Package 'statsExpressions' reference manual

Title:	Tidy Dataframes and Expressions with Statistical Details
Description:	Utilities for producing dataframes with rich details for the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages. This package also forms the statistical processing backend for 'ggstatsplot'. References: Patil (2021) <doi:10.21105/joss.03236>.
Authors:	Indrajeet Patil [cre, aut, cph]
Maintainer:	Indrajeet Patil <[email protected]>
License:	MIT + file LICENSE
Version:	1.6.1
Built:	2024-11-11 19:17:07 UTC
Source:	https://github.com/indrajeetpatil/statsexpressions

Template for expressions with statistical details

Description

Creates an expression from a data frame containing statistical details. Ideally, this data frame would come from having run tidy_model_parameters() on your model object.

This function is currently not stable and should not be used outside of this package context.

Usage

add_expression_col(
  data,
  paired = FALSE,
  statistic.text = NULL,
  effsize.text = NULL,
  prior.type = NULL,
  n = NULL,
  n.text = ifelse(paired, list(quote(italic("n")["pairs"])),
    list(quote(italic("n")["obs"]))),
  digits = 2L,
  digits.df = 0L,
  digits.df.error = digits.df,
  ...
)
add_expression_col(
  data,
  paired = FALSE,
  statistic.text = NULL,
  effsize.text = NULL,
  prior.type = NULL,
  n = NULL,
  n.text = ifelse(paired, list(quote(italic("n")["pairs"])),
    list(quote(italic("n")["obs"]))),
  digits = 2L,
  digits.df = 0L,
  digits.df.error = digits.df,
  ...
)

Arguments

`data`	A data frame containing details from the statistical analysis and should contain some or all of the the following columns: statistic: the numeric value of a statistic. df.error: the numeric value of a parameter being modeled (often degrees of freedom for the test); irrelevant. if there are no degrees of freedom. df: relevant if the statistic in question has two degrees of freedom. p.value: the two-sided p-value associated with observed statistic. method: method describing the test carried out. effectsize: name of the effect size (if not present, same as `method`). estimate: estimated value of the effect size. conf.level: width for the confidence intervals. conf.low: lower bound for effect size estimate. conf.high: upper bound for effect size estimate. bf10: Bayes Factor value (if `bayesian = TRUE`).
`paired`	Logical that decides whether the experimental design is repeated measures/within-subjects or between-subjects. The default is `FALSE`.
`statistic.text`	A character that specifies the relevant test statistic. For example, for tests with t-statistic, `statistic.text = "t"`.
`effsize.text`	A character that specifies the relevant effect size.
`prior.type`	The type of prior.
`n`	An integer specifying the sample size used for the test.
`n.text`	A character that specifies the design, which will determine what the `n` stands for. It defaults to `quote(italic("n")["pairs"])` if `paired = TRUE`, and to `quote(italic("n")["obs"])` if `paired = FALSE`. If you wish to customize this further, you will need to provide object of `language` type.
`digits`, `digits.df`, `digits.df.error`	Number of decimal places to display for the parameters (default: `0L`).
`...`	Currently ignored.

Citation

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

Examples

set.seed(123)

# creating a data frame with stats results
stats_df <- cbind.data.frame(
  statistic  = 5.494,
  df         = 29.234,
  p.value    = 0.00001,
  estimate   = -1.980,
  conf.level = 0.95,
  conf.low   = -2.873,
  conf.high  = -1.088,
  method     = "Student's t-test"
)

# expression for *t*-statistic with Cohen's *d* as effect size
# note that the plotmath expressions need to be quoted
add_expression_col(
  data           = stats_df,
  statistic.text = list(quote(italic("t"))),
  effsize.text   = list(quote(italic("d"))),
  n              = 32L,
  n.text         = list(quote(italic("n")["no.obs"])),
  digits         = 3L,
  digits.df      = 3L
)

set.seed(123)

# creating a data frame with stats results
stats_df <- cbind.data.frame(
  statistic  = 5.494,
  df         = 29.234,
  p.value    = 0.00001,
  estimate   = -1.980,
  conf.level = 0.95,
  conf.low   = -2.873,
  conf.high  = -1.088,
  method     = "Student's t-test"
)

# expression for *t*-statistic with Cohen's *d* as effect size
# note that the plotmath expressions need to be quoted
add_expression_col(
  data           = stats_df,
  statistic.text = list(quote(italic("t"))),
  effsize.text   = list(quote(italic("d"))),
  n              = 32L,
  n.text         = list(quote(italic("n")["no.obs"])),
  digits         = 3L,
  digits.df      = 3L
)

Tidy version of the "Bugs" dataset.

Description

Tidy version of the "Bugs" dataset.

Usage

bugs_long
bugs_long

Format

A data frame with 372 rows and 6 variables

subject. Dummy identity number for each participant.
gender. Participant's gender (Female, Male).
region. Region of the world the participant was from.
education. Level of education.
condition. Condition of the experiment the participant gave rating for (LDLF: low freighteningness and low disgustingness; LFHD: low freighteningness and high disgustingness; HFHD: high freighteningness and low disgustingness; HFHD: high freighteningness and high disgustingness).
desire. The desire to kill an arthropod was indicated on a scale from 0 to 10.

Details

This data set, "Bugs", provides the extent to which men and women want to kill arthropods that vary in freighteningness (low, high) and disgustingness (low, high). Each participant rates their attitudes towards all anthropods. Subset of the data reported by Ryan et al. (2013).

References

Ryan, R. S., Wilde, M., & Crist, S. (2013). Compared to a small, supervised lab experiment, a large, unsupervised web-based experiment on a previously unknown effect has benefits that outweigh its potential costs. Computers in Human Behavior, 29(4), 1295-1301.

Examples

dim(bugs_long)
head(bugs_long)
dplyr::glimpse(bugs_long)
dim(bugs_long)
head(bugs_long)
dplyr::glimpse(bugs_long)

Data frame and expression for distribution properties

Description

Parametric, non-parametric, robust, and Bayesian measures of centrality.

