Fit kernel_model() informed by an environment-cluster table

"fixed" adds mega-environment means as fixed effects; "block" zeroes between-cluster entries of the GxE kernel so genotypes may rank differently across mega-environments; "both" does each.

Usage

kernel_model_clustered(
  y,
  data,
  random,
  env,
  gid,
  clusters,
  use = c("block", "fixed", "both"),
  gxe_pattern = "^KGE_|GE",
  fixed = NULL,
  ...
)

Arguments

y

character. Name of the response column in data.

data

data.frame with the response, environment and genotype columns.

random

named list of kernels passed to kernel_model.

env

character. Name of the environment column in data.

gid

character. Name of the genotype column in data.

clusters

an environment-cluster mapping: a cluster_environments result, a data.frame with columns env and cluster, or a named vector.

use

character. How clusters enter the model: "block" (default), "fixed" or "both".

gxe_pattern

character. Regular expression selecting the GxE kernels to block-diagonalise. Default "^KGE_|GE".

fixed

optional fixed-effects specification passed to kernel_model.

...

further arguments passed to kernel_model.

Value

An object of class "kernel_model" (see kernel_model) with an added cluster_info element: a list recording the environment-to-cluster mapping, the number of clusters k, the mechanism used ("block", "fixed" or "both"), the gxe_pattern, the number of added fixed columns n_fixed_cols and the names of the blocked_kernels.

Details

Block-diagonalisation MODIFIES the GxE kernels, so any cached decomposition for those kernels is dropped and recomputed. Both dense and factored kernels are supported. Selected factored kernels are reconstructed as dense observation-by-observation matrices before blocking; unselected kernels are left unchanged.