asremlPlus
Augments 'ASReml-R' in Fitting Mixed Models and Packages Generally in Exploring Prediction Differences
Assists in automating the selection of terms to include in mixed models when 'asreml' is used to fit the models. Procedures are available for choosing models that conform to the hierarchy or marginality principle, for fitting and choosing between two-dimensional spatial models using correlation, natural cubic smoothing spline and P-spline models. A history of the fitting of a sequence of models is kept in a data frame. Also used to compute functions and contrasts of, to investigate differences between and to plot predictions obtained using any model fitting function. The content falls into the following natural groupings: (i) Data, (ii) Model modification functions, (iii) Model selection and description functions, (iv) Model diagnostics and simulation functions, (v) Prediction production and presentation functions, (vi) Response transformation functions, (vii) Object manipulation functions, and (viii) Miscellaneous functions (for further details see 'asremlPlus-package' in help). The 'asreml' package provides a computationally efficient algorithm for fitting a wide range of linear mixed models using Residual Maximum Likelihood. It is a commercial package and a license for it can be purchased from 'VSNi' https://vsni.co.uk/ as 'asreml-R', who will supply a zip file for local installation/updating (see https://asreml.kb.vsni.co.uk/). It is not needed for functions that are methods for 'alldiffs' and 'data.frame' objects. The package 'asremPlus' can also be installed from http://chris.brien.name/rpackages/.
- Version4.4.43
- R versionR (≥ 3.5.0)
- LicenseMIT
- LicenseLICENSE
- Needs compilation?No
- Last release12/10/2024
Documentation
- VignetteLadybird: a predictions (EMMs) example using asreml and asremlPlus
- VignetteLadybird: a predictions (EMMs) example using lm and asremlPlus
- VignetteWheat: a full analysis of an experiment with spatial variation
- VignetteWheat: using information criteria
- VignetteWheatSpatialModels: a full analysis of an experiment that includes choosing local spatial variation models
- VignetteasremlPlus-manual
- MaterialNEWS
- In ViewsAgriculture
- In ViewsMixedModels
Team
Chris Brien
MaintainerShow author details
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- Imports14 packages
- Suggests7 packages