QTLRel (version 0.2-9)

estVC: Estimate Variance Component Parameters

Description

Estimate model parameters for covariates, genetic variance components and residual effect.

Usage

estVC(y,x,v=vector("list",6),initpar,nit=25,
   method=c("Nelder-Mead","BFGS","CG","SANN"),
   control=list(),hessian=FALSE)

Arguments

y

a numeric vector or a numeric matrix of one column (representing a phenotype for instance).

x

a data frame or matrix, representing covariates if not missing.

v

a list of variance components (AA, DD, HH, AD, MH, EE,...), where "AA" and "DD" are respectively additive and dominance genetic matrices, "HH", "AD" and "MH" are other genetic matrices that one may be interested in (see

initpar

optional initial parameter values.

nit

number of iterations to call optim for optimization.

method

the optimization method to be used. See optim for details.

control

a list of control parameters to be passed to optim.

hessian

logical. Should a numerically differentiated Hessian matrix be returned?

Value

• par
  estimates of the model parameters.
• value
  log-likelihood of the model.
• y
  y used.
• x
  associated with x used.
• v
  variance component matrices v used.
• ...
  other information.

Details

The optimization function optim is adopted in the above function to estimate the parameters and maximum likelihood. Several optimization methods are available for the optimization algorithm in optim, but we recommend "Nelder-Mead" for the sake of stability. Alternatively, one may choose other options, e.g., "BFGS" to initialize and speed up the estimation procedure and then the procedure will automatically turn to "Nelder-Mead" for final results. Normality is assumed for the random effects. Input data should be free of missing values.

See Also

optim and rem.

Examples

x<- sample(c("M","F"),100,replace=TRUE)
y<- rnorm(100) + (x=="M")*1.0
v<- cov(matrix(rnorm(10^6),ncol=100))

# no sex effect
o<- estVC(y, v = list(AA=v,DD=NULL,HH=NULL,AD=NULL,
   MH=NULL,EE=diag(100)),method="BFGS")
o

# sex as fixed effect
fo<- estVC(y, x, v = list(AA=v,DD=NULL,HH=NULL,AD=NULL,
   MH=NULL,EE=diag(100)),method="BFGS")
fo
2*(fo$value-o$value) # log-likelihood test statistic

# sex as random effect
SM<- rem(~x,data.frame(x=x))
ro<- estVC(y, v = list(AA=v,DD=NULL,HH=NULL,AD=NULL,
   MH=NULL,SE=SM$x,EE=diag(100)),method="BFGS")
ro
2*(ro$value-o$value) # log-likelihood test statistic