| Title: | Taba Robust Correlations |
|---|---|
| Description: | Calculates the robust Taba linear, Taba rank (monotonic), TabWil, and TabWil rank correlations. Test statistics as well as one sided or two sided p-values are provided for all correlations. Multiple correlations and p-values can be calculated simultaneously across multiple variables. In addition, users will have the option to use the partial, semipartial, and generalized partial correlations; where the partial and semipartial correlations use linear, logistic, or Poisson regression to modify the specified variable. |
| Authors: | Mohammad Tabatabai [aut], Derek Wilus [aut, cre] |
| Maintainer: | Derek Wilus <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.0 |
| Built: | 2024-12-10 06:42:49 UTC |
| Source: | CRAN |
Returns the Taba robust linear, Taba rank (monotonic), TabWil, or TabWil rank correlation coefficient between two numeric vectors.
taba(x, y, method = c("taba", "tabarank","tabwil", "tabwilrank"), omega)taba(x, y, method = c("taba", "tabarank","tabwil", "tabwilrank"), omega)
x |
A numeric vector of length greater than 2 must be same length as y |
y |
A numeric vector of length greater than 2 must be same length as x |
method |
A character string of |
omega |
Numeric allowing the user to alter the tuning constant. If one is not specified, the function will default to 0.45 for Taba and Taba rank, and 0.1 for TabWil and TabWil rank. Range is between 0 and 1. |
This function can be used to compare two non-empty numeric vectors of
length greater than two, or two columns of a data frame or matrix composed
of more than two numeric elements. Missing values in either x or y are
deleted row-wise. The default method is Taba Linear correlation, with the
tuning constant omega.
This function returns a the robust linear or monotonic association between two numeric vectors as a numeric.
Tabatabai, M., Bailey, S., Bursac, Z. et al. An introduction to new robust linear
and monotonic correlation coefficients. BMC Bioinformatics 22, 170 (2021). https://doi.org/10.1186/s12859-021-04098-4
doi:10.1186/s12859-021-04098-4
taba.test for testing Taba linear or Taba rank (monotonic) correlations
taba.partial for partial and semipartial correlations
taba.gpartial for generalized partial correlations
taba.matrix for calculating correlation, p-value, and distance matricies
x = rnorm(100) y = rnorm(100) taba(x, y) taba(x, y, method = "tabarank", omega = 0.4) taba(x, y, method = "tabwil", omega = 0.22)x = rnorm(100) y = rnorm(100) taba(x, y) taba(x, y, method = "tabarank", omega = 0.4) taba(x, y, method = "tabwil", omega = 0.22)
Calculates a generalized partial correlation using one of the specified robust methods Taba linear or Taba rank correlation.
taba.gpartial(x, y, xcov, ycov, regress.x, regress.y, method = c("taba","tabarank","tabwil","tabwilrank"), alternative = c("less", "greater", "two.sided"), omega)taba.gpartial(x, y, xcov, ycov, regress.x, regress.y, method = c("taba","tabarank","tabwil","tabwilrank"), alternative = c("less", "greater", "two.sided"), omega)
x |
A numeric vector of length greater than 2 must be same length as y and covariates listed in x and ycov |
y |
A numeric vector of length greater than 2 must be same length as x and covariates listed in y and xcov |
xcov |
A data frame, matrix, or numeric vectors combined columnwize used as covariates for x, which have length equal to x |
ycov |
A data frame, matrix, or numeric vectors combined columnwize used as covariates for y, which have length equal to y |
regress.x |
A string variable " |
regress.y |
A string variable " |
method |
A character string of |
alternative |
Character string specifying the alternative hypothesis must be one
of |
omega |
Numeric allowing the user to alter the tuning constant. If one is not specified, the function will default to 0.45 for Taba and Taba rank, and 0.1 for TabWil and TabWil rank. Range is between 0 and 1. |
This function generalizes the partial correlation. In the event that the controlling
variables for x and y are identical, it reduces to Taba, Taba rank, TabWil, and TabWil
rank partial correlation. Covariates used to control for x
should be represented columnwise in a matrix or data frame as xcov. Similarly,
covariates used to control for y should be represented columnwise in a matrix or
data frame as ycov. When controling an outcome variable with one covariate,
a vector will suffice. Because x and y refer to the outcome varibales, names of
covariates (or control variables) must not be named "x" or "y". The user has the
option of using different regression methods when controling each outcome variable.
Missing values in x, y, or any of the covariates are deleted row-wise. All categorical
variables must be converted to type factor prior to using this function.
The default for this function is a two sided test using generalized partial Taba
correlation using a linear regression to obtain residuals, with the tuning
constant omega equal to 0.45.
This function returns the robust association between two numeric vectors, adjusting for specified covariates. In addition, this function can provide the semipartial correlation, if specified.
