Type: Package
Title: Covariance Measure Tests for Conditional Independence
Version: 0.1-1
Description: Covariance measure tests for conditional independence testing against conditional covariance and nonlinear conditional mean alternatives. The package implements versions of the generalised covariance measure test (Shah and Peters, 2020, <doi:10.1214/19-aos1857>) and projected covariance measure test (Lundborg et al., 2023, <doi:10.1214/24-AOS2447>). The tram-GCM test, for censored responses, is implemented including the Cox model and survival forests (Kook et al., 2024, <doi:10.1080/01621459.2024.2395588>). Application examples to variable significance testing and modality selection can be found in Kook and Lundborg (2024, <doi:10.1093/bib/bbae475>).
Depends: R (≥ 4.2.0)
Imports: ranger, glmnet, Formula, survival, coin
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.1
Suggests: testthat (≥ 3.0.0), ggplot2, tidyr, dplyr, xgboost
Config/testthat/edition: 3
URL: https://github.com/LucasKook/comets
BugReports: https://github.com/LucasKook/comets/issues
NeedsCompilation: no
Packaged: 2025-01-31 09:33:41 UTC; lkook
Author: Lucas Kook ORCID iD [aut, cre], Anton Rask Lundborg [ctb]
Maintainer: Lucas Kook <lucasheinrich.kook@gmail.com>
Repository: CRAN
Date/Publication: 2025-01-31 10:10:06 UTC

Covariance measure tests with formula interface

Description

Covariance measure tests with formula interface

Usage

comet(formula, data, test = c("gcm", "pcm", "wgcm"), ...)

comets(formula, data, test = c("gcm", "pcm", "wgcm"), ...)

Arguments

formula

Formula of the form Y ~ X | Z for testing Y independent of X given Z.

data

Data.frame containing the variables in formula.

test

Character string; "gcm", "pcm", or "wgcm".

...

Additional arguments passed to test.

Details

Formula-based interface for the generalised and projected covariance measure tests.

Value

Object of class "gcm", "wgcm" or "pcm" and "htest". See gcm and pcm for details.

References

Kook, L. & Lundborg A. R. (2024). Algorithm-agnostic significance testing in supervised learning with multimodal data. Briefings in Bioinformatics, 25(6), 2024. doi:10.1093/bib/bbae475

Examples

tn <- 1e2
df <- data.frame(y = rnorm(tn), x1 = rnorm(tn), x2 = rnorm(tn), z = rnorm(tn))
comet(y ~ x1 + x2 | z, data = df, test = "gcm")

Generalised covariance measure test

Description

Generalised covariance measure test

Usage

gcm(
  Y,
  X,
  Z,
  alternative = c("two.sided", "less", "greater"),
  reg_YonZ = "rf",
  reg_XonZ = "rf",
  args_YonZ = NULL,
  args_XonZ = NULL,
  type = c("quadratic", "max", "scalar"),
  B = 499L,
  coin = TRUE,
  cointrol = list(distribution = "asymptotic"),
  return_fitted_models = FALSE,
  multivariate = c("none", "YonZ", "XonZ", "both"),
  ...
)

Arguments

Y

Vector or matrix of response values.

X

Matrix or data.frame of covariates.

Z

Matrix or data.frame of covariates.

alternative

A character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". Only applies if type = "quadratic" and Y and X are one-dimensional.

reg_YonZ

Character string or function specifying the regression for Y on Z. See ?regressions for more detail.

reg_XonZ

Character string or function specifying the regression for X on Z. See ?regressions for more detail.

args_YonZ

A list of named arguments passed to reg_YonZ.

args_XonZ

A list of named arguments passed to reg_XonZ.

type

Type of test statistic, either "quadratic" (default) or "max". If "max" is specified, the p-value is computed based on a bootstrap approximation of the null distribution with B samples.

B

Number of bootstrap samples. Only applies if type = "max" is used.

coin

Logical; whether or not to use the coin package for computing the test statistic and p-value. The coin package computes variances with n - 1 degrees of freedom. The default is TRUE.

cointrol

List; further arguments passed to independence_test.

return_fitted_models

Logical; whether to return the fitted regressions (default is FALSE).

multivariate

Character; specifying which regression can handle multivariate outcomes ("none", "YonZ", "XonZ", or "both"). If "none", then the regression is run using each column in Y (or X) as the response.

...

Additional arguments passed to reg_YonZ.

Details

The generalised covariance measure test tests whether the conditional covariance of Y and X given Z is zero.

