linearity_test {bartMachine} | R Documentation |
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
linearity_test(lin_mod = NULL, X = NULL, y = NULL, num_permutation_samples = 100, plot = TRUE, ...)
lin_mod |
A linear model you can pass in if you do not want to use the default which is |
X |
Data frame of predictors. Factors are automatically converted to dummies internally. Default is |
y |
Vector of response variable. If |
num_permutation_samples |
This function relies on |
plot |
This function relies on |
... |
Additional parameters to be passed to |
permutation_samples_of_error |
This function relies on |
observed_error_estimate |
This function relies on |
pval |
The approximate p-value for this test. See the documentation at |
Adam Kapelner
## 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)