| Title: | Visualising Multiple Pairwise Variable Correlations and Other Scores |
| Version: | 1.0.0 |
| Description: | We provide a tidy data structure and visualisations for multiple or grouped variable correlations, general association measures scagnostics and other pairwise scores suitable for numerical, ordinal and nominal variables. Supported measures include distance correlation, maximal information, ace correlation, Kendall's tau, and polychoric correlation. |
| License: | MIT + file LICENSE |
| Depends: | R (≥ 4.1.0) |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Imports: | stats, dplyr, cli, rlang, ggplot2, labeling, ggiraph, DendSer, tidyr, polycor, colorspace |
| Suggests: | DescTools, acepack, energy, linkspotter, minerva, scagnostics, testthat (≥ 3.0.0), knitr, rmarkdown, correlation, palmerpenguins, kableExtra, openintro, corrplot |
| Config/testthat/edition: | 3 |
| VignetteBuilder: | knitr |
| URL: | https://cbhurley.github.io/bullseye/, https://github.com/cbhurley/bullseye |
| NeedsCompilation: | no |
| Packaged: | 2025-05-09 09:53:02 UTC; catherine |
| Author: | Amit Chinwan [aut], Catherine Hurley [aut, cre] |
| Maintainer: | Catherine Hurley <catherine.hurley@mu.ie> |
| Repository: | CRAN |
| Date/Publication: | 2025-05-09 10:10:02 UTC |
bullseye: Visualising Multiple Pairwise Variable Correlations and Other Scores
Description
We provide a tidy data structure and visualisations for multiple or grouped variable correlations, general association measures scagnostics and other pairwise scores suitable for numerical, ordinal and nominal variables. Supported measures include distance correlation, maximal information, ace correlation, Kendall's tau, and polychoric correlation.
Author(s)
Maintainer: Catherine Hurley catherine.hurley@mu.ie
Authors:
Amit Chinwan amit.chinwan.2019@mumail.ie
See Also
Useful links:
Calculates ace based transformations and correlation, handling missing values and factors.
Description
Calculates ace based transformations and correlation, handling missing values and factors.
Usage
ace_cor(x, y, handle.na = TRUE)
Arguments
x |
a numeric vector or factor |
y |
a numeric vector or factor |
handle.na |
If TRUE uses pairwise complete observations. |
Value
result of acepack::ace
Examples
ace_cor(iris$Sepal.Length, iris$Species)
Adds number of observations column to pairwise tibble
Description
Adds number of observations column to pairwise tibble
Usage
add_nobs_to_pairwise(scores, d, by = NULL)
Arguments
scores |
An object of class |
d |
a dataframe |
by |
a character string for the name of the conditioning variable from d used to construct scores. Set to |
Value
A pairwise tibble with a column n
Examples
irisc <- pairwise_scores(iris[40:150,], by= "Species")
irisc <- add_nobs_to_pairwise(irisc, iris[40:150,], by= "Species")
irisc
plot_pairwise(irisc) # setosa gets a small slice in proportion to n
Converts a pairwise to a symmetric matrix. Uses the first entry for each (x,y) pair.
Description
Converts a pairwise to a symmetric matrix. Uses the first entry for each (x,y) pair.
Usage
## S3 method for class 'pairwise'
as.matrix(x, ...)
Arguments
x |
An object of class pairwise |
... |
other arguments |
Value
A symmetric matrix
Alternating conditional expectations correlation
Description
Calculates the maximal correlation coefficient from alternating conditional expectations algorithm for every variable pair in a dataset.
Usage
pair_ace(d, handle.na = TRUE, warnings = TRUE, ...)
Arguments
d |
A dataframe |
handle.na |
If TRUE uses pairwise complete observations, otherwise NAs not handled. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Details
The maximal correlation is calculated using alternating conditional expectations
algorithm which find the transformations of variables such that the squared correlation
is maximised. The ace function from acepack package is used for the
calculation.
Value
A tibble of class pairwise with a maximal correlation from the alternating conditional expectations
algorithm for every variable pair
References
Breiman, Leo, and Jerome H. Friedman. "Estimating optimal transformations for multiple regression and correlation." Journal of the American statistical Association 80.391 (1985): 580-598.
Examples
pair_ace(iris)
Canonical correlation
Description
Calculates canonical correlation for every variable pair in a dataset.
Usage
pair_cancor(d, handle.na = TRUE, warnings = TRUE, ...)