Usage

centrality_description(
  data,
  x,
  y,
  type = "parametric",
  conf.level = NULL,
  tr = 0.2,
  digits = 2L,
  ...
)
centrality_description(
  data,
  x,
  y,
  type = "parametric",
  conf.level = NULL,
  tr = 0.2,
  digits = 2L,
  ...
)

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`	The grouping (or independent) variable in `data`.
`y`	The response (or outcome or dependent) variable from `data`.
`type`	A character specifying the type of statistical approach: `"parametric"` `"nonparametric"` `"robust"` `"bayes"` You can specify just the initial letter.
`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.
`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()`).
`...`	Currently ignored.

Details

This function describes a distribution for y variable for each level of the grouping variable in x by a set of indices (e.g., measures of centrality, dispersion, range, skewness, kurtosis, etc.). It additionally returns an expression containing a specified centrality measure. The function internally relies on datawizard::describe_distribution() function.

Centrality measures

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

Type	Measure	Function used
Parametric	mean	`datawizard::describe_distribution()`
Non-parametric	median	`datawizard::describe_distribution()`
Robust	trimmed mean	`datawizard::describe_distribution()`
Bayesian	MAP	`datawizard::describe_distribution()`

Citation

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

Examples

# for reproducibility
set.seed(123)

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

centrality_description(iris, Species, Sepal.Length, type = "parametric")

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

centrality_description(mtcars, am, wt, type = "nonparametric")

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

centrality_description(ToothGrowth, supp, len, type = "robust")

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

centrality_description(sleep, group, extra, type = "bayes")
# for reproducibility
set.seed(123)

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

centrality_description(iris, Species, Sepal.Length, type = "parametric")

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

centrality_description(mtcars, am, wt, type = "nonparametric")

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

centrality_description(ToothGrowth, supp, len, type = "robust")

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

centrality_description(sleep, group, extra, type = "bayes")

Contingency table analyses

Description

Parametric and Bayesian one-way and two-way contingency table analyses.

Usage

contingency_table(
  data,
  x,
  y = NULL,
  paired = FALSE,
  type = "parametric",
  counts = NULL,
  ratio = NULL,
  alternative = "two.sided",
  digits = 2L,
  conf.level = 0.95,
  sampling.plan = "indepMulti",
  fixed.margin = "rows",
  prior.concentration = 1,
  ...
)
contingency_table(
  data,
  x,
  y = NULL,
  paired = FALSE,
  type = "parametric",
  counts = NULL,
  ratio = NULL,
  alternative = "two.sided",
  digits = 2L,
  conf.level = 0.95,
  sampling.plan = "indepMulti",
  fixed.margin = "rows",
  prior.concentration = 1,
  ...
)

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`	The variable to use as the rows in the contingency table.
`y`	The variable to use as the columns in the contingency table. Default is `NULL`. If `NULL`, one-sample proportion test (a goodness of fit test) will be run for the `x` variable.
`paired`	Logical indicating whether data came from a within-subjects or repeated measures design study (Default: `FALSE`).
`type`	A character specifying the type of statistical approach: `"parametric"` `"nonparametric"` `"robust"` `"bayes"` You can specify just the initial letter.
`counts`	The variable in data containing counts, or `NULL` if each row represents a single observation.
`ratio`	A vector of proportions: the expected proportions for the proportion test (should sum to `1`). Default is `NULL`, which means the null is equal theoretical proportions across the levels of the nominal variable. E.g., `ratio = c(0.5, 0.5)` for two levels, `ratio = c(0.25, 0.25, 0.25, 0.25)` for four levels, etc.
`alternative`	A character string specifying the alternative hypothesis; Controls the type of CI returned: `"two.sided"` (default, two-sided CI), `"greater"` or `"less"` (one-sided CI). Partial matching is allowed (e.g., `"g"`, `"l"`, `"two"`...). See section One-Sided CIs in the effectsize_CIs vignette.
`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.
`sampling.plan`	Character describing the sampling plan. Possible options: `"indepMulti"` (independent multinomial; default) `"poisson"` `"jointMulti"` (joint multinomial) `"hypergeom"` (hypergeometric). For more, see `BayesFactor::contingencyTableBF()`.
`fixed.margin`	For the independent multinomial sampling plan, which margin is fixed (`"rows"` or `"cols"`). Defaults to `"rows"`.
`prior.concentration`	Specifies the prior concentration parameter, set to `1` by default. It indexes the expected deviation from the null hypothesis under the alternative, and corresponds to Gunel and Dickey's (1974) `"a"` parameter.
`...`	Additional arguments (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.

Contingency table analyses

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

two-way table

Hypothesis testing

Type	Design	Test	Function used
Parametric/Non-parametric	Unpaired	Pearson's chi-squared test	`stats::chisq.test()`
Bayesian	Unpaired	Bayesian Pearson's chi-squared test	`BayesFactor::contingencyTableBF()`
Parametric/Non-parametric	Paired	McNemar's chi-squared test	`stats::mcnemar.test()`
Bayesian	Paired	No	No

Effect size estimation

Type	Design	Effect size	CI available?	Function used
Parametric/Non-parametric	Unpaired	Cramer's V	Yes	`effectsize::cramers_v()`
Bayesian	Unpaired	Cramer's V	Yes	`effectsize::cramers_v()`
Parametric/Non-parametric	Paired	Cohen's g	Yes	`effectsize::cohens_g()`
Bayesian	Paired	No	No	No

one-way table

Hypothesis testing

Type	Test	Function used
Parametric/Non-parametric	Goodness of fit chi-squared test	`stats::chisq.test()`
Bayesian	Bayesian Goodness of fit chi-squared test	(custom)

Effect size estimation

Type	Effect size	CI available?	Function used
Parametric/Non-parametric	Pearson's C	Yes	`effectsize::pearsons_c()`
Bayesian	No	No	No

Examples

if (identical(Sys.getenv("NOT_CRAN"), "true")) {
  #### -------------------- association test ------------------------ ####

  # ------------------------ frequentist ---------------------------------

  # unpaired

  set.seed(123)
  contingency_table(
    data   = mtcars,
    x      = am,
    y      = vs,
    paired = FALSE
  )

  # paired

  paired_data <- tibble(
    response_before = structure(c(1L, 2L, 1L, 2L), levels = c("no", "yes"), class = "factor"),
    response_after = structure(c(1L, 1L, 2L, 2L), levels = c("no", "yes"), class = "factor"),
    Freq = c(65L, 25L, 5L, 5L)
  )

  set.seed(123)
  contingency_table(
    data   = paired_data,
    x      = response_before,
    y      = response_after,
    paired = TRUE,
    counts = Freq
  )

