Simulation of Multivariate Linear Model Data
simrel(n, p, q, relpos, gamma, R2, type = "univariate", ...)
Number of observations.
Number of variables.
An integer for univariate, a vector of 3 integers for bivariate and 3 or more for multivariate simulation (for details see Notes).
A list (vector in case of univariate simulation) of position of relevant component for predictor variables corresponding to each response.
A declining (decaying) factor of eigenvalues of predictors (X). Higher the value of
Vector of coefficient of determination (proportion of variation explained by predictor variable) for each relevant response components.
Type of simulation -
Since this is a wrapper function to simulate univariate, bivariate or multivariate, it calls their respective function. This parameter should contain all the necessary arguements for respective simulations. See
A simrel object with all the input arguments along with following additional items.
Simulated predictor components
Simulated response components
True regression coefficients
True regression intercept
Position of relevant predictors
Test predictor components
Test response components
Minimum model error
Rotation matrix of predictor (R)
Rotation matrix of response (Q)
Type of simrel object univariate or multivariate
Eigenvalues of predictors
Variance-Covariance matrix of components of response and predictors
Covariance matrix of response components and predictors
Covariance matrix of response and predictor components
Variance-Covariance matrix of response and predictors
Coefficient of determination corresponding to response components
Coefficient of determination corresponding to response variables
Sæbø, S., Almøy, T., & Helland, I. S. (2015). simrel—A versatile tool for linear model data simulation based on the concept of a relevant subspace and relevant predictors. Chemometrics and Intelligent Laboratory Systems, 146, 128-135.
Almøy, T. (1996). A simulation study on comparison of prediction methods when only a few components are relevant. Computational statistics & data analysis, 21(1), 87-107.