Arguments
d |
A dataframe |
handle.na |
If TRUE uses pairwise complete observations to calculate correlation coefficient,, otherwise NAs not handled. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Value
A tibble of class pairwise with canonical correlation for every numeric or factor or mixed variable pair
Examples
pair_cancor(iris)
Pearson's Contingency Coefficient for association between factors.
Description
Calculates Pearson's Contingency coefficient for every factor variable pair in a dataset.
Usage
pair_chi(d, handle.na = TRUE, warnings = TRUE, ...)
Arguments
d |
A dataframe |
handle.na |
ignored. Pairwise complete observations are used automatically. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Details
The Pearson's contingency coefficient is calculated using ContCoef.
NAs are automatically handled by pairwise omit.
Value
A tibble of class pairwise with calculated Pearson's contingency coefficient for every factor variable
pair, or NULL if there are not at least two factor variables
Examples
pair_chi(iris)
Default scores calculated by pairwise_scores
Description
Gives a list specifying the function to be used for two numeric (nn) variables, two factors (ff), two ordinals (oo) and for a factor-numeric pair (fn).
Usage
pair_control(
nn = "pair_cor",
oo = "pair_polychor",
ff = "pair_cancor",
fn = "pair_cancor",
nnargs = NULL,
ooargs = NULL,
ffargs = NULL,
fnargs = NULL
)
Arguments
nn |
function for numeric pairs of variables, should return object of class |
oo |
function for ordered factor pairs of variables, should return object of class |
ff |
function for factor pairs of variables (not ordered), should return object of class |
fn |
function for factor-numeric pairs of variables, should return object of class |
nnargs |
other arguments for the nn function |
ooargs |
other arguments for the oo function |
ffargs |
other arguments for the ff function |
fnargs |
other arguments for the fn function |
Value
list
Pearson, Spearman or Kendall correlation
Description
Calculates one of either pearson, spearman or kendall correlation for every numeric variable pair in a dataset.
Usage
pair_cor(d, method = "pearson", handle.na = TRUE, warnings = TRUE, ...)
Arguments
d |
A dataframe |
method |
A character string for the correlation coefficient to be calculated. Either "pearson" (default), "spearman", or "kendall". If the value is "all", then all three correlations are calculated. |
handle.na |
If TRUE uses pairwise complete observations to calculate correlation coefficient, otherwise NAs not handled. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Value
A tibble of class pairwise with calculated association value for every numeric variable pair,
or NULL if there are not at least two numeric variables
See Also
See pair_methods for other score options.
Examples
pair_cor(iris)
pair_cor(iris, method="kendall")
pair_cor(iris, method="spearman")
pair_cor(iris, method="all")
Distance correlation
Description
Calculates distance correlation for every numeric variable pair in a dataset.
Usage
pair_dcor(d, handle.na = TRUE, warnings = TRUE, ...)
Arguments
d |
A dataframe |
handle.na |
If TRUE uses pairwise complete observations to calculate distance correlation, otherwise NAs not handled. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Details
The distance correlation is calculated using dcor2d from energy package
Value
A tibble of class pairwise with distance correlation for every numeric variable pair,
or NULL if there are not at least two numeric variables
Examples
pair_dcor(iris)
Goodman Kruskal's Gamma for association between ordinal factors.
Description
Calculates Goodman Kruskal's Gamma coefficient for every factor variable pair in a dataset.
Usage
pair_gkGamma(d, handle.na = TRUE, warnings = TRUE, ...)
Arguments
d |
A dataframe |
handle.na |
ignored. Pairwise complete observations are used automatically. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Details
The Goodman Kruskal's Gamma coefficient is calculated using GoodmanKruskalGamma
function from the DescTools package. Assumes factor levels are in the given order.
NAs are automatically handled by pairwise omit.
Value
A tibble of class pairwise with factor variable pairs and Goodman Kruskal's Gamma coefficient,
or NULL if there are not at least two factor variables
Examples
pair_gkGamma(iris)
Goodman Kruskal's Tau for association between ordinal factors.
Description
Calculates Goodman Kruskal's Tau coefficient for every factor variable pair in a dataset.
Usage
pair_gkTau(d, handle.na = TRUE, warnings = TRUE, ...)
Arguments
d |
A dataframe |
handle.na |
ignored. Pairwise complete observations are used automatically. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Details
The Goodman Kruskal's Tau coefficient is calculated using GoodmanKruskalTau
function from the DescTools package. Assumes factor levels are in the given order.
NAs are automatically handled by pairwise omit.