Tabatabai, M., Bailey, S., Bursac, Z. et al. An introduction to new robust linear
and monotonic correlation coefficients. BMC Bioinformatics 22, 170 (2021). https://doi.org/10.1186/s12859-021-04098-4
doi:10.1186/s12859-021-04098-4
taba for calculating Taba linear or Taba rank (monotonic) correlations
taba.test for testing Taba linear or Taba rank Monotonic correlations
taba.partial for partial and semipartial correlations
taba.matrix for calculating correlation, p-value, and distance matricies
x = rnorm(100) y = rnorm(100) z1 = rnorm(100) z2 = rnorm(100) z3 = rnorm(100) w = sample(c(0,1), replace=TRUE, size=100) taba.gpartial(x, y, xcov = cbind(z1, z2), ycov = cbind(z1, z3), method = "tabarank") taba.gpartial(x, y, z2, ycov = cbind(z1, z2), alternative = "less") taba.gpartial(w, y, z1, cbind(z2, z3),regress.x = "logistic")x = rnorm(100) y = rnorm(100) z1 = rnorm(100) z2 = rnorm(100) z3 = rnorm(100) w = sample(c(0,1), replace=TRUE, size=100) taba.gpartial(x, y, xcov = cbind(z1, z2), ycov = cbind(z1, z3), method = "tabarank") taba.gpartial(x, y, z2, ycov = cbind(z1, z2), alternative = "less") taba.gpartial(w, y, z1, cbind(z2, z3),regress.x = "logistic")
Calculates a correlation, distance, and p-value matrix using one of the specified robust methods Taba linear or Taba rank correlation.
taba.matrix(x, y = NULL, ..., method = c("taba","tabarank","tabwil","tabwilrank"), alternative = c("less", "greater", "two.sided"), omega)taba.matrix(x, y = NULL, ..., method = c("taba","tabarank","tabwil","tabwilrank"), alternative = c("less", "greater", "two.sided"), omega)
x |
A numeric vector of length greater than 2 must be same length as all other vectors. |
y |
A numeric vector of length greater than 2 must be same length as all other vectors. |
... |
Numeric vector(s) of length equal to x and y. May be of class matrix or data.frame, whose columns will be compared and whose column's length must be of equal length to x and y. Not one vector or column name can be "x" or "y." |
method |
A character string of |
alternative |
Character string specifying the alternative hypothesis must be one
of |
omega |
Numeric allowing the user to alter the tuning constant. If one is not specified, the function will default to 0.45 for Taba and Taba rank, and 0.1 for TabWil and TabWil rank. Range is between 0 and 1. |
This function uses Taba linear, Taba rank (monotonic), TabWil, or TabWil rank correlation to
calculate the association of two or more numeric vectors. Numeric vectors under ...
are combined colomn-wise with x and y. When inserting a single matrix x, the function will
calculate the correlation matix using the columns of matrix x.
Matricies or data frames with numeric cells can be inserted in ..., whereby
each column in the matrix or data frame will be treated as a different vector
for comparison. Columns must all have different names from each other. No vector
or column should be named "x" or "y," as these refer to the first two vectors respectively,
if inserted as a vector or matrix with no name. Missing values in any of the vectors
are deleted row-wise.
The default for this function is a two sided test using Taba linear partial correlation,
with the tuning constant omega equal to 0.45.
This function returns the robust association between two or more numeric vectors, as a matrix; the distance matrix, as type dist; and a p-value matrix corresponding to the correlation matrix.
Tabatabai, M., Bailey, S., Bursac, Z. et al. An introduction to new robust linear
and monotonic correlation coefficients. BMC Bioinformatics 22, 170 (2021). https://doi.org/10.1186/s12859-021-04098-4
doi:10.1186/s12859-021-04098-4
taba for calculating Taba linear or Taba rank (monotonic) correlations
taba.test for testing Taba linear or Taba rank (monotonic) correlations
taba.gpartial for generalized partial correlations
taba.partial for partial and semipartial correlations
x = rnorm(100) y = rnorm(100) z1 = rnorm(100) z2 = rnorm(100) z3 = rnorm(100) Z = cbind(z1,z3) colnames(Z) = c("A","B") taba.matrix(x, y, z1, z2, z3, method = "tabarank") taba.matrix(x, y, z2, Z, alternative = "less", omega = 0.4) taba.matrix(Z, method = "tabarank")x = rnorm(100) y = rnorm(100) z1 = rnorm(100) z2 = rnorm(100) z3 = rnorm(100) Z = cbind(z1,z3) colnames(Z) = c("A","B") taba.matrix(x, y, z1, z2, z3, method = "tabarank") taba.matrix(x, y, z2, Z, alternative = "less", omega = 0.4) taba.matrix(Z, method = "tabarank")
Calculates a partial or semipartial correlation using one of the specified robust methods Taba linear or Taba rank correlation.
