Functions for data simulation from a random regression model with one response variable where the data properties can be controlled by a few input parameters. The data simulation is based on the concept of relevant latent components and relevant predictors, and was developed for the purpose of testing methods for variable selection for prediction.
Facilitates data simulation from a multiple linear model where the data properties can be controlled by a few input parameters. The data simulation is based on the concept of relevant latent components and relevant predictors, and was developed for the purpose of testing methods for variable selection for prediction. The package also contains the tools for designing computer experiments in order to investigate the effects of the data properties on the performance of the tested methods. The design is constructed using the multi-level binary replacement (MBR) design approach which makes it possible to set up fractional designs for multi-factor problems with potentially many levels for each factor.
The multi-level binary replacement (MBR) design approach is used here in order to facilitate the investigation of the effects of the data properties on the performance of estimation/prediction methods. The mbrdsim function takes as input a list containing a set of factors with their levels. The output is an MBR-design with the combinations of the factor levels to be run.
Function to create multi-level binary replacement (MBR) design (Martens et al., 2010). The MBR approach was developed for constructing experimental designs for computer experiments. MBR makes it possible to set up fractional designs for multi-factor problems with potentially many levels for each factor. In this package it is mainly called by the mbrdsim function.
The plotting function produces three plots: 1) A barplot of the true regression coefficents in the linear regression model. 2) Scree-plot of true eigenvalues (barplot) with true covariances (absolute values) between components and response overlayed (red dots). The covariances are scaled by the largest covariance (in absolute value). 3) Scree-plot of eigenvalues estimated from the simulated data (barplot) with estimated covariances (absolute values) between components and simulated response overlayed (red dots). The estimated covariances are scaled by the largest estimated covariance (in absolute value).
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