Value
A tibble of class pairwise with Goodman Kruskal's Tau for every factor variable pair,
or NULL if there are not at least two factor variables
Examples
pair_gkTau(iris)
Kendall's correlation
Description
Calculates Kendall's correlation for every numeric variable pair in a dataset.
Usage
pair_kendall(d, handle.na = TRUE, warnings = TRUE, ...)
Arguments
d |
A dataframe |
handle.na |
If TRUE uses pairwise complete observations to calculate correlation coefficient, otherwise NAs not handled. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Value
A tibble of class pairwise with calculated association value for every numeric variable pair,
or NULL if there are not at least two numeric variables
See Also
See pair_methods for other score options.
Examples
pair_kendall(iris)
# same as
pair_cor(iris, method="kendall")
Pairwise score functions available in the package
Description
A tibble of score functions along with the types of variable pairs these functions can be applied to. It also contains information regarding the packages used to calculate scores and the range of the values calculated.
Usage
pair_methods
Format
An object of class tbl_df (inherits from tbl, data.frame) with 17 rows and 7 columns.
Value
tibble
Examples
pair_methods
MINE family values
Description
Calculates MINE family values for every numeric variable pair in a dataset.
Usage
pair_mine(d, method = "MIC", handle.na = TRUE, warnings = TRUE, ...)
Arguments
d |
A dataframe |
method |
character vector for the MINE value to be calculated. Subset of "MIC","MAS","MEV","MCN","MICR2", "GMIC", "TIC" |
handle.na |
If TRUE uses pairwise complete observations to calculate score, otherwise NAs not handled. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Details
The values are calculated using mine from minerva
Value
A tibble of class pairwise with scores for numeric variable pairs,
or NULL if there are not at least two numeric variables
References
Reshef, David N., et al. "Detecting novel associations in large data sets." science 334.6062 (2011): 1518-1524
Examples
pair_mine(iris)
pair_mine(iris, method="MAS")
Normalized mutual information
Description
Calculates normalized mutual information for every numeric or factor or mixed variable pair in a dataset.
Usage
pair_nmi(d, handle.na = TRUE, warnings = TRUE, ...)
Arguments
d |
A dataframe |
handle.na |
If TRUE uses pairwise complete observations to calculate normalized mutual information, otherwise NAs not handled. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Details
The normalized mutual information is calculated using maxNMI from linkpotter package
Value
A tibble of class pairwise
Examples
if (requireNamespace("linkspotter", quietly = TRUE)) {
pair_nmi(iris)
}
Polychoric correlation
Description
Calculates Polychoric correlation using for every factor variable pair in a dataset.
Usage
pair_polychor(d, handle.na = TRUE, warnings = TRUE, ...)
Arguments
d |
A dataframe |
handle.na |
ignored. Pairwise complete observations are used automatically. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Details
The polychoric correlation is calculated using the polychor function from the
polycor package, and assumes factor levels are in the given order. NAs are automatically handled by pairwise omit.
Value
A tibble of class pairwise with polychoric correlation for factor pairs,
or NULL if there are not at least two factor variables
Examples
pair_polychor(iris)
Polyserial correlation
Description
Calculates Polyserial correlation using for every factor-numeric variable pair in a dataset.
Usage
pair_polyserial(d, handle.na = TRUE, warnings = TRUE, ...)
Arguments
d |
A dataframe |
handle.na |
ignored. Pairwise complete observations are used automatically. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Details
The polyserial correlation is calculated using the polyserial function from the
polycor package, and assumes factor levels are in the given order. NAs are automatically handled by pairwise omit.
Value
A tibble of class pairwise with polyserial correlation for factor-numeric pairs,
or NULL if there are not at least one such pair.
Examples
pair_polyserial(iris)
Graph-theoretic scagnostics values
Description
Calculates scagnostic values for every numeric variable pair in a dataset.
Usage
pair_scagnostics(
d,
scagnostic = c("Outlying", "Skewed", "Clumpy", "Sparse", "Striated", "Convex",
"Skinny", "Stringy", "Monotonic"),
handle.na = TRUE,
warnings = TRUE,
...
)
Arguments
d |
A dataframe |
scagnostic |
a character vector for the scagnostic to be calculated. Subset of "Outlying", "Stringy", "Striated", "Clumpy", "Sparse", "Skewed", "Convex", "Skinny" or "Monotonic" |
handle.na |
If TRUE uses pairwise complete observations. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Details
The scagnostic values are calculated using scagnostics
function from the scagnostics package.