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

  # unpaired

  set.seed(123)
  contingency_table(
    data = mtcars,
    x = am,
    y = vs,
    paired = FALSE,
    type = "bayes"
  )

  # paired

  set.seed(123)
  contingency_table(
    data = paired_data,
    x = response_before,
    y = response_after,
    paired = TRUE,
    counts = Freq,
    type = "bayes"
  )

  #### -------------------- goodness-of-fit test -------------------- ####

  # ------------------------ frequentist ---------------------------------

  set.seed(123)
  contingency_table(
    data   = as.data.frame(HairEyeColor),
    x      = Eye,
    counts = Freq
  )

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

  set.seed(123)
  contingency_table(
    data   = as.data.frame(HairEyeColor),
    x      = Eye,
    counts = Freq,
    ratio  = c(0.2, 0.2, 0.3, 0.3),
    type   = "bayes"
  )
}
if (identical(Sys.getenv("NOT_CRAN"), "true")) {
  #### -------------------- association test ------------------------ ####

  # ------------------------ frequentist ---------------------------------

  # unpaired

  set.seed(123)
  contingency_table(
    data   = mtcars,
    x      = am,
    y      = vs,
    paired = FALSE
  )

  # paired

  paired_data <- tibble(
    response_before = structure(c(1L, 2L, 1L, 2L), levels = c("no", "yes"), class = "factor"),
    response_after = structure(c(1L, 1L, 2L, 2L), levels = c("no", "yes"), class = "factor"),
    Freq = c(65L, 25L, 5L, 5L)
  )

  set.seed(123)
  contingency_table(
    data   = paired_data,
    x      = response_before,
    y      = response_after,
    paired = TRUE,
    counts = Freq
  )

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

  # unpaired

  set.seed(123)
  contingency_table(
    data = mtcars,
    x = am,
    y = vs,
    paired = FALSE,
    type = "bayes"
  )

  # paired

  set.seed(123)
  contingency_table(
    data = paired_data,
    x = response_before,
    y = response_after,
    paired = TRUE,
    counts = Freq,
    type = "bayes"
  )

  #### -------------------- goodness-of-fit test -------------------- ####

  # ------------------------ frequentist ---------------------------------

  set.seed(123)
  contingency_table(
    data   = as.data.frame(HairEyeColor),
    x      = Eye,
    counts = Freq
  )

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

  set.seed(123)
  contingency_table(
    data   = as.data.frame(HairEyeColor),
    x      = Eye,
    counts = Freq,
    ratio  = c(0.2, 0.2, 0.3, 0.3),
    type   = "bayes"
  )
}

Correlation analyses

Description

Parametric, non-parametric, robust, and Bayesian correlation test.

Usage

corr_test(
  data,
  x,
  y,
  type = "parametric",
  digits = 2L,
  conf.level = 0.95,
  tr = 0.2,
  bf.prior = 0.707,
  ...
)
corr_test(
  data,
  x,
  y,
  type = "parametric",
  digits = 2L,
  conf.level = 0.95,
  tr = 0.2,
  bf.prior = 0.707,
  ...
)

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`	The column in `data` containing the explanatory variable to be plotted on the `x`-axis.
`y`	The column in `data` containing the response (outcome) variable to be plotted on the `y`-axis.
`type`	A character specifying the type of statistical approach: `"parametric"` `"nonparametric"` `"robust"` `"bayes"` 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.
`...`	Additional arguments (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.

Correlation analyses

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 and Effect size estimation

Type	Test	CI available?	Function used
Parametric	Pearson's correlation coefficient	Yes	`correlation::correlation()`
Non-parametric	Spearman's rank correlation coefficient	Yes	`correlation::correlation()`
Robust	Winsorized Pearson's correlation coefficient	Yes	`correlation::correlation()`
Bayesian	Bayesian Pearson's correlation coefficient	Yes	`correlation::correlation()`

Citation

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

Examples

# for reproducibility
set.seed(123)

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

corr_test(mtcars, wt, mpg, type = "parametric")

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

corr_test(mtcars, wt, mpg, type = "nonparametric")

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

corr_test(mtcars, wt, mpg, type = "robust")

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

corr_test(mtcars, wt, mpg, type = "bayes")
# for reproducibility
set.seed(123)

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

corr_test(mtcars, wt, mpg, type = "parametric")

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

corr_test(mtcars, wt, mpg, type = "nonparametric")

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

corr_test(mtcars, wt, mpg, type = "robust")

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

corr_test(mtcars, wt, mpg, type = "bayes")

Switch the type of statistics.

Description

Relevant mostly for {ggstatsplot} and {statsExpressions} packages, where different statistical approaches are supported via this argument: parametric, non-parametric, robust, and Bayesian. This switch function converts strings entered by users to a common pattern for convenience.

Usage

extract_stats_type(type)

stats_type_switch(type)
extract_stats_type(type)

stats_type_switch(type)

Arguments

type

A character specifying the type of statistical approach:

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

You can specify just the initial letter.

Examples

extract_stats_type("p")
extract_stats_type("bf")
extract_stats_type("p")
extract_stats_type("bf")

Edgar Anderson's Iris Data in long format.

Description

Edgar Anderson's Iris Data in long format.

Usage

iris_long
iris_long

Format

A data frame with 600 rows and 5 variables

id. Dummy identity number for each flower (150 flowers in total).
Species. The species are Iris setosa, versicolor, and virginica.
condition. Factor giving a detailed description of the attribute (Four levels: "Petal.Length", "Petal.Width", "Sepal.Length", "Sepal.Width").
attribute. What attribute is being measured ("Sepal" or "Pepal").
measure. What aspect of the attribute is being measured ("Length" or "Width").
value. Value of the measurement.

Details

This famous (Fisher's or Anderson's) iris data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.

This is a modified dataset from {datasets} package.

Examples

dim(iris_long)
head(iris_long)
dplyr::glimpse(iris_long)
dim(iris_long)
head(iris_long)
dplyr::glimpse(iris_long)

Convert long/tidy data frame to wide format

Description

This conversion is helpful mostly for repeated measures design, where removing NAs by participant can be a bit tedious.

