hierarchicalFWER {MLGL} | R Documentation |
Apply hierarchical test for each hierarchy, and test external variables for FWER control at level alpha
hierarchicalFWER(X, y, group, var, test = partialFtest, Shaffer = FALSE)
X |
original data |
y |
associated response |
group |
vector with index of groups. group[i] contains the index of the group of the variable var[i]. |
var |
vector whith the variables contained in each group. group[i] contains the index of the group of the variable var[i]. |
test |
function for testing the nullity of a group of coefficients in linear regression. 3 parameters : X : design matrix, y response and varToTest : vector of variables to test; return a pvalue |
Shaffer |
boolean, if TRUE, a shaffer correction is performed |
Version of the hierarchical testing procedure of Meinshausen for MLGL output. You can use th selFWER function to select groups at a desired level alph
a list containing :
pvalues of the different test (without correction)
adjusted pvalues
Index of the group
Matrix describing the hierarchical tree.
Meinshausen, Nicolai. "Hierarchical Testing of Variable Importance." Biometrika 95.2 (2008): 265-78.
set.seed(42) X = simuBlockGaussian(50,12,5,0.7) y = X[,c(2,7,12)]%*%c(2,2,-2) + rnorm(50,0,0.5) res = MLGL(X,y) test = hierarchicalFWER(X, y, res$group[[20]], res$var[[20]])