Last chance! 50% off unlimited learning
Sale ends in
Performs a correlation analysis.
correlation(
data,
data2 = NULL,
select = NULL,
select2 = NULL,
rename = NULL,
method = "pearson",
p_adjust = "holm",
ci = 0.95,
bayesian = FALSE,
bayesian_prior = "medium",
bayesian_ci_method = "hdi",
bayesian_test = c("pd", "rope", "bf"),
redundant = FALSE,
include_factors = FALSE,
partial = FALSE,
partial_bayesian = FALSE,
multilevel = FALSE,
ranktransform = FALSE,
winsorize = FALSE,
verbose = TRUE,
standardize_names = getOption("easystats.standardize_names", FALSE),
...
)A data frame.
An optional data frame. If specified, all pair-wise correlations
between the variables in data and data2 will be computed.
(Ignored if data2 is specified.) Optional names
of variables that should be selected for correlation. Instead of providing
the data frames with those variables that should be correlated, data
can be a data frame and select and select2 are (quoted) names
of variables (columns) in data. correlation() will then
compute the correlation between data[select] and
data[select2]. If only select is specified, all pairwise
correlations between the select variables will be computed. This is
a "pipe-friendly" alternative way of using correlation() (see
'Examples').
In case you wish to change the names of the variables in
the output, these arguments can be used to specify these alternative names.
Note that the number of names should be equal to the number of columns
selected. Ignored if data2 is specified.
A character string indicating which correlation coefficient is
to be used for the test. One of "pearson" (default),
"kendall", "spearman" (but see also the robust argument), "biserial",
"polychoric", "tetrachoric", "biweight",
"distance", "percentage" (for percentage bend correlation),
"blomqvist" (for Blomqvist's coefficient), "hoeffding" (for
Hoeffding's D), "gamma", "gaussian" (for Gaussian Rank
correlation) or "shepherd" (for Shepherd's Pi correlation). Setting
"auto" will attempt at selecting the most relevant method
(polychoric when ordinal factors involved, tetrachoric when dichotomous
factors involved, point-biserial if one dichotomous and one continuous and
pearson otherwise). See below the details section for a description of
these indices.
Correction method for frequentist correlations. Can be one of
"holm" (default), "hochberg", "hommel",
"bonferroni", "BH", "BY", "fdr",
"somers" or "none". See
stats::p.adjust() for further details.
Confidence/Credible Interval level. If "default", then it is
set to 0.95 (95% CI).
If TRUE, will run the correlations under a
Bayesian framework. Note that for partial correlations, you will also need
to set partial_bayesian to TRUE to obtain "full" Bayesian
partial correlations. Otherwise, you will obtain pseudo-Bayesian partial
correlations (i.e., Bayesian correlation based on frequentist
partialization).
For the prior argument, several named values are
recognized: "medium.narrow", "medium", "wide", and
"ultrawide". These correspond to scale values of 1/sqrt(27),
1/3, 1/sqrt(3) and 1, respectively. See the
BayesFactor::correlationBF function.
See arguments in
model_parameters() for BayesFactor tests.
See arguments in
model_parameters() for BayesFactor tests.
Should the data include redundant rows (where each given correlation is repeated two times).
If TRUE, the factors are kept and eventually
converted to numeric or used as random effects (depending of
multilevel). If FALSE, factors are removed upfront.
Can be TRUE or "semi" for partial and
semi-partial correlations, respectively.
If TRUE, will run the correlations under a
Bayesian framework. Note that for partial correlations, you will also need
to set partial_bayesian to TRUE to obtain "full" Bayesian
partial correlations. Otherwise, you will obtain pseudo-Bayesian partial
correlations (i.e., Bayesian correlation based on frequentist
partialization).
If TRUE, the factors are included as random factors.
Else, if FALSE (default), they are included as fixed effects in the
simple regression model.
If TRUE, will rank-transform the variables prior to
estimating the correlation, which is one way of making the analysis more
resistant to extreme values (outliers). Note that, for instance, a Pearson's
correlation on rank-transformed data is equivalent to a Spearman's rank
correlation. Thus, using robust=TRUE and method="spearman" is
redundant. Nonetheless, it is an easy option to increase the robustness of the
correlation as well as flexible way to obtain Bayesian or multilevel
Spearman-like rank correlations.