Value
A tibble of class pairwise with scagnostic values for every numeric variable pair,
or NULL if there are not at least two numeric variables
References
Wilkinson, Leland, Anushka Anand, and Robert Grossman. "Graph-theoretic scagnostics." Information Visualization, IEEE Symposium on. IEEE Computer Society, 2005
Examples
pair_scagnostics(iris)
Spearman correlation
Description
Calculates Spearman's correlation for every numeric variable pair in a dataset.
Usage
pair_spearman(d, handle.na = TRUE, warnings = TRUE, ...)
Arguments
d |
A dataframe |
handle.na |
If TRUE uses pairwise complete observations to calculate correlation coefficient, otherwise NAs not handled. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Value
A tibble of class pairwise with calculated association value for every numeric variable pair,
or NULL if there are not at least two numeric variables
See Also
See pair_methods for other score options.
Examples
pair_spearman(iris)
# same as
pair_cor(iris, method="spearman")
Kendall's tau A for association between ordinal factors.
Description
Calculates Kendall's tau A for every factor variable pair in a dataset.
Usage
pair_tauA(d, handle.na = TRUE, warnings = TRUE, ...)
Arguments
d |
A dataframe |
handle.na |
ignored. Pairwise complete observations are used automatically. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Details
Calculated using KendallTauA. Assumes factor levels are in the given order.
NAs are automatically handled by pairwise omit.
Value
A tibble of class pairwise with factor pairs, or NULL if there are not at least two factor variables
Examples
d <- data.frame(x=rnorm(20),
y=factor(sample(3,20, replace=TRUE)),
z=factor(sample(2,20, replace=TRUE)))
pair_tauA(d)
Kendall's tau B for association between ordinal factors.
Description
Calculates Kendall's tau B every factor variable pair in a dataset.
Usage
pair_tauB(d, handle.na = TRUE, warnings = TRUE, ...)
Arguments
d |
A dataframe |
handle.na |
ignored. Pairwise complete observations are used automatically. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Details
Calculated using KendallTauB. Assumes factor levels are in the given order.
NAs are automatically handled by pairwise omit.
Value
A tibble of class pairwise with factor pairs, or NULL if there are not at least two factor variables
Examples
d <- data.frame(x=rnorm(20),
y=factor(sample(3,20, replace=TRUE)),
z=factor(sample(2,20, replace=TRUE)))
pair_tauB(d)
Stuarts's tau C for association between ordinal factors.
Description
Calculates Stuarts's tau C every factor variable pair in a dataset.
Usage
pair_tauC(d, handle.na = TRUE, warnings = TRUE, ...)
Arguments
d |
A dataframe |
handle.na |
ignored. Pairwise complete observations are used automatically. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Details
Calculated using StuartTauC. Assumes factor levels are in the given order.
NAs are automatically handled by pairwise omit.
Value
A tibble of class pairwise with factor pairs, or NULL if there are not at least two factor variables
Examples
d <- data.frame(x=rnorm(20),
y=factor(sample(3,20, replace=TRUE)),
z=factor(sample(2,20, replace=TRUE)))
pair_tauC(d)
Kendall's W for association between ordinal factors.
Description
Calculates Kendall's tau W every factor variable pair in a dataset.
Usage
pair_tauW(d, handle.na = TRUE, warnings = TRUE, ...)
Arguments
d |
A dataframe |
handle.na |
ignored. Pairwise complete observations are used automatically. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Details
Calculated using KendallW. Assumes factor levels are in the given order.
NAs are automatically handled by pairwise omit.
Value
A tibble of class pairwise with factor pairs, or NULL if there are not at least two factor variables
Examples
d <- data.frame(x=rnorm(20),
y=factor(sample(3,20, replace=TRUE)),
z=factor(sample(2,20, replace=TRUE)))
pair_tauW(d)
Uncertainty coefficient for association between factors.
Description
Calculates uncertainty coefficient for every factor variable pair in a dataset.
Usage
pair_uncertainty(d, handle.na = TRUE, warnings = TRUE, ...)
Arguments
d |
A dataframe |
handle.na |
ignored. Pairwise complete observations are used automatically. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
... |
other arguments |
Details
The Uncertainty coefficient is calculated using UncertCoef function from the
DescTools package.
Value
A tibble of class pairwise with every factor variable pair and uncertainty coefficient value,
or NULL if there are not at least two factor variables
Examples
pair_uncertainty(iris)
A generic function to create a data structure for summarising variable pairs in a dataset
Description
Creates a data structure for every variable pair in a dataset.