Usage

long_to_wide_converter(
  data,
  x,
  y,
  subject.id = NULL,
  paired = TRUE,
  spread = TRUE,
  ...
)
long_to_wide_converter(
  data,
  x,
  y,
  subject.id = NULL,
  paired = TRUE,
  spread = TRUE,
  ...
)

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`	The grouping (or independent) variable from `data`. In case of a repeated measures or within-subjects design, if `subject.id` argument is not available or not explicitly specified, the function assumes that the data has already been sorted by such an id by the user and creates an internal identifier. So if your data is not sorted, the results can be inaccurate when there are more than two levels in `x` and there are `NA`s present. The data is expected to be sorted by user in subject-1, subject-2, ..., pattern.
`y`	The response (or outcome or dependent) variable from `data`.
`subject.id`	Relevant in case of a repeated measures or within-subjects design (`paired = TRUE`, i.e.), it specifies the subject or repeated measures identifier. Important: Note that if this argument is `NULL` (which is the default), the function assumes that the data has already been sorted by such an id by the user and creates an internal identifier. So if your data is not sorted and you leave this argument unspecified, the results can be inaccurate when there are more than two levels in `x` and there are `NA`s present.
`paired`	Logical that decides whether the experimental design is repeated measures/within-subjects or between-subjects. The default is `FALSE`.
`spread`	Logical that decides whether the data frame needs to be converted from long/tidy to wide (default: `TRUE`).
`...`	Currently ignored.

Value

A data frame with NAs removed while respecting the between-or-within-subjects nature of the dataset.

Citation

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

Examples

# for reproducibility
library(statsExpressions)
set.seed(123)

# repeated measures design
long_to_wide_converter(
  bugs_long,
  condition,
  desire,
  subject.id = subject,
  paired = TRUE
)

# independent measures design
long_to_wide_converter(mtcars, cyl, wt, paired = FALSE)

# for reproducibility
library(statsExpressions)
set.seed(123)

# repeated measures design
long_to_wide_converter(
  bugs_long,
  condition,
  desire,
  subject.id = subject,
  paired = TRUE
)

# independent measures design
long_to_wide_converter(mtcars, cyl, wt, paired = FALSE)

Random-effects meta-analysis

Description

Parametric, non-parametric, robust, and Bayesian random-effects meta-analysis.

Usage

meta_analysis(
  data,
  type = "parametric",
  random = "mixture",
  digits = 2L,
  conf.level = 0.95,
  ...
)
meta_analysis(
  data,
  type = "parametric",
  random = "mixture",
  digits = 2L,
  conf.level = 0.95,
  ...
)

Arguments

`data`	A data frame. It must contain columns named `estimate` (effect sizes or outcomes) and `std.error` (corresponding standard errors). These two columns will be used: as `yi` and `sei` arguments in `metafor::rma()` (for parametric test) as `yi` and `sei` arguments in `metaplus::metaplus()` (for robust test) as `y` and `SE` arguments in `metaBMA::meta_random()` (for Bayesian test)
`type`	A character specifying the type of statistical approach: `"parametric"` `"nonparametric"` `"robust"` `"bayes"` You can specify just the initial letter.
`random`	The type of random effects distribution. One of "normal", "t-dist", "mixture", for standard normal, $t$ -distribution or mixture of normals respectively.
`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.
`...`	Additional arguments passed to the respective meta-analysis function.

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.

Random-effects meta-analysis

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 and Effect size estimation

Type	Test	CI available?	Function used
Parametric	Pearson's correlation coefficient	Yes	`correlation::correlation()`
Non-parametric	Spearman's rank correlation coefficient	Yes	`correlation::correlation()`
Robust	Winsorized Pearson's correlation coefficient	Yes	`correlation::correlation()`
Bayesian	Bayesian Pearson's correlation coefficient	Yes	`correlation::correlation()`

Citation

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

Note

Important: The function assumes that you have already downloaded the needed package ({metafor}, {metaplus}, or {metaBMA}) for meta-analysis. If they are not available, you will be asked to install them.

Examples


# setup
set.seed(123)
library(statsExpressions)



# let's use `mag` dataset from `{metaplus}`
data(mag, package = "metaplus")
dat <- dplyr::rename(mag, estimate = yi, std.error = sei)

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

meta_analysis(dat)



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

meta_analysis(dat, type = "random", random = "normal")



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

meta_analysis(dat, type = "bayes")

# setup
set.seed(123)
library(statsExpressions)



# let's use `mag` dataset from `{metaplus}`
data(mag, package = "metaplus")
dat <- dplyr::rename(mag, estimate = yi, std.error = sei)

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

meta_analysis(dat)



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

meta_analysis(dat, type = "random", random = "normal")



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

meta_analysis(dat, type = "bayes")

Movie information and user ratings from IMDB.

Description

Movie information and user ratings from IMDB.

Usage

movies_long
movies_long

Format

A data frame with 1,579 rows and 8 variables

title. Title of the movie.
year. Year of release.
budget. Total budget (if known) in US dollars
length. Length in minutes.
rating. Average IMDB user rating.
votes. Number of IMDB users who rated this movie.
mpaa. MPAA rating.
genre. Different genres of movies (action, animation, comedy, drama, documentary, romance, short).

Details

Modified dataset from {ggplot2movies} package.

Source

https://CRAN.R-project.org/package=ggplot2movies

Examples

dim(movies_long)
head(movies_long)
dplyr::glimpse(movies_long)
dim(movies_long)
head(movies_long)
dplyr::glimpse(movies_long)

One-sample tests

Description

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",
  ...
)
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.

One-sample tests

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()`

Citation

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

Examples

# 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")
# 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")

One-way analysis of variance (ANOVA)

Description

Parametric, non-parametric, robust, and Bayesian one-way ANOVA.