Another way of making the correlation more "robust" (i.e.,
limiting the impact of extreme values). Can be either FALSE or a
number between 0 and 1 (e.g., 0.2) that corresponds to the desired
threshold. See the winsorize() function for more details.
Toggle warnings.
This option can be set to TRUE to run
insight::standardize_names() on the output to get standardized column
names. This option can also be set globally by running
options(easystats.standardize_names = TRUE).
Additional arguments (e.g., alternative) to be passed to
other methods. See stats::cor.test for further details.
A correlation object that can be displayed using the print, summary or
table methods.
The p_adjust argument can be used to adjust p-values for multiple
comparisons. All adjustment methods available in p.adjust function
stats package are supported.
Pearson's correlation: This is the most common correlation method. It corresponds to the covariance of the two variables normalized (i.e., divided) by the product of their standard deviations.
Spearman's rank correlation: A non-parametric measure of rank correlation (statistical dependence between the rankings of two variables). The Spearman correlation between two variables is equal to the Pearson correlation between the rank values of those two variables; while Pearson's correlation assesses linear relationships, Spearman's correlation assesses monotonic relationships (whether linear or not). Confidence Intervals (CI) for Spearman's correlations are computed using the Fieller et al. (1957) correction (see Bishara and Hittner, 2017).
Kendall's rank correlation: In the normal case, the Kendall correlation is preferred than the Spearman correlation because of a smaller gross error sensitivity (GES) and a smaller asymptotic variance (AV), making it more robust and more efficient. However, the interpretation of Kendall's tau is less direct than that of Spearman's rho, in the sense that it quantifies the difference between the percentage of concordant and discordant pairs among all possible pairwise events. Confidence Intervals (CI) for Kendall's correlations are computed using the Fieller et al. (1957) correction (see Bishara and Hittner, 2017).
Biweight midcorrelation: A measure of similarity that is median-based, instead of the traditional mean-based, thus being less sensitive to outliers. It can be used as a robust alternative to other similarity metrics, such as Pearson correlation (Langfelder & Horvath, 2012).
Distance correlation: Distance correlation measures both linear and non-linear association between two random variables or random vectors. This is in contrast to Pearson's correlation, which can only detect linear association between two random variables.
Percentage bend correlation: Introduced by Wilcox (1994), it
is based on a down-weight of a specified percentage of marginal observations
deviating from the median (by default, 20%).
Shepherd's Pi correlation: Equivalent to a Spearman's rank correlation after outliers removal (by means of bootstrapped Mahalanobis distance).
Blomqvist<U+2019>s coefficient: The Blomqvist<U+2019>s coefficient (also referred to as Blomqvist's Beta or medial correlation; Blomqvist, 1950) is a median-based non-parametric correlation that has some advantages over measures such as Spearman's or Kendall's estimates (see Shmid & Schimdt, 2006).
Hoeffding<U+2019>s D: The Hoeffding<U+2019>s D statistics is a non-parametric rank based measure of association that detects more general departures from independence (Hoeffding 1948), including non-linear associations. Hoeffding<U+2019>s D varies between -0.5 and 1 (if there are no tied ranks, otherwise it can have lower values), with larger values indicating a stronger relationship between the variables.
Somers<U+2019> D: The Somers<U+2019> D statistics is a non-parametric rank based measure of association between a binary variable and a continuous variable, for instance, in the context of logistic regression the binary outcome and the predicted probabilities for each outcome. Usually, Somers' D is a measure of ordinal association, however, this implementation it is limited to the case of a binary outcome.
Point-Biserial and biserial correlation: Correlation coefficient used when one variable is continuous and the other is dichotomous (binary). Point-Biserial is equivalent to a Pearson's correlation, while Biserial should be used when the binary variable is assumed to have an underlying continuity. For example, anxiety level can be measured on a continuous scale, but can be classified dichotomously as high/low.
Gamma correlation: The Goodman-Kruskal gamma statistic is similar to Kendall's Tau coefficient. It is relatively robust to outliers and deals well with data that have many ties.
Winsorized correlation: Correlation of variables that have been formerly Winsorized, i.e., transformed by limiting extreme values to reduce the effect of possibly spurious outliers.
