LogitNet: Fit a LogitNet model for a given tuning parameter value.

In LogitNet: Infer network based on binary arrays using regularized logistic regression

Description

Fit a LogitNet model by performing joint sparse logistic regressions for the binary data.

Usage

LogitNet(X.m, weight.m, lambda, beta.ini=NULL)

Arguments

X.m	numeric matrix (n by p). Columns are for variables and rows are for samples. Missing values are not allowed.
weight.m	numeric matrix (p by p). This weight matrix allows the coefficients in the regression model to be penalized to varying degree, such that the spatial correlations along the genome profiles are taken into account. Can use the output from LogitNet.weight function.
lambda	numeric scalar. It gives the l_1 norm penalty parameter.
beta.ini	numeric matrix (p by p). This matrix serves as the initial estimation of the coefficient matrix. The default is NULL.

Details

LogitNet is developed for infering interaction network of binary variables. The method is based on penalized logistic regression with an extension to account for spatial correlation in the genomic instability data. (Wang et al., 2009).

Value

beta	the estimated coefficient matrix (p by p) from the LogitNet model.

Author(s)

Pei Wang, Dennis Chao, Li Hsu

References

Pei Wang, Dennis Chao, Li Hsu, "Learning oncogenic pathways from binary genomic instability data", Biometrics, (submitted 2009, July)

Examples

######################## get data example

data(LogitNet.data)
data.m=LogitNet.data$data.m
chromosome=LogitNet.data$chromosome
p=ncol(data.m)

######################## specify the penalty parameter
lambda.n=5
lambda.v=exp(seq(log(13), log(30), length=lambda.n))

######################## calculate the weight matrix
w.m=LogitNet.weight(data.m, chr=chromosome) 

######################## perform cross validation to select lambda
if(0) ### this part will take 10 minutes.
{
try.CV=LogitNet.CV(data.m, w.m, lambda.v, fold=5) 
temp=apply(try.CV[[3]], 2, sum) 
index=which.max(temp) 
}
index=2
######################## estimate the model at selected lambda

result=LogitNet(data.m, w.m, lambda.v[index]) ###20-30 seconds

######################## illustrate the result similar to Figure 3 of Wang et al. (2009)).

temp=result
diag(temp)=0

par(cex=1.8)
image(1:p, 1:p, temp!=0, col=c("white", "red"), axes=FALSE, xlab="Marker Loci", ylab="Marker Loci")
abline(h=(0:5)*p/6+p/6/2, col=4, lty=3, lwd=0.8)
abline(v=(0:5)*p/6+p/6/2, col=4, lty=3, lwd=0.8)
axis(1, at=c(1,1:6*100), labels=c(1,1:6*100))
axis(2, at=c(1,1:6*100), labels=c(1,1:6*100))
axis(3, at=(0:5)*p/6+p/6/2, labels=c("A", "B", "C", "D", "E", "F"), col.axis=4, tick=FALSE)
axis(4, at=(0:5)*p/6+p/6/2, labels=c("A", "B", "C", "D", "E", "F"), col.axis=4, tick=FALSE)

lab.v=c("A", "B", "C", "D", "E", "F")

cut=30
for(i in 0:4)
{
   cur=i*p/6+p/6/2
   cur2=(i+1)*p/6+p/6/2

   x.cur=c(cur-cut, cur, cur+cut, cur)
   y.cur=c(cur2, cur2-cut, cur2, cur2+cut)
   polygon(x.cur, y.cur, border=grey(0.5))
   polygon(y.cur, x.cur, border=grey(0.5))
}

LogitNet documentation built on May 2, 2019, 1:20 p.m.