This function performs stability selection for the cumulative logit model.
Stability.cumu(x, y, lambda, n_iter=100, type=c("selection", "fusion"), ...)
the matrix of estimated selection probabilities. Columns correspond to different lambda values, rows correspond to covariates.
matrix of size n_iter \(x\) length(lambda) containing the corresponding model size.
a vector or matrix of integers 1,2,... giving the observed levels
of the ordinal factor(s). If x is a matrix, it is assumed that
each column corresponds to one ordinal factor.
the vector of response values.
vector of penalty parameters (in decreasing order).
number of subsamples. Details below.
penalty to be applied. If "selection", group lasso penalty for smoothing and selection is used. If "fusion", a fused lasso penalty for fusiona dn selection is used.
additional arguments to ordFusion and
ordSelect, respectively.
Aisouda Hoshiyar
The method assumes that ordinal factor levels (contained in vector/columns of
matrix x) take values 1,2,...,max, where max denotes the highest level
of the respective factor observed in the data. Every level between 1 and max has
to be observed at least once.
Instead of selecting/fitting one model, the data are pertubed/subsampled iter times and we choose those variables that occur in a large fraction (\(pi\)) of runs.
The stability path then shows the order of relevance of the predictors according to stability selection.
Hoshiyar, A., Gertheiss, L.H., and Gertheiss, J. (2023). Regularization and Model Selection for Item-on-Items Regression with Applications to Food Products' Survey Data. Preprint, available from https://arxiv.org/abs/2309.16373.
Meinshausen, N. and Buehlmann, P. (2010). Stability selection, Journal of the Royal Statistical Society B (Statistical Methodology), 72, 417-473.
ordSelect, ordFusion