One-sample tests

Parametric, non-parametric, robust, and Bayesian one-sample tests.

Usage

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",
  ...
)

Arguments

data

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.

x

A numeric variable from the data frame data.

type

A character specifying the type of statistical approach:

"parametric"
"nonparametric"
"robust"
"bayes"

You can specify just the initial letter.

test.value

A number indicating the true value of the mean (Default: 0).

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.

digits

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()).

conf.level

Scalar between 0 and 1 (default: 95% confidence/credible intervals, 0.95). If NULL, no confidence intervals will be computed.

tr

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.

bf.prior

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.

effsize.type

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.

Value

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.

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()`

Examples

# for reproducibility
set.seed(123)

# ----------------------- parametric -----------------------

one_sample_test(mtcars, wt, test.value = 3)
#> # A tibble: 1 × 15
#>      mu statistic df.error p.value method            alternative effectsize
#>   <dbl>     <dbl>    <dbl>   <dbl> <chr>             <chr>       <chr>     
#> 1     3      1.26       31   0.218 One Sample t-test two.sided   Hedges' g 
#>   estimate conf.level conf.low conf.high conf.method conf.distribution n.obs
#>      <dbl>      <dbl>    <dbl>     <dbl> <chr>       <chr>             <int>
#> 1    0.217       0.95   -0.127     0.557 ncp         t                    32
#>   expression
#>   <list>    
#> 1 <language>

# ----------------------- non-parametric -------------------

one_sample_test(mtcars, wt, test.value = 3, type = "nonparametric")
#> # A tibble: 1 × 12
#>   statistic p.value method                    alternative effectsize       
#>       <dbl>   <dbl> <chr>                     <chr>       <chr>            
#> 1       319   0.308 Wilcoxon signed rank test two.sided   r (rank biserial)
#>   estimate conf.level conf.low conf.high conf.method n.obs expression
#>      <dbl>      <dbl>    <dbl>     <dbl> <chr>       <int> <list>    
#> 1    0.208       0.95   -0.184     0.543 normal         32 <language>

# ----------------------- robust ---------------------------

one_sample_test(mtcars, wt, test.value = 3, type = "robust")
#> # A tibble: 1 × 10
#>   statistic p.value n.obs method                                 effectsize  
#>       <dbl>   <dbl> <int> <chr>                                  <chr>       
#> 1      1.18   0.275    32 Bootstrap-t method for one-sample test Trimmed mean
#>   estimate conf.level conf.low conf.high expression
#>      <dbl>      <dbl>    <dbl>     <dbl> <list>    
#> 1     3.20       0.95     2.85      3.54 <language>

# ----------------------- Bayesian -------------------------

one_sample_test(mtcars, wt, test.value = 3, type = "bayes")
#> # A tibble: 1 × 16
#>   term       effectsize      estimate conf.level conf.low conf.high    pd
#>   <chr>      <chr>              <dbl>      <dbl>    <dbl>     <dbl> <dbl>
#> 1 Difference Bayesian t-test    0.195       0.95   -0.165     0.555  0.86
#>   prior.distribution prior.location prior.scale  bf10 method         
#>   <chr>                       <dbl>       <dbl> <dbl> <chr>          
#> 1 cauchy                          0       0.707 0.387 Bayesian t-test
#>   conf.method log_e_bf10 n.obs expression
#>   <chr>            <dbl> <int> <list>    
#> 1 ETI             -0.950    32 <language>