Description Usage Arguments Details References Examples
Hierarchical inference testing for linear models with high-dimensional and/or correlated covariates by repeated sample splitting.
1 2 3 4 |
x |
Design matrix of dimension |
y |
Quantitative response variable dimension |
hierarchy |
Object of class |
family |
Family of response variable distribution. Ether |
B |
Number of sample-splits. |
p.samp1 |
Fraction of data used for the LASSO. The hierachical ANOVA testing uses
|
nfolds |
Number of folds (default is 10). See |
overall.lambda |
Logical, if true, lambda is estimated once, if false, lambda is estimated for each sample split. |
lambda.opt |
Criterion for optimum selection of cross-validated lasso. Either
"lambda.1se" (default) or "lambda.min". See |
alpha |
A single value in the range of 0 to 1 for the elastic net mixing parameter. |
gamma |
Vector of gamma-values. |
max.p.esti |
Maximum alpha level. All p-values above this value are set to one.
Small |
mc.cores |
Number of cores for parallelising. Theoretical maximum is 'B'. For
details see |
trace |
If TRUE it prints current status of the program. |
... |
Additional arguments for |
The H0-model contains variables, with are not tested, like experimental-design variables. These variables are not penalised in the LASSO model selection and are always include in the reduced ANOVA model.
Mandozzi, J. and Buehlmann, P. (2013). Hierarchical testing in the high-dimensional setting with correlated variables. To appear in the Journal of the American Statistical Association. Preprint arXiv:1312.5556
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | # Simulation:
set.seed(123)
n <- 80
p <- 82
## x with correlated columns
corMat <- toeplitz((p:1/p)^2)
corMatQ <- chol(corMat)
x <- matrix(rnorm(n * p), nrow = n) %*% corMatQ
colnames(x) <- paste0("x", 1:p)
## y
mu <- x[, c(5, 10, 72)] %*% c(2, -2, 2)
y <- rnorm(n, mu)
## clustering of the clumns of x
hc <- hclust(dist(t(x)))
# HIT with AF
out <- hit(x, y, hc)
summary(out)
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