Refit a mht.order object for a new observation Ynew and/or a new order ordrenew
Usage
## S3 method for class 'mht.order'
refit(object,Ynew,ordrenew,IT,var_nonselect,sigma,showresult,...)
Arguments
object
Object of class "mht.order" as obtained from mht.
Ynew
Response variable of length n.
ordrenew
Vector from which the varibles are to be ordered, it can be a partial order. If absent, data is considers to be already ordered; Default is (1,2,..,p).
IT
Number of simulations in the calculation of the quantile. Default is 10000.
var_nonselect
Number of variables that don't undergo feature selection. They have to be in the first columns of data. Default is 1, the selection is not performed on the intercept.
sigma
Value of the variance if it is known; 0 otherwise. Default is 0.
showresult
Logical value. if TRUE, show the value of the statistics and the estimated quantile at each step of the procedure. Default is TRUE.
...
not used.
Details
see mht.order for details.
Value
A 'mht.order' object is returned.
data
A list containing:
X - The scaled matrix used in the algorithm, the first column being (1,...,1).
Y - the input response vector
means.X - Vector of means of the input data matrix.
sigma.X - Vector of variances of the input data matrix.
coefficients
Matrix of the estimated coefficients. Each row concerns a specific user level alpha.
residuals
Matrix of the residuals. Each row concerns a specific user level alpha.
relevant_var
Set of the relevant variables. Each row concerns a specific user level alpha
fitted.values
Matrix of the fitted values, each column concerns a specific user level alpha.
kchap
Vector containing the length of the estimated set of relevant variables, for each values of alpha.
quantile
The estimated type I error to be used in the second step of the procedure in order to have a test of level alpha, each column stands for one test. See F.Rohart (2011) for details.
call
The call that has been used.
call.old
The call that produced the initial 'object'.
Examples
## Not run:
x=matrix(rnorm(100*20),100,20)
beta=c(rep(2,5),rep(0,15))
y=x%*%beta+rnorm(100)
ynew=x%*%beta+rnorm(100)
# mht.order
mod.order=mht.order(x,y)
# refit mht.order on a new vector of observation
mod.refit=refit(mod.order,ynew)
## End(Not run)