taba.partial(x, y, ..., regress, method = c("taba", "tabarank", "tabwil", "tabwilrank"), alternative = c("less", "greater", "two.sided"), semi = c("none", "x", "y"), omega)taba.partial(x, y, ..., regress, method = c("taba", "tabarank", "tabwil", "tabwilrank"), alternative = c("less", "greater", "two.sided"), semi = c("none", "x", "y"), omega)
x |
A numeric vector of length greater than 2 must be same length as y and covariates listed in ... |
y |
A numeric vector of length greater than 2 must be same length as x and covariates listed in ... |
... |
Numeric vectors used as covariates of length equal to x and y |
regress |
A string variable " |
method |
A character string of |
alternative |
Character string specifying the alternative hypothesis must be one
of |
semi |
A character string specifying which variable (x or y) should be adjusted. |
omega |
Numeric allowing the user to alter the tuning constant. If one is not specified, the function will default to 0.45 for Taba and Taba rank, and 0.1 for TabWil and TabWil rank. Range is between 0 and 1. |
This function calculates the partial or semipartial association of two
numeric vectors, or columns of a matrix or data frame composed
of more than two numeric elements, adjusting for covariates of length equal to
x and y. Covariates are combined colomn-wise and can be numeric vectors, matricies,
or data frames with numeric cells. Each column in the matrix or data frame will be
treated as a different covariate, and must have different names from x and y.
Missing values in x, y, or any of the covariates are deleted row-wise.
The default for this function is a two sided test using Taba linear partial
correlation, with the tuning constant omega equal to 0.45 for Taba and
Taba rank, and 0.1 for TabWil and TabWil rank. Range is between 0 and 1.
The variable you are not controlling must be continuous when using semipartial correlation.
This function returns the robust association between two numeric vectors, adjusting for specified covariates. In addition, this function can provide the semipartial correlation, if specified.
Tabatabai, M., Bailey, S., Bursac, Z. et al. An introduction to new robust linear
and monotonic correlation coefficients. BMC Bioinformatics 22, 170 (2021). https://doi.org/10.1186/s12859-021-04098-4
doi:10.1186/s12859-021-04098-4
taba for calculating Taba linear or Taba rank (monotonic) correlations
taba.test for testing Taba linear or Taba rank (monotonic) correlations
taba.gpartial for generalized partial correlations
taba.matrix for calculating correlation, p-value, and distance matricies
x = rnorm(100) y = rnorm(100) z1 = rnorm(100) z2 = rnorm(100) z3 = rnorm(100) taba.partial(x, y, z1, z2, z3, method = "tabwilrank") taba.partial(x, y, z2, alternative = "less", semi = "x")x = rnorm(100) y = rnorm(100) z1 = rnorm(100) z2 = rnorm(100) z3 = rnorm(100) taba.partial(x, y, z1, z2, z3, method = "tabwilrank") taba.partial(x, y, z2, alternative = "less", semi = "x")
Tests the association between two numeric vectors using Taba robust linear, Taba rank (monotonic), TabWil, or TabWil rank correlation coefficient.
taba.test(x, y, method = c("taba", "tabarank", "tabwil", "tabwilrank"), alternative = c("less", "greater", "two.sided"), omega, alpha = 0.05)taba.test(x, y, method = c("taba", "tabarank", "tabwil", "tabwilrank"), alternative = c("less", "greater", "two.sided"), omega, alpha = 0.05)
x |
A numeric vector of length greater than 2 must be same length as y |
y |
A numeric vector of length greater than 2 must be same length as x |
method |
A character string of |
alternative |
Character string specifying the alternative hypothesis must be one
of |
omega |
Numeric allowing the user to alter the tuning constant. If one is not specified, the function will default to 0.45 for Taba and Taba rank, and 0.1 for TabWil and TabWil rank. Range is between 0 and 1. |
alpha |
Type I error rate. Numeric must be between 0 and 1. Default set to 0.05. |
This function tests the association of two non-empty numeric vectors of
length greater than two, or two columns of a data frame or matrix composed
of more than two numeric elements. Covariates are combined colomn-wise and can be
numeric vectors, matricies, or data frames with numeric cells. Each column in the
matrix or data frame will be treated as a different covariate, and must have
different names. Missing values in either x or y are deleted row-wise. The two sided
test with the null hypothesis correlation is equal to zero. The default is a two
sided test using Taba Linear correlation, with tuning constant omega.
This function returns the robust linear or monotonic association between two numeric vectors, along with it's respective test statistic, and p-value.
Tabatabai, M., Bailey, S., Bursac, Z. et al. An introduction to new robust linear
and monotonic correlation coefficients. BMC Bioinformatics 22, 170 (2021). https://doi.org/10.1186/s12859-021-04098-4
doi:10.1186/s12859-021-04098-4
taba for calculating Taba linear or Taba rank (monotonic) correlations
taba.partial for partial and semipartial correlations
taba.gpartial for generalized partial correlations
taba.matrix for calculating correlation, p-value, and distance matricies
x = rnorm(10) y = rnorm(10) taba.test(x, y) taba.test(x, y, method = "tabarank", alternative = "less")$p.value taba.test(x, y, method = "tabwil", omega = .1)x = rnorm(10) y = rnorm(10) taba.test(x, y) taba.test(x, y, method = "tabarank", alternative = "less")$p.value taba.test(x, y, method = "tabwil", omega = .1)