paired-sample design (i.e., paired = TRUE) when specifying weighted = FALSE.

ref

character string "x" or "y" for specifying the reference reference group when using the default test.t() function or a numeric value or character string indicating the reference group in a two-sample design when using the formula test.t() function. The standard deviation of the reference variable or reference group is used to standardized the mean difference to compute Cohen's d. Note that this argument is only used in a two-sample design (i.e., paired = FALSE).

correct

logical: if TRUE, correction factor to remove positive bias in small samples is used.

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 NA before conducting the analysis.

plot

logical: if TRUE, a plot showing bar plots with error bars for confidence intervals is drawn.

bar

ogical: if TRUE (default), bars representing means for each groups are drawn.

point

logical: if TRUE, points representing means for each groups are drawn.

ci

logical: if TRUE (default), error bars representing confidence intervals are drawn.

jitter

logical: if TRUE, jittered data points are drawn.

line

logical: if TRUE (default), a horizontal line is drawn at mu for the one-sample t-test or at 0 for the paired-sample t-test.

adjust

logical: if TRUE (default), difference-adjustment for the confidence intervals in a two-sample design is applied.

point.size

a numeric value indicating the size aesthetic for the point representing the mean value.

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 linewidth aesthetic for the line representing the population mean under the null hypothesis.

jitter.size

a numeric value indicating the size aesthetic

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 alpha argument in the geom_jitter function for controlling the opacity of the jittered data points.

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 filename argument (default is "NA_Pattern.pdf") including the file extension for 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.

width

a numeric value indicating the width argument (default is the size of the current graphics device) for the ggsave function.

height

a numeric value indicating the height argument (default is the size of the current graphics device) for the ggsave function.

units

a character string indicating the units argument (default is in) for the ggsave function.

dpi

a numeric value indicating the dpi argument (default is 600) for the ggsave function.

write

a character string naming a text file with file extension ".txt" (e.g., "Output.txt") for writing the output into a text file.

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.

formula

in case of two sample t-test (i.e., paired = FALSE), a formula of the form y ~ group where group is a numeric variable, character variable or factor with two values or factor levels giving the corresponding groups.

data

a matrix or data frame containing the variables in the formula formula.

Author

Takuya Yanagida

Details

Effect Size Measure

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.

References

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

See Also

aov.b, aov.w, test.welch, test.z, test.levene, cohens.d, ci.mean.diff, ci.mean

Examples

#----------------------------------------------------------------------------
# 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)

if (FALSE) {
# 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)}

#----------------------------------------------------------------------------
# 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)

if (FALSE) {
# 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)}

#----------------------------------------------------------------------------
# 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)

if (FALSE) {
# 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)}