Parametric, non-parametric, robust, and Bayesian one-sample tests.
one_sample_test(
data,
x,
type = "parametric",
test.value = 0,
alternative = "two.sided",
digits = 2L,
conf.level = 0.95,
tr = 0.2,
bf.prior = 0.707,
effsize.type = "g",
...
)The returned tibble data frame can contain some or all of the following columns (the exact columns will depend on the statistical test):
statistic: the numeric value of a statistic
df: the numeric value of a parameter being modeled (often degrees
of freedom for the test)
df.error and df: relevant only if the statistic in question has
two degrees of freedom (e.g. anova)
p.value: the two-sided p-value associated with the observed statistic
method: the name of the inferential statistical test
estimate: estimated value of the effect size
conf.low: lower bound for the effect size estimate
conf.high: upper bound for the effect size estimate
conf.level: width of the confidence interval
conf.method: method used to compute confidence interval
conf.distribution: statistical distribution for the effect
effectsize: the name of the effect size
n.obs: number of observations
expression: pre-formatted expression containing statistical details
For examples, see data frame output vignette.
A data frame (or a tibble) from which variables specified are to
be taken. Other data types (e.g., matrix,table, array, etc.) will not
be accepted. Additionally, grouped data frames from {dplyr} should be
ungrouped before they are entered as data.
A numeric variable from the data frame data.
A character specifying the type of statistical approach:
"parametric"
"nonparametric"
"robust"
"bayes"
You can specify just the initial letter.
A number indicating the true value of the mean (Default:
0).
a character string specifying the alternative
hypothesis, must be one of "two.sided" (default),
"greater" or "less". You can specify just the initial
letter.
Number of digits for rounding or significant figures. May also
be "signif" to return significant figures or "scientific"
to return scientific notation. Control the number of digits by adding the
value as suffix, e.g. digits = "scientific4" to have scientific
notation with 4 decimal places, or digits = "signif5" for 5
significant figures (see also signif()).
Scalar between 0 and 1 (default: 95%
confidence/credible intervals, 0.95). If NULL, no confidence intervals
will be computed.
Trim level for the mean when carrying out robust tests. In case
of an error, try reducing the value of tr, which is by default set to
0.2. Lowering the value might help.
A number between 0.5 and 2 (default 0.707), the prior
width to use in calculating Bayes factors and posterior estimates. In
addition to numeric arguments, several named values are also recognized:
"medium", "wide", and "ultrawide", corresponding to r scale values
of 1/2, sqrt(2)/2, and 1, respectively. In case of an ANOVA, this
value corresponds to scale for fixed effects.
Type of effect size needed for parametric tests. The
argument can be "d" (for Cohen's d) or "g" (for Hedge's g).
Currently ignored.
The table below provides summary about:
statistical test carried out for inferential statistics
type of effect size estimate and a measure of uncertainty for this estimate
functions used internally to compute these details
Hypothesis testing
| Type | Test | Function used |
| Parametric | One-sample Student's t-test | stats::t.test() |
| Non-parametric | One-sample Wilcoxon test | stats::wilcox.test() |
| Robust | Bootstrap-t method for one-sample test | WRS2::trimcibt() |
| Bayesian | One-sample Student's t-test | BayesFactor::ttestBF() |
Effect size estimation
| Type | Effect size | CI available? | Function used |
| Parametric | Cohen's d, Hedge's g | Yes | effectsize::cohens_d(), effectsize::hedges_g() |
| Non-parametric | r (rank-biserial correlation) | Yes | effectsize::rank_biserial() |
| Robust | trimmed mean | Yes | WRS2::trimcibt() |
| Bayes Factor | difference | Yes | bayestestR::describe_posterior() |
Patil, I., (2021). statsExpressions: R Package for Tidy Dataframes and Expressions with Statistical Details. Journal of Open Source Software, 6(61), 3236, https://doi.org/10.21105/joss.03236
# for reproducibility
set.seed(123)
# ----------------------- parametric -----------------------
one_sample_test(mtcars, wt, test.value = 3)
# ----------------------- non-parametric -------------------
one_sample_test(mtcars, wt, test.value = 3, type = "nonparametric")
# ----------------------- robust ---------------------------
one_sample_test(mtcars, wt, test.value = 3, type = "robust")
# ----------------------- Bayesian -------------------------
one_sample_test(mtcars, wt, test.value = 3, type = "bayes")
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