Value

Object of class 'gcm' and 'htest' with the following components:

statistic

The value of the test statistic.

p.value

The p-value for the hypothesis

parameter

In case X is multidimensional, this is the degrees of freedom used for the chi-squared test.

hypothesis

String specifying the null hypothesis.

null.value

String specifying the null hypothesis.

method

The string "Generalised covariance measure test".

data.name

A character string giving the name(s) of the data.

rY

Residuals for the Y on Z regression.

rX

Residuals for the X on Z regression.

models

List of fitted regressions if return_fitted_models is TRUE.

References

Rajen D. Shah, Jonas Peters "The hardness of conditional independence testing and the generalised covariance measure," The Annals of Statistics, 48(3), 1514-1538. doi:10.1214/19-aos1857

Examples

n <- 1e2
X <- matrix(rnorm(2 * n), ncol = 2)
colnames(X) <- c("X1", "X2")
Z <- matrix(rnorm(2 * n), ncol = 2)
colnames(Z) <- c("Z1", "Z2")
Y <- X[, 2]^2 + Z[, 2] + rnorm(n)
(gcm1 <- gcm(Y, X, Z))

Projected covariance measure test for conditional mean independence

Description

Projected covariance measure test for conditional mean independence

Usage

pcm(
  Y,
  X,
  Z,
  rep = 1,
  est_vhat = TRUE,
  reg_YonXZ = "rf",
  reg_YonZ = "rf",
  reg_YhatonZ = "rf",
  reg_VonXZ = "rf",
  reg_RonZ = "rf",
  args_YonXZ = NULL,
  args_YonZ = NULL,
  args_YhatonZ = NULL,
  args_VonXZ = NULL,
  args_RonZ = NULL,
  frac = 0.5,
  indices = NULL,
  coin = FALSE,
  cointrol = NULL,
  return_fitted_models = FALSE,
  ...
)

Arguments

Y

Vector of response values. Can be supplied as a numeric vector or a single column matrix.

X

Matrix or data.frame of covariates.

Z

Matrix or data.frame of covariates.

rep

Number of repetitions with which to repeat the PCM test

est_vhat

Logical; whether to estimate the variance functional

reg_YonXZ

Character string or function specifying the regression for Y on X and Z, default is "rf" for random forest. See ?regressions for more detail.

reg_YonZ

Character string or function specifying the regression for Y on Z, default is "rf" for random forest. See ?regressions for more detail.

reg_YhatonZ

Character string or function specifying the regression for the predicted values of reg_YonXZ on Z, default is "rf" for random forest. See ?regressions for more detail.

reg_VonXZ

Character string or function specifying the regression for estimating the conditional variance of Y given X and Z, default is "rf" for random forest. See ?regressions for more detail.

reg_RonZ

Character string or function specifying the regression for the estimated transformation of Y, X, and Z on Z, default is "rf" for random forest. See ?regressions for more detail.

args_YonXZ

A list of named arguments passed to reg_YonXZ.

args_YonZ

A list of named arguments passed to reg_YonZ.

args_YhatonZ

A list of named arguments passed to reg_YhatonZ.

args_VonXZ

A list of named arguments passed to reg_VonXZ.

args_RonZ

A list of named arguments passed to reg_RonZ.

frac

Relative size of train split.

indices

A numeric vector of indices specifying the observations used for estimating the estimating the direction (the other observations will be used for computing the final test statistic). Default is NULL and the indices will be generated randomly using frac. When using rep larger than 1, a list (of length rep) of indices can be supplied.

coin

Logical; whether or not to use the coin package for computing the test statistic and p-value. The coin package computes variances with n - 1 degrees of freedom. The default is TRUE.

cointrol

List; further arguments passed to independence_test.

return_fitted_models

Logical; whether to return the fitted regressions (default is FALSE).

...

Additional arguments currently ignored.

Details

The projected covariance measure test tests whether the conditional mean of Y given X and Z is independent of X.

Value

Object of class 'pcm' and 'htest' with the following components:

statistic

The value of the test statistic.

p.value

The p-value for the hypothesis

parameter

In case X is multidimensional, this is the degrees of freedom used for the chi-squared test.

hypothesis

Null hypothesis of conditional mean independence.

null.value

Null hypothesis of conditional mean independence.

method

The string "Projected covariance measure test".

data.name

A character string giving the name(s) of the data.

check.data

A data.frame containing the residuals for plotting.

models

List of fitted regressions if return_fitted_models is TRUE.

References

Lundborg, A. R., Kim, I., Shah, R. D., & Samworth, R. J. (2022). The Projected Covariance Measure for assumption-lean variable significance testing. arXiv preprint. doi:10.48550/arXiv.2211.02039

Examples

n <- 1e2
X <- matrix(rnorm(2 * n), ncol = 2)
colnames(X) <- c("X1", "X2")
Z <- matrix(rnorm(2 * n), ncol = 2)
colnames(Z) <- c("Z1", "Z2")
Y <- X[, 2]^2 + Z[, 2] + rnorm(n)
(pcm1 <- pcm(Y, X, Z))

Equivalence test for the parameter in a partially linear model

Description

Equivalence test for the parameter in a partially linear model

Usage

plm_equiv_test(Y, X, Z, from, to, scale = c("plm", "cov", "cor"), ...)

