Fitter Function for Linear Mixed Model

Prepare data for model fitting with a call to Rcpp code

Usage

fastlmm.fit(
  y,
  X,
  Z,
  offset = NULL,
  REML = FALSE,
  delta = NULL,
  rank = ncol(Z),
  weights = NULL,
  delta.range = c(-10, 10),
  tol = 1e-06,
  lambda = 0,
  nthreads = 6
)

Arguments

y

response vector

X

design matrix

Z

sparse matrix of indicators for random effect

offset

offset

REML

logical scalar - Should the estimates be chosen to optimize the REML criterion vs ML?

delta

if NULL estimate delta, if value is given used this fixed values

rank

rank of random effect. The maximum rank is the number of columns in Z. A low rank approximation can be useful if the eigen-values decrease quickly.

weights

an optional vector of prior weights with a value for each sample. When the response has multiple columns, a vector of weight can be reused for each respose, or a matrix the same dimension as the responses matrix can weight each response separately.

delta.range

min and max values (in log space), of the search space for delta to fit the random effect

tol

convergence criterion for the 1D search of the delta space

lambda

ridge shrinkage parameter

nthreads

number of threads

Value

U and s values are from the SVD of weighted Z

Details

Fit a linear mixed model with a single variance component.