Description Usage Arguments Details Value Author(s) See Also Examples
Run hierarchical clustering following by a group-lasso on all the different partition and a hierarchical testing procedure.
1 2 | fullProcess(X, y, control = c("FWER", "FDR"), alpha = 0.05,
test = partialFtest, hc = NULL, plot = TRUE, ...)
|
X |
matrix of size n*p |
y |
vector of size n |
control |
either "FDR" or "FWER" |
alpha |
control elvel for testing procedure |
test |
test used in the testing procedure. Default is partialFtest |
hc |
output of |
plot |
If TRUE plot the number of groups selected before and after the testing procedure |
... |
Others parameters |
Divide the n individuals in two samples. Then the three following steps are done : 1) Hierarchical CLustering of the variables of X based on the first sample of individuals 2) HCgglasso on the second sample of individuals 3) Hierarchical testing procedure on the first sample of individuals.
a list containing :
output of HCgglasso function
lambda values maximizing the number of rejects
A vector containing the index of selected variables for lambdaOpt
A vector containing the values index of selected groups for lambdaOpt
Quentin Grimonprez
HCgglasso, hierarchicalFDR, hierarchicalFWER, selFDR, selFWER
1 2 3 4 5 6 7 | ## Not run:
set.seed(42)
X <- simuBlockGaussian(50,12,5,0.7)
y <- drop(X[,c(2,7,12)]%*%c(2,2,-2)+rnorm(50,0,0.5))
res <- fullProcess(X, y)
## End(Not run)
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