nadiv (version 2.17.2)

grfx: Simulated genetic random effects

Description

This function simulates effects for random terms in a linear mixed model based on relatedness matrices. The intended purpose is for simulating genetic and environmental effects from a pedigree.

Usage

grfx(
  n,
  G,
  incidence = NULL,
  saveIncidence = FALSE,
  output = "matrix",
  stdnorms = NULL,
  warn = TRUE
)

Value

The random effects coerced to be in the format specified by output. The default is a "matrix".

Arguments

n

The number of individuals for which to simulate effects

G

The variance-covariance matrix to model the effects after

incidence

The covariance structure of the 'n' individuals

saveIncidence

A logical or NULL, indicating if the cholesky decomposition of the incidence matrix should be saved/retrieved to the global environment. SEE details below!!

output

Format for the output

stdnorms

Standard normal deviates to use

warn

Should a warning be produced when incidence = NULL indicating that a previous incidence matrix is being used if one exists, otherwise an Identity matrix

Author

Details

The total number of effects simulated will be n*d, where d is the number of columns in the 'G' matrix. The standard normal deviates can be supplied instead of generated within the function when stdnorms != NULL. The length of this vector must be n*nrow(G).

Supplied incidence matrices should be n-by-n symmetric matrices. For simulated random effects using design matrices, see drfx. If no incidence matrix is supplied, incidence = NULL, the function first checks the environment to see if anything with the name 'nadiv_prev_Mincidence' exists when checking ls(). If so, this saved version is used with a warning. Otherwise the Identity matrix is used, which assumes that all 'n' random effects are independently and identically distributed (default to Identity matrix).

BE CAREFUL with saveIncidence = TRUE as this will save the incidence matrix outside of the function environment so as to be accessed within the function at a later call. This can be useful for Monte Carlo simulation, to avoid performing the cholesky decomposition on a large matrix at each iteration. Setting warn = FALSE will suppress the warnings that this is occurring. DO NOT turn this warning off unless you are sure which incidence matrix will be used by grfx.

If G = x, where 'x' is a single number, then 'x' should still be specified as a 1-by-1 matrix (e.g., matrix(x)). Note, the G-matrix should never have a structure which produces a correlation exactly equal to 1 or -1. Instead, covariances should be specified so as to create a correlation of slightly less than (greater than) 1 (-1). For example: 0.9999 or -0.9999.

See Also

MCMCglmm, drfx, makeA, makeAA, makeD, makeDomEpi, makeDsim, makeS

Examples

# Create additive genetic breeding values for 2 uncorrelated traits
# with different additive genetic variances
  A <- makeA(warcolak[1:200, 1:3])
  Gmat <- matrix(c(20, 0, 0, 10), 2, 2)
  breedingValues <- grfx(n = 200, G = Gmat, incidence = A, saveIncidence = FALSE)

 # Now with a user supplied set of standard normal deviates
  snorms <- rnorm(nrow(warcolak[1:200,]) * ncol(Gmat))
  breedingValues2a <- grfx(n = 200, G = Gmat, incidence = A, stdnorms = snorms)
  breedingValues2b <- grfx(n = 200, G = Gmat, incidence = A, stdnorms = snorms)
  identical(breedingValues2a, breedingValues2b)  # TRUE
  var(breedingValues2a)
  var(breedingValues2b)