Usage
pairwise(x, score = NA_character_, pair_type = NA_character_)
## S3 method for class 'matrix'
pairwise(x, score = NA_character_, pair_type = NA_character_)
## S3 method for class 'data.frame'
pairwise(x, score = NA_character_, pair_type = NA_character_)
## S3 method for class 'easycorrelation'
pairwise(x, score = NA_character_, pair_type = NA_character_)
as.pairwise(x, score = NA_character_, pair_type = NA_character_)
Arguments
x |
A dataframe or symmetric matrix. |
score |
a character string indicating the value of association |
pair_type |
a character string specifying the type of variable pair, should be either "nn", "fn", "ff", for a numeric-numeric pair, factor-numeric pair, or factor-factor pair, or NA if unknown. |
Details
The pairwise class has columns x and y for (ordered pairs) of variables, where x < y.
The column score has the name of the summary measure used for the two variables,
and the column value has the associated value.
The group column defaults to "all", meaning summary measures apply to the complete dataset,
otherwise it describes a subset of the data.
The functions pair_* calculate pairwise tibbles for the summary measure named by *, eg pair_cor(), pair_cancor().
The functions pairwise_scores() and pairwise_by() calculate pairwise tibbles for levels of a grouping variable.
The function pairwise_multi() calculates a pairwise_tibble for multiple named scores.
The pairwise tibble has at most one row for each combination of x, y, score and group.
This is checked prior to plotting by plot.pairwise.
Note that the pair_type column is included for information purposes, but it is not currently used by plot.pairwise.
Value
A tbl_df of class pairwise for pairs of variables with a column value for the score value,
score for a type of association value and pair_type for the type of variable pair.
Methods (by class)
-
pairwise(matrix): pairwise method -
pairwise(data.frame): pairwise method -
pairwise(easycorrelation): pairwise method
Functions
-
as.pairwise(): Same aspairwise
Examples
pairwise(cor(iris[,1:4]), score="pearson")
pairwise(iris)
pair_cor(iris)
pair_cancor(iris)
pairwise_scores(iris, by="Species")
pairwise_multi(iris)
Constructs a pairwise result for each level of a by variable.
Description
Constructs a pairwise result for each level of a by variable.
Usage
pairwise_by(
d,
by,
pair_fun,
ungrouped = TRUE,
warnings = TRUE,
add.nobs = FALSE
)
Arguments
d |
a dataframe |
by |
a character string for the name of the conditioning variable. |
pair_fun |
A function returning a |
ungrouped |
If TRUE calculates the ungrouped score in addition to grouped scores. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
add.nobs |
If TRUE, adds an extra column containing the number of observations used for each score. |
Value
tibble of class "pairwise"
Examples
pairwise_by(iris, by="Species", pair_cor)
Calculates multiple scores
Description
Calculates multiple scores for every variable pair in a dataset.
Usage
pairwise_multi(
d,
scores = c("pair_cor", "pair_spearman", "pair_dcor", "pair_mine", "pair_ace",
"pair_cancor", "pair_nmi", "pair_uncertainty", "pair_chi"),
handle.na = TRUE,
warnings = TRUE
)
Arguments
d |
dataframe |
scores |
a character vector naming functions returning a |
handle.na |
If TRUE uses pairwise complete observations to calculate pairwise score, otherwise NAs not handled. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
Value
tibble of class "pairwise"
Examples
iris1 <- iris
iris1$Sepal.Length <- cut(iris1$Sepal.Length,3)
pairwise_multi(iris1)
Calculates scores or conditional scores for a dataset
Description
Calculates scores for every variable pair in a dataset when by is NULL. If by
is a name of a variable in the dataset, conditional scores for every
variable pair at different levels of the grouping variable are calculated.
Usage
pairwise_scores(
d,
by = NULL,
ungrouped = TRUE,
control = pair_control(),
handle.na = TRUE,
warnings = TRUE,
add.nobs = FALSE
)
Arguments
d |
a dataframe |
by |
a character string for the name of the conditioning variable. Set to |
ungrouped |
Ignored if |
control |
a list for the measures to be calculated for different variable types. The default is
|
handle.na |
If TRUE uses pairwise complete observations to calculate measure of association. |
warnings |
If TRUE, generates a warning for datasets of one row, one column, or with constant variables. |
add.nobs |
If TRUE, adds an extra column containing the number of observations used for each score. |
Details
Returns a pairwise tibble structure.
Value
A tibble with class pairwise.