Arguments

Y

Vector or matrix of response values.

X

Matrix or data.frame of covariates.

Z

Matrix or data.frame of covariates.

from

Lower bound of the equivalence margin

to

Upper bound of the equivalence margin

scale

Scale on which to specify the equivalence margin. Default "plm" corresponds to the partially linear model parameter described in the details. "cov" corresponds to the conditional covariance and "cor" to conditional correlation which lies in [-1, 1].

...

Further arguments passed to gcm

Details

The partially linear model postulates

Y = X \theta + g(Z) + \epsilon,

and the target of inference is theta. The target is closely related to the conditional covariance between Y and X given Z:

\theta = E[cov(X, Y | Z)] / E[Var(X | Z)].

The equivalence test (based on the GCM test) tests H_0: \theta \not\in [{\tt from}, {\tt to}] versus H_1: \theta \in [{\tt from}, {\tt to}]. Y, X (and theta) can only be one-dimensional. There are no restrictions on Z. The equivalence test can also be performed on the conditional covariance scale directly (using scale = "cov") or on the conditional correlation scale:

E[cov(X, Y | Z)] / \sqrt{E[Var(X | Z)]E[Var(Y | Z)]}

, using scale = "cor".

Value

Object of class 'gcm' and 'htest'

Examples

n <- 150
X <- rnorm(n)
Z <- matrix(rnorm(2 * n), ncol = 2)
colnames(Z) <- c("Z1", "Z2")
Y <- X^2 + Z[, 2] + rnorm(n)
plm_equiv_test(Y, X, Z, from = -1, to = 1)

Plotting methods for COMETs

Description

Plotting methods for COMETs

Usage

## S3 method for class 'gcm'
plot(x, plot = TRUE, ...)

## S3 method for class 'pcm'
plot(x, plot = TRUE, ...)

## S3 method for class 'wgcm'
plot(x, plot = TRUE, ...)

Arguments

x

Object of class 'gcm', 'pcm', or 'wgcm'.

plot

Logical; whether to print the plot (default: TRUE).

...

Currently ignored.

Implemented regression methods

Description

Implemented regression methods

Usage

rf(y, x, ...)

survforest(y, x, ...)

qrf(y, x, ...)

lrm(y, x, ...)

glrm(y, x, ...)

lasso(y, x, ...)

ridge(y, x, ...)

postlasso(y, x, ...)

cox(y, x, ...)

tuned_rf(
  y,
  x,
  max.depths = 1:5,
  mtrys = list(1, function(p) ceiling(sqrt(p)), identity),
  verbose = FALSE,
  ...
)

xgb(y, x, nrounds = 2, verbose = 0, ...)

tuned_xgb(
  y,
  x,
  etas = c(0.1, 0.5, 1),
  max_depths = 1:5,
  nfold = 5,
  nrounds = c(2, 10, 50),
  verbose = 0,
  metrics = list("rmse"),
  ...
)

Arguments

y

Vector (or matrix) of response values.

x

Design matrix of predictors.

...

Additional arguments passed to the underlying regression method. In case of "rf", "tuned_rf", "survforest" and "qrf", this is ranger. In case of "lasso" and "ridge", this is glmnet. In case of "cox", this is coxph. In case of "xgb" and "tuned_xgb" this is xgboost.

max.depths

Values for max.depth to tune out-of-bag. See ranger.

mtrys

for mtry to tune out-of-bag. See ranger.

verbose

See xgboost.

nrounds

See xgboost.

etas

Values for eta to cross-validate. See xgboost.

max_depths

Values for max_depth to cross-validate. See xgboost.

nfold

Number of folds for nfold-cross validation.

metrics

See xgboost.

Details

The implemented choices are "rf" for random forests as implemented in ranger, "lasso" for cross-validated Lasso regression (using the one-standard error rule), "ridge" for cross-validated ridge regression (using the one-standard error rule), "cox" for the Cox proportional hazards model as implemented in survival, "qrf" or "survforest" for quantile and survival random forests, respectively. The option "postlasso" option refers to a cross-validated LASSO (using the one-standard error rule) and subsequent OLS regression. The "lrm" option implements a standard linear regression model. The "xgb" and "tuned_xgb" options require the xgboost package.

The "tuned_rf" regression method tunes the mtry and max.depth parameters in ranger out-of-bag. The "tuned_xgb" regression method uses k-fold cross-validation to tune the nrounds, mtry and max_depth parameters in xgb.cv.

