A tool for simulating multi-response linear model data

Abstract

Data science is generating enormous amounts of data, and new and advanced analytical methods are constantly being developed to cope with the challenge of extracting information from such “big-data”. Researchers often use simulated data to assess and document the properties of these new methods, and in this paper we present an extension to the R-package simrel, which is a versatile and transparent tool for simulating linear model data with an extensive range of adjustable properties. The method is based on the concept of relevant components, and is equivalent to the newly developed envelope model. It is a multi-response extension of R-package simrel which is available in R-package repository CRAN, and as simrel the new approach is essentially based on random rotations of latent relevant components to obtain a predictor matrix X, but in addition we introduce random rotations of latent components spanning a response space in order to obtain a multivariate response matrix Y. The properties of the linear relation between X and Y are defined by a small set of input parameters which allow versatile and adjustable simulations. Sub-space rotations also allow for generating data suitable for testing variable selection methods in multi-response settings. The method is implemented as an update to the R-package simrel.