diabetes

<p>The dataset consists of observations on 442 individuals for which a
quantitative measure of diabetes progression is recorded in variable <code>Y</code>.
Predictors include 10 baseline measurements, 45 interactions and 9 quadratic
terms, for a total of 64 variables for each individual. Each variable has
been standardized by subtracting the mean and then dividing it by its
standard deviation.</p>

datasets

Linear and logistic regression models penalized with hierarchical
shrinkage priors for selection of biomarkers (or more general variable
selection), which can be fitted using Stan (Carpenter et al. (2017)
<doi:10.18637/jss.v076.i01>). It implements the horseshoe and regularized
horseshoe priors (Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>),
as well as the projection predictive selection approach to recover a sparse
set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020)
<doi:10.1214/20-EJS1711>).

Marco Colombo

hsstan

Hierarchical Shrinkage Stan Models for Biomarker Selection

Paul McKeigue

Athina Spiliopoulou

diabetes function

<p>A data frame with 442 rows and 65 columns (centred and scaled).</p>

diabetes: Diabetes data with interaction terms

Description

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Examples