qdg.sem {qtlnet}

Score directed graphs outputed by qdg using structural equation models (SEM)

Description

Score directed graphs (cyclic or acyclic) outputed by qdg function using the sem R package.

Usage

qdg.sem(qdgObject, cross)
## S3 method for class 'qdg.sem'
print(x, ...)
## S3 method for class 'qdg.sem'
summary(object, ...)

Arguments

qdgObject

list containing the output of qdg.

cross

object of class cross (see read.cross).

x, object

object of class qdg.

...

extra arguments to print or summary (ignored).

Details

Fits a SEM to the phenotypes network. QTLs are not included as variables in the model. When additive covariates are used in qdg, qdg.sem fits a SEM model to the residuals of the variables after adjustment of the additive covariates.

Value

List object that inherits class "qdg.sem" and "qdg" composed by:

best.SEM

Solution with lowest SEM BIC (best fit to the data).

BIC.SEM

Vector with the BIC values of all solutions from qdg.

path.coeffs

Path coefficients associated with the best SEM solution.

Solutions

Solutions of dependency graph after recheck step (output of steps 3, 4 and 5 of the QDG algorithm.)

marker.names

List of character strings, one for each of phenotype.names. Each character string has the marker names for that phenotype.

phenotype.names

Character string with names of phenotype nodes corresponding to phenotypes in cross.

dropped

Indexes of solutions that were dropped (NULL if none dropped).

See Also

qdg sem

Examples

## simulate a genetic map (20 autosomes, 10 not equaly spaced markers per 
## chromosome)
mymap <- sim.map(len=rep(100,20), n.mar=10, eq.spacing=FALSE, include.x=FALSE)

## simulate an F2 cross object with n.ind (number of individuals)
n.ind <- 200
mycross <- sim.cross(map=mymap, n.ind=n.ind, type="f2")

## produce multiple imputations of genotypes using the 
## sim.geno function. The makeqtl function requires it,
## even though we are doing only one imputation (since 
## we don't have missing data and we are using the 
## genotypes in the markers, one imputation is enough)
mycross <- sim.geno(mycross,n.draws=1)

## sample markers (2 per phenotype)
genotypes <- pull.geno(mycross)
geno.names <- dimnames(genotypes)[[2]]
m1 <- sample(geno.names,2,replace=FALSE)
m2 <- sample(geno.names,2,replace=FALSE)
m3 <- sample(geno.names,2,replace=FALSE)
m4 <- sample(geno.names,2,replace=FALSE)

## get marker genotypes
g11 <- genotypes[,m1[1]]; g12 <- genotypes[,m1[2]]
g21 <- genotypes[,m2[1]]; g22 <- genotypes[,m2[2]]
g31 <- genotypes[,m3[1]]; g32 <- genotypes[,m3[2]]
g41 <- genotypes[,m4[1]]; g42 <- genotypes[,m4[2]]

## generate phenotypes
y1 <- runif(3,0.5,1)[g11] + runif(3,0.5,1)[g12] + rnorm(n.ind)
y2 <- runif(3,0.5,1)[g21] + runif(3,0.5,1)[g22] + rnorm(n.ind)
y3 <- runif(1,0.5,1) * y1 +  runif(1,0.5,1) * y2 + runif(3,0.5,1)[g31] +
      runif(3,0.5,1)[g32] + rnorm(n.ind)
y4 <- runif(1,0.5,1) * y3 + runif(3,0.5,1)[g41] + runif(3,0.5,1)[g42] +
      rnorm(n.ind)

## incorporate phenotypes to cross object
mycross$pheno <- data.frame(y1,y2,y3,y4)

## create markers list
markers <- list(m1,m2,m3,m4)
names(markers) <- c("y1","y2","y3","y4")

## create qtl object
allqtls <- list()
m1.pos <- find.markerpos(mycross, m1)
allqtls[[1]] <- makeqtl(mycross, chr = m1.pos[,"chr"], pos = m1.pos[,"pos"])
m2.pos <- find.markerpos(mycross, m2)
allqtls[[2]] <- makeqtl(mycross, chr = m2.pos[,"chr"], pos = m2.pos[,"pos"])
m3.pos <- find.markerpos(mycross, m3)
allqtls[[3]] <- makeqtl(mycross, chr = m3.pos[,"chr"], pos = m3.pos[,"pos"])
m4.pos <- find.markerpos(mycross, m4)
allqtls[[4]] <- makeqtl(mycross, chr = m4.pos[,"chr"], pos = m4.pos[,"pos"])

names(allqtls) <- c("y1","y2","y3","y4")

## infer QDG 
out <- qdg(cross=mycross, 
		phenotype.names = c("y1","y2","y3","y4"), 
		marker.names = markers, 
		QTL = allqtls, 
		alpha = 0.005, 
		n.qdg.random.starts=10, 
		skel.method="pcskel")
		
## Not run: 
gr <- graph.qdg(out)
plot(gr)

## Following does not work. Not sure why.
out2 <- qdg.sem(out, cross=mycross)
out2
gr2 <- graph.qdg(out2)
plot(gr2)

## End(Not run)