| QGglmm-package {QGglmm} | R Documentation |
Estimate Quantitative Genetics Parameters from Generalised Linear Mixed Models
Description
Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance.
Details
The DESCRIPTION file:
| Package: | QGglmm |
| Type: | Package |
| Title: | Estimate Quantitative Genetics Parameters from Generalised Linear Mixed Models |
| Version: | 0.7.4 |
| Date: | 2020-01-03 |
| Author: | Pierre de Villemereuil <bonamy@horus.ens.fr> |
| Maintainer: | Pierre de Villemereuil <bonamy@horus.ens.fr> |
| BugReports: | https://github.com/devillemereuil/qgglmm/issues |
| Description: | Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance. |
| Imports: | cubature (>= 1.4) |
| License: | GPL-2 |
Index of help topics:
QGglmm-package Estimate Quantitative Genetics Parameters from
Generalised Linear Mixed Models
QGicc Intra - Class Correlation coefficients (ICC) on
the observed data scale
QGlink.funcs List of functions according to a distribution
and a link function
QGmean Compute the phenotypic mean on the observed
scale
QGmvicc Intra - Class Correlation coefficients (ICC) on
the observed data scale (multivariate
analysis).
QGmvmean Compute the multivariate phenotypic mean on the
observed scale
QGmvparams Quantitative Genetics parameters from GLMM
estimates (multivariate analysis).
QGmvpred Predict the evolutionary response to selection
on the observed scale
QGmvpsi Compute a multivariate "Psi" (used to compute
the additive genetic variance on the observed
scale).
QGparams Quantitative Genetics parameters from GLMM
estimates.
QGpred Predict the evolutionary response to selection
on the observed scale
QGpsi Compute "Psi" (used to compute the additive
genetic variance on the observed scale).
QGvar.dist Compute the distribution variance
QGvar.exp Compute the variance of expected values (i.e.
the latent values after inverse-link
transformation.)
QGvcov Compute the phenotypic variance-covariance
matrix on the observed / expected scale
This package gives the values on the observed scale for several quantitative genetics parameter using estimates from a Generalised Linear Mixed Model (GLMM). If a fitness function is assumed or measured, it also predicts the evolutionary response to selection on the observed scale.
The two main functions of this package are QGparams and QGpred. The first allows to compute the quantitative genetics parameters on the observed scale for any given GLMM and its estimates. The second allows to compute a predicted response to evolution on the observed scale using GLMM estimates and an assumed/measured/inferred fitness function.
For some distribution/link models (e.g. Binomial/probit and Poisson and Negative Binomial with logartihm or square-root link), a closed form solutions of the integrals computed by this package are available. They are automatially used by QGparams and this function only.
Author(s)
Pierre de Villemereuil <bonamy@horus.ens.fr>
Maintainer: Pierre de Villemereuil <bonamy@horus.ens.fr>
References
de Villemereuil, P., Schielzeth, H., Nakagawa, S., and Morrissey, M.B. (2016). General methods for evolutionary quantitative genetic inference from generalised mixed models. Genetics 204, 1281-1294.