extract.object fits a multivariate quantile regression and extracts objects for the cluster effects algorithm.extract.object fits a multivariate quantile regression and extracts objects for the cluster effects algorithm.
extract.object(Y, X, intercept=TRUE, formula.p=~slp(p, 3), s, object, p, which)
The percentiles used in the quantile regression.
A list containing as many matrices as covariates, where for each matrix the number of columns corresponds to the number of the responses. Each column of a matrix corresponds to one curve effect. In the case of a univariate model it is a unique matrix.
A list as X. Each column of a matrix corresponds to the lower interval of the curve effect. In the case of a univariate model it is a unique matrix.
A list as X. Each column of a matrix corresponds to the upper interval of the curve effect. In the case of a univariate model it is a unique matrix.
A multivariate response matrix of dimension n x q1, or a vector of length n.
The covariates matrix of dimension n x q2.
If TRUE, the intercept is included in the model.
a one-sided formula of the form ~ b1(p, ...) + b2(p, ...) + ...
An optional 0/1 matrix that allows to exclude some model coefficients (see ‘Examples’).
An object of class “iqr”. If missing, Y and X have to be supplied.
The percentiles used in quantile regression coefficient modeling. If missing a default sequence is choosen.
If fixed, only the selected covariates are extraced from the model. If missing all the covariates are extracted.
Gianluca Sottile gianluca.sottile@unipa.it
A list of objects useful to run the cluster effect algorithm is created.
clustEff, for clustering algorithm; summary.clustEff and plot.clustEff, for summarizing and plotting clustEff objects.
# using simulated data
# see the documentation for 'clustEff'
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