bartMachine: linearity_test – R documentation

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linearity_test

Test of Linearity

Description

Test to investigate H_0: the functional relationship between the response and the regressors is linear. We fit a linear model and then test if the residuals are a function of the regressors using the

Usage

linearity_test(lin_mod = NULL, X = NULL, y = NULL, 
	num_permutation_samples = 100, plot = TRUE, ...)

Arguments

`lin_mod`	A linear model you can pass in if you do not want to use the default which is `lm(y ~ X)`. Default is `NULL` which should be used if you pass in `X` and `y`.
`X`	Data frame of predictors. Factors are automatically converted to dummies internally. Default is `NULL` which should be used if you pass in `lin_mode`.
`y`	Vector of response variable. If `y` is `numeric` or `integer`, a BART model for regression is built. If `y` is a factor with two levels, a BART model for classification is built. Default is `NULL` which should be used if you pass in `lin_mode`.
`num_permutation_samples`	This function relies on `cov_importance_test` (see documentation there for details).
`plot`	This function relies on `cov_importance_test` (see documentation there for details).
`...`	Additional parameters to be passed to `bartMachine`, the model constructed on the residuals of the linear model.

Value

`permutation_samples_of_error`	This function relies on `cov_importance_test` (see documentation there for details).
`observed_error_estimate`	This function relies on `cov_importance_test` (see documentation there for details).
`pval`	The approximate p-value for this test. See the documentation at `cov_importance_test`.

Author(s)

Adam Kapelner

Examples

## Not run: 
##regression example

##generate Friedman data i.e. a nonlinear response model
set.seed(11)
n  = 200 
p = 5
X = data.frame(matrix(runif(n * p), ncol = p))
y = 10 * sin(pi* X[ ,1] * X[,2]) +20 * (X[,3] -.5)^2 + 10 * X[ ,4] + 5 * X[,5] + rnorm(n)

##now test if there is a nonlinear relationship between X1, ..., X5 and y.
linearity_test(X = X, y = y)
## note the plot and the printed p-value.. should be approx 0

#generate a linear response model
y = 1 * X[ ,1] + 3 * X[,2] + 5 * X[,3] + 7 * X[ ,4] + 9 * X[,5] + rnorm(n)
linearity_test(X = X, y = y)
## note the plot and the printed p-value.. should be > 0.05


## End(Not run)

bartMachine

Bayesian Additive Regression Trees

v1.2.6

GPL-3

Authors

Adam Kapelner and Justin Bleich (R package)

Initial release

2020-12-17