mtcmimStep {qtlmt} | R Documentation |
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=10000, tol=1e-12, ext=FALSE)
mtcmimDrop1(object, y, x, xid, mpos, mdat, pp=1, len=1, type=1,
iter=10000, tol=1e-12, ext=FALSE)
mtcmimStep(object, y, x, xid, mpos, mdat, cv=0,
direction=c("both","backward","forward"), pp=1, len=1,
type=1, iter=10000, tol=1e-12, ext=FALSE)
Arguments
object |
an object of class |
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 |
Note
Currently, not able to include epistatic effects.
See Also
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
## Not run:
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)
## End(Not run)