CCI (version 0.3.6)

perm.test: Permutation Test for Conditional Independence

Description

Permutation Test for Conditional Independence

Usage

perm.test(
  formula,
  data,
  p = 0.5,
  nperm = 160,
  subsample = 1,
  metric = "RMSE",
  method = "rf",
  nrounds = 600,
  mtry = NULL,
  parametric = FALSE,
  tail = NA,
  robust = TRUE,
  metricfunc = NULL,
  mlfunc = NULL,
  nthread = 1,
  progress = TRUE,
  k = 15,
  center = TRUE,
  scale = TRUE,
  eps = 1e-15,
  positive = NULL,
  kernel = "optimal",
  distance = 2,
  ...
)

Value

An object of class 'CCI' containing the null distribution, observed test statistic, p-values, the machine learning model used, and the data.

Arguments

formula

Model formula or DAGitty object specifying the relationship between dependent and independent variables.

data

A data frame containing the variables specified in the formula.

p

Proportion of data to use for training the model. Default is 0.5.

nperm

Number of permutations to perform. Default is 160.

subsample

The proportion of the data to be used. Default is 1 (no subsampling).

metric

Type of metric: "RMSE", "Kappa" or "LogLoss". Default is 'RMSE'.

method

The machine learning method to use for the learner. Supported methods include "rf", "xgboost", "KNN" and "svm". Default is "rf".

nrounds

Number of rounds (trees) for methods 'xgboost' and 'rf'. Default is 600.

mtry

Number of variables to possibly split at in each node for method 'rf'. Default is NULL (sqrt of number of variables).

parametric

Logical. If TRUE, a parametric p-value is calculated instead of an empirical p-value. Default is FALSE.

tail

Specifies whether the test is one-tailed ("left" or "right") or two-tailed. Default is NA.

robust

Logical. If TRUE, uses a robust method for permutation. Default is TRUE.

metricfunc

An optional custom function to calculate the performance metric based on the model's predictions. Default is NULL.

mlfunc

An optional custom machine learning function to use instead of the predefined methods. Default is NULL.

nthread

Integer. The number of threads to use for parallel processing for method 'rf' and 'xgboost'. Default is 1.

progress

Logical. If TRUE, a progress bar is displayed during the permutation process. Default is TRUE.

k

Integer. The number of nearest neighbors for the "KNN" method. Default is 15.

center

Logical. If TRUE, the data is centered before model fitting. Default is TRUE.

scale

Logical. If TRUE, the data is scaled before model fitting. Default is TRUE.

eps

Numeric. A small value added to avoid division by zero. Default is 1e-15.

positive

Character vector. Specifies which levels of a factor variable should be treated as positive class in classification tasks. Default is NULL.

kernel

Character string specifying the kernel type for method option "KNN" . Possible choices are "rectangular" (which is standard unweighted knn), "triangular", "epanechnikov" (or beta(2,2)), "biweight" (or beta(3,3)), "triweight" (or beta(4,4)), "cos", "inv", "gaussian" and "optimal". Default is "optimal".

distance

Numeric. Parameter of Minkowski distance for the "KNN" method. Default is 2.

...

Additional arguments to pass to the machine learning model fitting function.

See Also

print.CCI, summary.CCI, plot.CCI, QQplot

Examples

set.seed(123)
dat <- data.frame(x1 = rnorm(100),
x2 = rnorm(100),
x3 = rnorm(100),
x4 = rnorm(100),
y = rnorm(100))
perm.test(y ~ x1 | x2 + x3 + x4, data = dat, nperm = 25)