Usage

oneway_anova(
  data,
  x,
  y,
  subject.id = NULL,
  type = "parametric",
  paired = FALSE,
  digits = 2L,
  conf.level = 0.95,
  effsize.type = "omega",
  var.equal = FALSE,
  bf.prior = 0.707,
  tr = 0.2,
  nboot = 100L,
  ...
)
oneway_anova(
  data,
  x,
  y,
  subject.id = NULL,
  type = "parametric",
  paired = FALSE,
  digits = 2L,
  conf.level = 0.95,
  effsize.type = "omega",
  var.equal = FALSE,
  bf.prior = 0.707,
  tr = 0.2,
  nboot = 100L,
  ...
)

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`	The grouping (or independent) variable from `data`. In case of a repeated measures or within-subjects design, if `subject.id` argument is not available or not explicitly specified, the function assumes that the data has already been sorted by such an id by the user and creates an internal identifier. So if your data is not sorted, the results can be inaccurate when there are more than two levels in `x` and there are `NA`s present. The data is expected to be sorted by user in subject-1, subject-2, ..., pattern.
`y`	The response (or outcome or dependent) variable from `data`.
`subject.id`	Relevant in case of a repeated measures or within-subjects design (`paired = TRUE`, i.e.), it specifies the subject or repeated measures identifier. Important: Note that if this argument is `NULL` (which is the default), the function assumes that the data has already been sorted by such an id by the user and creates an internal identifier. So if your data is not sorted and you leave this argument unspecified, the results can be inaccurate when there are more than two levels in `x` and there are `NA`s present.
`type`	A character specifying the type of statistical approach: `"parametric"` `"nonparametric"` `"robust"` `"bayes"` You can specify just the initial letter.
`paired`	Logical that decides whether the experimental design is repeated measures/within-subjects or between-subjects. The default is `FALSE`.
`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.
`effsize.type`	Type of effect size needed for parametric tests. The argument can be `"eta"` (partial eta-squared) or `"omega"` (partial omega-squared).
`var.equal`	a logical variable indicating whether to treat the two variances as being equal. If `TRUE` then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used.
`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.
`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.
`nboot`	Number of bootstrap samples for computing confidence interval for the effect size (Default: `100L`).
`...`	Additional arguments (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.

One-way ANOVA

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

between-subjects

Hypothesis testing

Type	No. of groups	Test	Function used
Parametric	> 2	Fisher's or Welch's one-way ANOVA	`stats::oneway.test()`
Non-parametric	> 2	Kruskal-Wallis one-way ANOVA	`stats::kruskal.test()`
Robust	> 2	Heteroscedastic one-way ANOVA for trimmed means	`WRS2::t1way()`
Bayes Factor	> 2	Fisher's ANOVA	`BayesFactor::anovaBF()`

Effect size estimation

Type	No. of groups	Effect size	CI available?	Function used
Parametric	> 2	partial eta-squared, partial omega-squared	Yes	`effectsize::omega_squared()`, `effectsize::eta_squared()`
Non-parametric	> 2	rank epsilon squared	Yes	`effectsize::rank_epsilon_squared()`
Robust	> 2	Explanatory measure of effect size	Yes	`WRS2::t1way()`
Bayes Factor	> 2	Bayesian R-squared	Yes	`performance::r2_bayes()`

within-subjects

Hypothesis testing

Type	No. of groups	Test	Function used
Parametric	> 2	One-way repeated measures ANOVA	`afex::aov_ez()`
Non-parametric	> 2	Friedman rank sum test	`stats::friedman.test()`
Robust	> 2	Heteroscedastic one-way repeated measures ANOVA for trimmed means	`WRS2::rmanova()`
Bayes Factor	> 2	One-way repeated measures ANOVA	`BayesFactor::anovaBF()`

Effect size estimation

Type	No. of groups	Effect size	CI available?	Function used
Parametric	> 2	partial eta-squared, partial omega-squared	Yes	`effectsize::omega_squared()`, `effectsize::eta_squared()`
Non-parametric	> 2	Kendall's coefficient of concordance	Yes	`effectsize::kendalls_w()`
Robust	> 2	Algina-Keselman-Penfield robust standardized difference average	Yes	`WRS2::wmcpAKP()`
Bayes Factor	> 2	Bayesian R-squared	Yes	`performance::r2_bayes()`

Citation

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

Examples


# for reproducibility
set.seed(123)
library(statsExpressions)

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

# between-subjects
oneway_anova(
  data = mtcars,
  x    = cyl,
  y    = wt
)

# within-subjects design
oneway_anova(
  data       = iris_long,
  x          = condition,
  y          = value,
  subject.id = id,
  paired     = TRUE
)

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

# between-subjects
oneway_anova(
  data = mtcars,
  x    = cyl,
  y    = wt,
  type = "np"
)

# within-subjects design
oneway_anova(
  data       = iris_long,
  x          = condition,
  y          = value,
  subject.id = id,
  paired     = TRUE,
  type       = "np"
)

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

# between-subjects
oneway_anova(
  data = mtcars,
  x    = cyl,
  y    = wt,
  type = "r"
)

# within-subjects design
oneway_anova(
  data       = iris_long,
  x          = condition,
  y          = value,
  subject.id = id,
  paired     = TRUE,
  type       = "r"
)



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

# between-subjects
oneway_anova(
  data = mtcars,
  x    = cyl,
  y    = wt,
  type = "bayes"
)

# within-subjects design
oneway_anova(
  data       = iris_long,
  x          = condition,
  y          = value,
  subject.id = id,
  paired     = TRUE,
  type       = "bayes"
)

# for reproducibility
set.seed(123)
library(statsExpressions)

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

# between-subjects
oneway_anova(
  data = mtcars,
  x    = cyl,
  y    = wt
)

# within-subjects design
oneway_anova(
  data       = iris_long,
  x          = condition,
  y          = value,
  subject.id = id,
  paired     = TRUE
)

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

# between-subjects
oneway_anova(
  data = mtcars,
  x    = cyl,
  y    = wt,
  type = "np"
)

# within-subjects design
oneway_anova(
  data       = iris_long,
  x          = condition,
  y          = value,
  subject.id = id,
  paired     = TRUE,
  type       = "np"
)

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

# between-subjects
oneway_anova(
  data = mtcars,
  x    = cyl,
  y    = wt,
  type = "r"
)

# within-subjects design
oneway_anova(
  data       = iris_long,
  x          = condition,
  y          = value,
  subject.id = id,
  paired     = TRUE,
  type       = "r"
)



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

# between-subjects
oneway_anova(
  data = mtcars,
  x    = cyl,
  y    = wt,
  type = "bayes"
)

# within-subjects design
oneway_anova(
  data       = iris_long,
  x          = condition,
  y          = value,
  subject.id = id,
  paired     = TRUE,
  type       = "bayes"
)

p-value adjustment method text

Description

Preparing text to describe which p-value adjustment method was used

Usage

p_adjust_text(p.adjust.method)
p_adjust_text(p.adjust.method)

Arguments

p.adjust.method

Adjustment method for p-values for multiple comparisons. Possible methods are: "holm" (default), "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none".