New regression methods can be implemented and supplied as well and need the following structure. The regression method "custom_reg" needs to take arguments y, x, ..., fit the model using y and x as matrices and return an object of a user-specified class, for instance, 'custom'. For the GCM test, implementing a residuals.custom method is sufficient, which should take arguments object, response = NULL, data = NULL, .... For the PCM test, a predict.custom method is necessary for out-of-sample prediction and computation of residuals.

GCM test with pre-computed residuals

Description

GCM test with pre-computed residuals

Usage

rgcm(
  rY,
  rX,
  alternative = "two.sided",
  type = c("quadratic", "max", "scalar"),
  ...
)

Arguments

rY

Vector or matrix of response values.

rX

Matrix or data.frame of covariates.

alternative

A character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". Only applies if type = "quadratic" and Y and X are one-dimensional.

type

Type of test statistic, either "quadratic" (default) or "max". If "max" is specified, the p-value is computed based on a bootstrap approximation of the null distribution with B samples.

...

Further arguments passed to independence_test().

Value

Object of class 'gcm' and 'htest' with the following components:

statistic

The value of the test statistic.

p.value

The p-value for the hypothesis

parameter

In case X is multidimensional, this is the degrees of freedom used for the chi-squared test.

hypothesis

String specifying the null hypothesis.

null.value

String specifying the null hypothesis.

method

The string "Generalised covariance measure test".

data.name

A character string giving the name(s) of the data.

rY

Residuals for the Y on Z regression.

rX

Residuals for the X on Z regression.

Weighted Generalised covariance measure test

Description

Weighted Generalised covariance measure test

Usage

wgcm(
  Y,
  X,
  Z,
  reg_YonZ = "rf",
  reg_XonZ = "rf",
  reg_wfun = "rf",
  args_YonZ = NULL,
  args_XonZ = NULL,
  args_wfun = NULL,
  frac = 0.5,
  B = 499L,
  coin = TRUE,
  cointrol = NULL,
  return_fitted_models = FALSE,
  multivariate = c("none", "YonZ", "XonZ", "both"),
  ...
)

Arguments

Y

Vector of response values. Can be supplied as a numeric vector or a single column matrix.

X

Matrix or data.frame of covariates.

Z

Matrix or data.frame of covariates.

reg_YonZ

Character string or function specifying the regression for Y on Z. See ?regressions for more detail.

reg_XonZ

Character string or function specifying the regression for X on Z. See ?regressions for more detail.

reg_wfun

Character string or function specifying the regression for estimating the weighting function. See ?regressions for more detail.

args_YonZ

A list of named arguments passed to reg_YonZ.

args_XonZ

A list of named arguments passed to reg_XonZ.

args_wfun

Additional arguments passed to reg_XonZ.

frac

Relative size of train split.

B

Number of bootstrap samples. Only applies if type = "max" is used.

coin

Logical; whether or not to use the coin package for computing the test statistic and p-value. The coin package computes variances with n - 1 degrees of freedom. The default is TRUE.

cointrol

List; further arguments passed to independence_test.

return_fitted_models

Logical; whether to return the fitted regressions (default is FALSE).

multivariate

Character; specifying which regression can handle multivariate outcomes ("none", "YonZ", "XonZ", or "both"). If "none", then the regression is run using each column in Y (or X) as the response.

...

Additional arguments currently ignored.

Details

The weighted generalised covariance measure test tests whether a weighted version of the conditional covariance of Y and X given Z is zero.

Value

Object of class 'wgcm' and 'htest' with the following components:

statistic

The value of the test statistic.

p.value

The p-value for the hypothesis

parameter

In case X is multidimensional, this is the degrees of freedom used for the chi-squared test.

hypothesis

String specifying the null hypothesis .

null.value

String specifying the null hypothesis.

method

The string "Generalised covariance measure test".

data.name

A character string giving the name(s) of the data.

rY

Residuals for the Y on Z regression.

rX

Weighted residuals for the X on Z regression.

W

Estimated weights.

models

List of fitted regressions if return_fitted_models is TRUE.

References

Scheidegger, C., Hörrmann, J., & Bühlmann, P. (2022). The weighted generalised covariance measure. Journal of Machine Learning Research, 23(273), 1-68.

Examples

n <- 100
X <- matrix(rnorm(2 * n), ncol = 2)
colnames(X) <- c("X1", "X2")
Z <- matrix(rnorm(2 * n), ncol = 2)
colnames(Z) <- c("Z1", "Z2")
Y <- X[, 2]^2 + Z[, 2] + rnorm(n)
(wgcm1 <- wgcm(Y, X, Z))