qtlmt (version 0.1-3)

mtcmimStep: MTCMIM model selection

Description

Model selection for multiple-trait composite multiple-interval mapping.

Usage

mtcmimAdd1(object, y, x, xid, mpos, mdat, pp=1, len=1, type=1,
   iter=2500, tol=1e-8, ext=FALSE)

mtcmimDrop1(object, y, x, xid, mpos, mdat, pp=1, len=1, type=1,
   iter=2500, tol=1e-8, ext=FALSE)

mtcmimStep(object, y, x, xid, mpos, mdat, cv=0,
   direction=c("both","backward","forward"), pp=1, len=1,
   type=1, iter=2500, tol=1e-8, ext=FALSE)

Arguments

object

an object of class mtcmim.

y

a n by p matrix, whose columns are dependent variables.

x

covariates; n by m numerical matrix.

xid

a list of length p; xid[[j]] specifies columns of x as covariates for y[,j] .

mpos

a data frame (id=marker index, ch=chromosome id, m=marker index on the chromosome, dist=genetic position in cM on the chromosome). Chromosome id should be an integer.

mdat

a matrix of n rows; marker genotypes (1 or 0). columns should correspond to markers in the order.

pp

mapping population: BC-1, RIL-selfing-2, RIL-brother-sister-mating-3.

len

step length in search.

type

1 if traits can have the different sets of covariates and QTL, 2 if all have the same set of covariates and QTL.

ext

whether to perform an extensive search for an "optimal" model with the same number of QTL per phenotype.

cv

critical value used in the likelihood ratio test to determine adding/dropping a QTL.

direction

forward selection, backward elimination or both directions.

iter

maximum number of iterations in a numerical process to estimate model parameters.

tol

convergence tolerance.

Value

• a list with the following components:
• loglik:
  log-likelihood of the final model
• a:
  covariate effects
• b:
  QTL effects
• sigma:
  residual variance-covariance
• qtl:
  QTL for each trait
• dists:
  QTL locations

See Also

mtcmim

Examples

data(etrait)
y<- traits[,1:5]
qtl<- vector("list",5); qtl[[1]]<- c(1,2)
dists<- dists[c(4,11),]
x<- mdat - 3/2
o<- mtcmim(y, mpos, mdat, dists=dists, qtl=qtl, eps=NULL,
   win=5, range=-1, pp=2, len=1)
of<- mtcmimAdd1(o, y=y, mpos=mpos, mdat=mdat, pp=2, len=3)
os<- mtcmimStep(of, y=y, mpos=mpos, mdat=mdat, cv=25,
   direction="both", pp=2, len=3)