Value

Standardized text description for what method was used.

Examples

p_adjust_text("none")
p_adjust_text("BY")

p_adjust_text("none")
p_adjust_text("BY")

Multiple pairwise comparison for one-way design

Description

Calculate parametric, non-parametric, robust, and Bayes Factor pairwise comparisons between group levels with corrections for multiple testing.

Usage

pairwise_comparisons(
  data,
  x,
  y,
  subject.id = NULL,
  type = "parametric",
  paired = FALSE,
  var.equal = FALSE,
  tr = 0.2,
  bf.prior = 0.707,
  p.adjust.method = "holm",
  digits = 2L,
  ...
)
pairwise_comparisons(
  data,
  x,
  y,
  subject.id = NULL,
  type = "parametric",
  paired = FALSE,
  var.equal = FALSE,
  tr = 0.2,
  bf.prior = 0.707,
  p.adjust.method = "holm",
  digits = 2L,
  ...
)

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`	The grouping (or independent) variable from `data`. In case of a repeated measures or within-subjects design, if `subject.id` argument is not available or not explicitly specified, the function assumes that the data has already been sorted by such an id by the user and creates an internal identifier. So if your data is not sorted, the results can be inaccurate when there are more than two levels in `x` and there are `NA`s present. The data is expected to be sorted by user in subject-1, subject-2, ..., pattern.
`y`	The response (or outcome or dependent) variable from `data`.
`subject.id`	Relevant in case of a repeated measures or within-subjects design (`paired = TRUE`, i.e.), it specifies the subject or repeated measures identifier. Important: Note that if this argument is `NULL` (which is the default), the function assumes that the data has already been sorted by such an id by the user and creates an internal identifier. So if your data is not sorted and you leave this argument unspecified, the results can be inaccurate when there are more than two levels in `x` and there are `NA`s present.
`type`	A character specifying the type of statistical approach: `"parametric"` `"nonparametric"` `"robust"` `"bayes"` You can specify just the initial letter.
`paired`	Logical that decides whether the experimental design is repeated measures/within-subjects or between-subjects. The default is `FALSE`.
`var.equal`	a logical variable indicating whether to treat the two variances as being equal. If `TRUE` then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used.
`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.
`p.adjust.method`	Adjustment method for p-values for multiple comparisons. Possible methods are: `"holm"` (default), `"hochberg"`, `"hommel"`, `"bonferroni"`, `"BH"`, `"BY"`, `"fdr"`, `"none"`.
`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()`).
`...`	Additional arguments passed to other methods.

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.

Pairwise comparison tests

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

between-subjects

Hypothesis testing

Type	Equal variance?	Test	p-value adjustment?	Function used
Parametric	No	Games-Howell test	Yes	`PMCMRplus::gamesHowellTest()`
Parametric	Yes	Student's t-test	Yes	`stats::pairwise.t.test()`
Non-parametric	No	Dunn test	Yes	`PMCMRplus::kwAllPairsDunnTest()`
Robust	No	Yuen's trimmed means test	Yes	`WRS2::lincon()`
Bayesian	`NA`	Student's t-test	`NA`	`BayesFactor::ttestBF()`

Effect size estimation

Not supported.

within-subjects

Hypothesis testing

Type	Test	p-value adjustment?	Function used
Parametric	Student's t-test	Yes	`stats::pairwise.t.test()`
Non-parametric	Durbin-Conover test	Yes	`PMCMRplus::durbinAllPairsTest()`
Robust	Yuen's trimmed means test	Yes	`WRS2::rmmcp()`
Bayesian	Student's t-test	`NA`	`BayesFactor::ttestBF()`

Effect size estimation

Not supported.

Citation

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

References

For more, see: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/pairwise.html

Examples


# for reproducibility
set.seed(123)
library(statsExpressions)

#------------------- between-subjects design ----------------------------

# parametric
# if `var.equal = TRUE`, then Student's t-test will be run
pairwise_comparisons(
  data            = mtcars,
  x               = cyl,
  y               = wt,
  type            = "parametric",
  var.equal       = TRUE,
  paired          = FALSE,
  p.adjust.method = "none"
)

# if `var.equal = FALSE`, then Games-Howell test will be run
pairwise_comparisons(
  data            = mtcars,
  x               = cyl,
  y               = wt,
  type            = "parametric",
  var.equal       = FALSE,
  paired          = FALSE,
  p.adjust.method = "bonferroni"
)

# non-parametric (Dunn test)
pairwise_comparisons(
  data            = mtcars,
  x               = cyl,
  y               = wt,
  type            = "nonparametric",
  paired          = FALSE,
  p.adjust.method = "none"
)

# robust (Yuen's trimmed means *t*-test)
pairwise_comparisons(
  data            = mtcars,
  x               = cyl,
  y               = wt,
  type            = "robust",
  paired          = FALSE,
  p.adjust.method = "fdr"
)

# Bayes Factor (Student's *t*-test)
pairwise_comparisons(
  data   = mtcars,
  x      = cyl,
  y      = wt,
  type   = "bayes",
  paired = FALSE
)

#------------------- within-subjects design ----------------------------

# parametric (Student's *t*-test)
pairwise_comparisons(
  data            = bugs_long,
  x               = condition,
  y               = desire,
  subject.id      = subject,
  type            = "parametric",
  paired          = TRUE,
  p.adjust.method = "BH"
)

# non-parametric (Durbin-Conover test)
pairwise_comparisons(
  data            = bugs_long,
  x               = condition,
  y               = desire,
  subject.id      = subject,
  type            = "nonparametric",
  paired          = TRUE,
  p.adjust.method = "BY"
)

# robust (Yuen's trimmed means t-test)
pairwise_comparisons(
  data            = bugs_long,
  x               = condition,
  y               = desire,
  subject.id      = subject,
  type            = "robust",
  paired          = TRUE,
  p.adjust.method = "hommel"
)

# Bayes Factor (Student's *t*-test)
pairwise_comparisons(
  data       = bugs_long,
  x          = condition,
  y          = desire,
  subject.id = subject,
  type       = "bayes",
  paired     = TRUE
)

