View source: R/StabilityCumu.R
Stability.cumu | R Documentation |
This function performs stability selection for the cumulative logit model.
Stability.cumu(x, y, lambda, n_iter=100, type=c("selection", "fusion"), ...)
x |
a vector or matrix of integers 1,2,... giving the observed levels
of the ordinal factor(s). If |
y |
the vector of response values. |
lambda |
vector of penalty parameters (in decreasing order). |
n_iter |
number of subsamples. Details below. |
type |
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 |
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.
Pi |
the matrix of estimated selection probabilities. Columns correspond to different lambda values, rows correspond to covariates. |
mSize |
matrix of size |
Aisouda Hoshiyar
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
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.