Gaussian rank Correlation: The Gaussian rank correlation estimator is a simple and well-performing alternative for robust rank correlations (Boudt et al., 2012). It is based on the Gaussian quantiles of the ranks.
Polychoric correlation: Correlation between two theorized normally distributed continuous latent variables, from two observed ordinal variables.
Tetrachoric correlation: Special case of the polychoric correlation applicable when both observed variables are dichotomous.
Partial correlations are estimated as the correlation between two
variables after adjusting for the (linear) effect of one or more other
variable. The correlation test is then run after having partialized the
dataset, independently from it. In other words, it considers partialization
as an independent step generating a different dataset, rather than belonging
to the same model. This is why some discrepancies are to be expected for the
t- and p-values, CIs, BFs etc (but not the correlation coefficient)
compared to other implementations (e.g., ppcor). (The size of these
discrepancies depends on the number of covariates partialled-out and the
strength of the linear association between all variables.) Such partial
correlations can be represented as Gaussian Graphical Models (GGM), an
increasingly popular tool in psychology. A GGM traditionally include a set of
variables depicted as circles ("nodes"), and a set of lines that visualize
relationships between them, which thickness represents the strength of
association (see Bhushan et al., 2019).
Multilevel correlations are a special case of partial correlations where
the variable to be adjusted for is a factor and is included as a random
effect in a mixed model (note that the remaining continuous variables of the
dataset will still be included as fixed effects, similarly to regular partial
correlations). That said, there is an important difference between using
cor_test() and correlation(): If you set multilevel=TRUE in
correlation() but partial is set to FALSE (as per default), then a
back-transformation from partial to non-partial correlation will be attempted
(through pcor_to_cor()). However, this is not possible when
using cor_test() so that if you set multilevel=TRUE in it, the resulting
correlations are partial one. Note that for Bayesian multilevel correlations,
if partial = FALSE, the back transformation will also recompute p-values
based on the new r scores, and will drop the Bayes factors (as they are not
relevant anymore). To keep Bayesian scores, set partial = TRUE.
Kendall and Spearman correlations when bayesian=TRUE: These are technically
Pearson Bayesian correlations of rank transformed data, rather than pure
Bayesian rank correlations (which have different priors).
Boudt, K., Cornelissen, J., & Croux, C. (2012). The Gaussian rank correlation estimator: robustness properties. Statistics and Computing, 22(2), 471-483.
Bhushan, N., Mohnert, F., Sloot, D., Jans, L., Albers, C., & Steg, L. (2019). Using a Gaussian graphical model to explore relationships between items and variables in environmental psychology research. Frontiers in psychology, 10, 1050.
Bishara, A. J., & Hittner, J. B. (2017). Confidence intervals for correlations when data are not normal. Behavior research methods, 49(1), 294-309.
Fieller, E. C., Hartley, H. O., & Pearson, E. S. (1957). Tests for rank correlation coefficients. I. Biometrika, 44(3/4), 470-481.
Langfelder, P., & Horvath, S. (2012). Fast R functions for robust correlations and hierarchical clustering. Journal of statistical software, 46(11).
Blomqvist, N. (1950). On a measure of dependence between two random variables,Annals of Mathematical Statistics,21, 593<U+2013>600
Somers, R. H. (1962). A new asymmetric measure of association for ordinal variables. American Sociological Review. 27 (6).
# NOT RUN {
library(correlation)
results <- correlation(iris)
results
summary(results)
summary(results, redundant = TRUE)
# pipe-friendly usage with grouped dataframes from {dplyr} package
if (require("poorman")) {
iris %>%
correlation(select = "Petal.Width", select2 = "Sepal.Length")
# Grouped dataframe
# grouped correlations
iris %>%
group_by(Species) %>%
correlation()
# selecting specific variables for correlation
mtcars %>%
group_by(am) %>%
correlation(
select = c("cyl", "wt"),
select2 = c("hp")
)
}
# supplying custom variable names
correlation(anscombe, select = c("x1", "x2"), rename = c("var1", "var2"))
# automatic selection of correlation method
correlation(mtcars[-2], method = "auto")
# }
Run the code above in your browser using DataLab