# for reproducibility
set.seed(123)
library(statsExpressions)

#------------------- between-subjects design ----------------------------

# parametric
# if `var.equal = TRUE`, then Student's t-test will be run
pairwise_comparisons(
  data            = mtcars,
  x               = cyl,
  y               = wt,
  type            = "parametric",
  var.equal       = TRUE,
  paired          = FALSE,
  p.adjust.method = "none"
)

# if `var.equal = FALSE`, then Games-Howell test will be run
pairwise_comparisons(
  data            = mtcars,
  x               = cyl,
  y               = wt,
  type            = "parametric",
  var.equal       = FALSE,
  paired          = FALSE,
  p.adjust.method = "bonferroni"
)

# non-parametric (Dunn test)
pairwise_comparisons(
  data            = mtcars,
  x               = cyl,
  y               = wt,
  type            = "nonparametric",
  paired          = FALSE,
  p.adjust.method = "none"
)

# robust (Yuen's trimmed means *t*-test)
pairwise_comparisons(
  data            = mtcars,
  x               = cyl,
  y               = wt,
  type            = "robust",
  paired          = FALSE,
  p.adjust.method = "fdr"
)

# Bayes Factor (Student's *t*-test)
pairwise_comparisons(
  data   = mtcars,
  x      = cyl,
  y      = wt,
  type   = "bayes",
  paired = FALSE
)

#------------------- within-subjects design ----------------------------

# parametric (Student's *t*-test)
pairwise_comparisons(
  data            = bugs_long,
  x               = condition,
  y               = desire,
  subject.id      = subject,
  type            = "parametric",
  paired          = TRUE,
  p.adjust.method = "BH"
)

# non-parametric (Durbin-Conover test)
pairwise_comparisons(
  data            = bugs_long,
  x               = condition,
  y               = desire,
  subject.id      = subject,
  type            = "nonparametric",
  paired          = TRUE,
  p.adjust.method = "BY"
)

# robust (Yuen's trimmed means t-test)
pairwise_comparisons(
  data            = bugs_long,
  x               = condition,
  y               = desire,
  subject.id      = subject,
  type            = "robust",
  paired          = TRUE,
  p.adjust.method = "hommel"
)

# Bayes Factor (Student's *t*-test)
pairwise_comparisons(
  data       = bugs_long,
  x          = condition,
  y          = desire,
  subject.id = subject,
  type       = "bayes",
  paired     = TRUE
)

Expressions with statistics for tidy regression data frames

Description

Expressions with statistics for tidy regression data frames

Usage

tidy_model_expressions(
  data,
  statistic = NULL,
  digits = 2L,
  effsize.type = "omega",
  ...
)
tidy_model_expressions(
  data,
  statistic = NULL,
  digits = 2L,
  effsize.type = "omega",
  ...
)

Arguments

`data`	A tidy data frame from regression model object (see `tidy_model_parameters()`).
`statistic`	Which statistic is to be displayed (either `"t"` or `"f"`or `"z"` or `"chi"`) in the expression.
`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()`).
`effsize.type`	Type of effect size needed for parametric tests. The argument can be `"eta"` (partial eta-squared) or `"omega"` (partial omega-squared).
`...`	Currently ignored.

Details

When any of the necessary numeric column values (estimate, statistic, p.value) are missing, for these rows, a NULL is returned instead of an expression with empty strings.

Citation

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

Examples


# setup
set.seed(123)
library(statsExpressions)

# extract a tidy data frame
df <- tidy_model_parameters(lm(wt ~ am * cyl, mtcars))

# create a column containing expression; the expression will depend on `statistic`
tidy_model_expressions(df, statistic = "t")
tidy_model_expressions(df, statistic = "z")
tidy_model_expressions(df, statistic = "chi")

# setup
set.seed(123)
library(statsExpressions)

# extract a tidy data frame
df <- tidy_model_parameters(lm(wt ~ am * cyl, mtcars))

# create a column containing expression; the expression will depend on `statistic`
tidy_model_expressions(df, statistic = "t")
tidy_model_expressions(df, statistic = "z")
tidy_model_expressions(df, statistic = "chi")

Convert `{parameters}` package output to `{tidyverse}` conventions

Description

Convert {parameters} package output to {tidyverse} conventions

Usage

tidy_model_parameters(model, ...)
tidy_model_parameters(model, ...)

Arguments

model

Statistical Model.

...

Arguments passed to or from other methods. Non-documented arguments are digits, p_digits, ci_digits and footer_digits to set the number of digits for the output. If s_value = TRUE, the p-value will be replaced by the S-value in the output (cf. Rafi and Greenland 2020). pd adds an additional column with the probability of direction (see bayestestR::p_direction() for details). groups can be used to group coefficients. It will be passed to the print-method, or can directly be used in print(), see documentation in print.parameters_model(). Furthermore, see 'Examples' in model_parameters.default(). For developers, whose interest mainly is to get a "tidy" data frame of model summaries, it is recommended to set pretty_names = FALSE to speed up computation of the summary table.

Citation

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

Examples

model <- lm(mpg ~ wt + cyl, data = mtcars)
tidy_model_parameters(model)

model <- lm(mpg ~ wt + cyl, data = mtcars)
tidy_model_parameters(model)

Two-sample tests

Description

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

Usage

two_sample_test(
  data,
  x,
  y,
  subject.id = NULL,
  type = "parametric",
  paired = FALSE,
  alternative = "two.sided",
  digits = 2L,
  conf.level = 0.95,
  effsize.type = "g",
  var.equal = FALSE,
  bf.prior = 0.707,
  tr = 0.2,
  nboot = 100L,
  ...
)
two_sample_test(
  data,
  x,
  y,
  subject.id = NULL,
  type = "parametric",
  paired = FALSE,
  alternative = "two.sided",
  digits = 2L,
  conf.level = 0.95,
  effsize.type = "g",
  var.equal = FALSE,
  bf.prior = 0.707,
  tr = 0.2,
  nboot = 100L,
  ...
)

