Description
Mixed-Effects REML Incorporating Generalized Inverses.
Description
Fit linear mixed-effects models using restricted (or residual) maximum likelihood (REML) and with generalized inverse matrices to specify covariance structures for random effects. In particular, the package is suited to fit quantitative genetic mixed models, often referred to as 'animal models'. Implements the average information algorithm as the main tool to maximize the restricted log-likelihood, but with other algorithms available.
README.md
gremlin
R
package for mixed-effects model REML incorporating Generalized Inverses (so, with some mental gymnastics: GREMLIN).
See the latest developments:
- gremlin NEWS page
Overview of main branches:
master
branch is the most recent production version (often the same as what is available from the R CRAN mirrors)devel
branch is a preview of the next release which should be functional and error/bug free, but proceed with caution
To install gremlin:
- From R:
- see the package page for the latest release of gremlin on CRAN where you can download the source.
- install the latest release of the package directly in R:
install.packages("gremlin")
then select your favorite CRAN mirror
- From GitHub:
- install the latest versions directly in R using the
devtools
package https://github.com/hadley/devtools:
- install the latest versions directly in R using the
library(devtools)
# Install `master` branch
install_github("matthewwolak/gremlin")
# Install `devel` branch
install_github("matthewwolak/gremlin", ref = "devel")
Examples
- Estimating autosomal additive and dominance genetic variances
library(gremlin)
library(nadiv) #<-- needed for creating inverse relatedness matrices
# Set up a subset of data for the example
warcolak$IDD <- warcolak$ID
# Create generalized inverse matrices
Ainv <- makeAinv(warcolak[, 1:3])$Ainv
Dinv <- makeD(warcolak[, 1:3])$Dinv
# Basic model structure is as follows:
## Fixed effects of sex
## ID = autosomal additive genetic variance term
## IDD = autosomal dominance genetic variance term
grAD <- gremlin(trait1 ~ sex-1,
random = ~ ID + IDD,
ginverse = list(ID = Ainv, IDD = Dinv),
data = warcolak)
# Summary
nrow(warcolak)
summary(grAD)