Examples
irisc <- pairwise_scores(iris)
irisc <- pairwise_scores(iris, control=pair_control(nnargs= c(method="spearman")))
irisc <- pairwise_scores(iris, control=pair_control(fn="pair_ace"))
#Lots of numerical measures
irisc <- pairwise_scores(iris, control=pair_control(nn="pairwise_multi", fn=NULL))
irisc <- pairwise_scores(iris,
control=pair_control(nn="pairwise_multi", nnargs="pair_cor", fn=NULL))
#conditional measures
cond_iris <- pairwise_scores(iris, by = "Species")
cond_iris_wo <- pairwise_scores(iris, by = "Species",ungrouped=FALSE) # without overall
irisc <- pairwise_scores(iris, control=pair_control(nn="pairwise_multi", fn=NULL))
irisc <- pairwise_scores(iris, by = "Species",control=pair_control(nn="pairwise_multi", fn=NULL))
#scagnostics
sc <- pairwise_scores(iris, control=pair_control(nn="pair_scagnostics", fn=NULL)) # ignore fn pairs
sc <- pairwise_scores(iris, by = "Species",
control=pair_control(nn="pair_scagnostics", fn=NULL)) # ignore fn pairs
Plot method for class pairwise.
Description
Plot method for class pairwise.
Usage
## S3 method for class 'pairwise'
plot(x, type = c("matrix", "linear"), ...)
Arguments
x |
An object of class |
type |
If "matrix", calls |
... |
further arguments to |
Value
a plot
Examples
plot(pairwise_scores(iris))
Pairwise plot in a matrix layout
Description
Plots multiple pairwise variable scores in a matrix layout.
Usage
plot_pairwise(
scores,
var_order = "seriate_max",
score_limits = NULL,
inner_width = 0.5,
center_level = "all",
na.value = "grey80",
pal = "Blue-Red 3",
interactive = FALSE
)
Arguments
scores |
The scores for the matrix plot. Either of class |
var_order |
The variable order to be used. The default NULL means variables in are ordered alphabetically. A value of "seriate_max" means variables are re-ordered to emphasize pairs with maximum abolute scores. A value of "seriate_max_diff" means variables are re-ordered to emphasize pairs with maximum score differences. Otherwise var_order must be a subset of variables in scores. |
score_limits |
a numeric vector of length specifying the limits of the scale. |
inner_width |
A number between 0 and 1 specifying radius of the inner bullseye. |
center_level |
Specifies which level of group goes into the innter bullseye. Defaults to "all". |
na.value |
used for scores with a value of NA |
pal |
If provided, should name a one of the sequential or diverging palettes from package colorspace.
See |
interactive |
defaults to FALSE |
Value
A girafe object if interactive==TRUE, otherwise a ggplot2.
If scores has one value for x,y pair, then a filled circle is drawn with fill representing the score value. If there are multiple values for each x,y pair then the filled circle is split into wedges, with the wedge fill representing the values.
If some rows have group=center_level, then the glyph is drawn as a bullseye.
If scores has a column n, then this is used to weight the slices. See the third example below.
Examples
pair_cor(iris) |> plot_pairwise()
pairwise_scores(iris,by="Species") |> plot_pairwise()
pairwise_scores(iris[-(1:30),],by="Species", add.nobs=TRUE) |> plot_pairwise()
Pairwise plot in a linear layout
Description
Plots the calculated measures of association among different variable pairs for a dataset in a linear layout.
Usage
plot_pairwise_linear(
scores,
pair_order = "seriate_max",
geom = c("point", "tile"),
add_lines = FALSE,
score_limits = NULL,
na.value = "grey80",
pal = "Blue-Red 3",
interactive = FALSE
)
Arguments
scores |
A tibble with the calculated association measures for the matrix plot.
Either of class |
pair_order |
The variable pair order to be used. The default NULL means pairs are in order of their first appearance in |
geom |
The geom to be used. Should be "point" or "tile". |
add_lines |
When geom= "point" is used, should the points be connected by lines? Defaults to FALSE. |
score_limits |
a numeric vector of length specifying the limits of the scale. |
na.value |
used for geom_tile with a value of NA |
pal |
For geom="title" only. If provided, should name a one of the sequential or diverging palettes from package colorspace.
See |
interactive |
defaults to FALSE |
Value
A girafe object if interactive==TRUE, otherwise a ggplot2.
Examples
plot_pairwise_linear(pairwise_scores(iris))
plot_pairwise_linear(pairwise_scores(iris,by="Species"))
plot_pairwise_linear(pairwise_multi(iris), geom="point")