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`	The grouping (or independent) variable from `data`. In case of a repeated measures or within-subjects design, if `subject.id` argument is not available or not explicitly specified, the function assumes that the data has already been sorted by such an id by the user and creates an internal identifier. So if your data is not sorted, the results can be inaccurate when there are more than two levels in `x` and there are `NA`s present. The data is expected to be sorted by user in subject-1, subject-2, ..., pattern.
`y`	The response (or outcome or dependent) variable from `data`.
`subject.id`	Relevant in case of a repeated measures or within-subjects design (`paired = TRUE`, i.e.), it specifies the subject or repeated measures identifier. Important: Note that if this argument is `NULL` (which is the default), the function assumes that the data has already been sorted by such an id by the user and creates an internal identifier. So if your data is not sorted and you leave this argument unspecified, the results can be inaccurate when there are more than two levels in `x` and there are `NA`s present.
`type`	A character specifying the type of statistical approach: `"parametric"` `"nonparametric"` `"robust"` `"bayes"` You can specify just the initial letter.
`paired`	Logical that decides whether the experimental design is repeated measures/within-subjects or between-subjects. The default is `FALSE`.
`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.
`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).
`var.equal`	a logical variable indicating whether to treat the two variances as being equal. If `TRUE` then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used.
`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.
`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.
`nboot`	Number of bootstrap samples for computing confidence interval for the effect size (Default: `100L`).
`...`	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.

Two-sample tests

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

between-subjects

Hypothesis testing

Type	No. of groups	Test	Function used
Parametric	2	Student's or Welch's t-test	`stats::t.test()`
Non-parametric	2	Mann-Whitney U test	`stats::wilcox.test()`
Robust	2	Yuen's test for trimmed means	`WRS2::yuen()`
Bayesian	2	Student's t-test	`BayesFactor::ttestBF()`

Effect size estimation

Type	No. of groups	Effect size	CI available?	Function used
Parametric	2	Cohen's d, Hedge's g	Yes	`effectsize::cohens_d()`, `effectsize::hedges_g()`
Non-parametric	2	r (rank-biserial correlation)	Yes	`effectsize::rank_biserial()`
Robust	2	Algina-Keselman-Penfield robust standardized difference	Yes	`WRS2::akp.effect()`
Bayesian	2	difference	Yes	`bayestestR::describe_posterior()`

within-subjects

Hypothesis testing

Type	No. of groups	Test	Function used
Parametric	2	Student's t-test	`stats::t.test()`
Non-parametric	2	Wilcoxon signed-rank test	`stats::wilcox.test()`
Robust	2	Yuen's test on trimmed means for dependent samples	`WRS2::yuend()`
Bayesian	2	Student's t-test	`BayesFactor::ttestBF()`

Effect size estimation

Type	No. of groups	Effect size	CI available?	Function used
Parametric	2	Cohen's d, Hedge's g	Yes	`effectsize::cohens_d()`, `effectsize::hedges_g()`
Non-parametric	2	r (rank-biserial correlation)	Yes	`effectsize::rank_biserial()`
Robust	2	Algina-Keselman-Penfield robust standardized difference	Yes	`WRS2::wmcpAKP()`
Bayesian	2	difference	Yes	`bayestestR::describe_posterior()`

Citation

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

Examples



# ----------------------- within-subjects -------------------------------------

# data
df <- dplyr::filter(bugs_long, condition %in% c("LDLF", "LDHF"))

# for reproducibility
set.seed(123)

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

two_sample_test(df, condition, desire, subject.id = subject, paired = TRUE, type = "parametric")

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

two_sample_test(df, condition, desire, subject.id = subject, paired = TRUE, type = "nonparametric")

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

two_sample_test(df, condition, desire, subject.id = subject, paired = TRUE, type = "robust")

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

two_sample_test(df, condition, desire, subject.id = subject, paired = TRUE, type = "bayes")
# ----------------------- between-subjects -------------------------------------

# for reproducibility
set.seed(123)

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

# unequal variance
two_sample_test(ToothGrowth, supp, len, type = "parametric")

# equal variance
two_sample_test(ToothGrowth, supp, len, type = "parametric", var.equal = TRUE)

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

two_sample_test(ToothGrowth, supp, len, type = "nonparametric")

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

two_sample_test(ToothGrowth, supp, len, type = "robust")

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

two_sample_test(ToothGrowth, supp, len, type = "bayes")
# ----------------------- within-subjects -------------------------------------

# data
df <- dplyr::filter(bugs_long, condition %in% c("LDLF", "LDHF"))

# for reproducibility
set.seed(123)

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

two_sample_test(df, condition, desire, subject.id = subject, paired = TRUE, type = "parametric")

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

two_sample_test(df, condition, desire, subject.id = subject, paired = TRUE, type = "nonparametric")

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

two_sample_test(df, condition, desire, subject.id = subject, paired = TRUE, type = "robust")

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

two_sample_test(df, condition, desire, subject.id = subject, paired = TRUE, type = "bayes")
# ----------------------- between-subjects -------------------------------------

# for reproducibility
set.seed(123)

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

# unequal variance
two_sample_test(ToothGrowth, supp, len, type = "parametric")

# equal variance
two_sample_test(ToothGrowth, supp, len, type = "parametric", var.equal = TRUE)

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

two_sample_test(ToothGrowth, supp, len, type = "nonparametric")

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

two_sample_test(ToothGrowth, supp, len, type = "robust")

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

two_sample_test(ToothGrowth, supp, len, type = "bayes")

Package 'statsExpressions'

Help Index

Template for expressions with statistical details

Description

Usage

Arguments

Citation

Examples

Tidy version of the "Bugs" dataset.

Description

Usage

Format

Details

References

Examples

Data frame and expression for distribution properties

Description

Usage

Arguments

Details

Centrality measures

Citation

Examples

Contingency table analyses

Description

Usage

Arguments

Value

Contingency table analyses

two-way table

one-way table

Examples

Correlation analyses

Description

Usage

Arguments

Value

Correlation analyses

Citation

Examples

Switch the type of statistics.

Description

Usage

Arguments

Examples

Edgar Anderson's Iris Data in long format.

Description

Usage

Format

Details

Examples

Convert long/tidy data frame to wide format

Description

Usage

Arguments

Value

Citation

Examples

Random-effects meta-analysis

Description

Usage

Arguments

Value

Random-effects meta-analysis

Citation

Note

Examples

Movie information and user ratings from IMDB.

Description

Usage

Format

Details

Source

Examples

One-sample tests

Description

Usage

Arguments

Value

One-sample tests

Convert `{parameters}` package output to `{tidyverse}` conventions