Description Usage Arguments Value References Examples
This function computes importance score for P
permuted data sets. For each permuted data set, sample labels of response variable are randomly permuted and iJRF is implemented. Resulting importance scores can be used to derive an estimate of FDR.
1 
X 
List object containing predictors for each class, 
Y 
List object containing response variables for each class, 
W 

ntree 
Numeric value: number of trees. If omitted, 
mtry 
Numeric value: number of predictors to be sampled at each node. If omitted, 
res.name 
pdimensional vector containing names of response variable. 
cov.name 
Mdimensional vector containing names of predictors. 
P 
Number of permutations. 
A three dimensional array (I
, P
, C
) where I=M x p
is the total number of interactions, C
is the number of classes and P
the total number of permutations.
Petralia, F. et al (2017) A new method to study the change of miRNAmRNA interactions due to environmental exposures, Submitted.
Petralia, F., Wang, P., Yang, J., and Tu Z. (2015) Integrative random forest for gene regulatory network inference. 31(12), i197i205.
Petralia, F., Song, W.M., Tu, Z. and Wang, P. (2016). New method for joint network analysis reveals common and different coexpression patterns among genes and proteins in breast cancer. Journal of proteome research, 15(3), pp.743754.
Some of the functions utilized are a modified version of functions contained in R package randomForest: A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2, 18–22.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  #  Generate data sets
nclasses=2 # number of data sets / classes
n1<n2<20 # sample size for each data sets
p<5 # number of response variables
M<10 # number of predictor variables
W<abs(matrix(rnorm(M*p),M,p)) # generate sampling scores
Res1<matrix(rnorm(p*n1),p,n1) # generate response for class 1
Res2<matrix(rnorm(p*n2),p,n2) # generate response for class 2
Cov1<matrix(rnorm(M*n1),M,n1) # generate predictors for class 1
Cov2<matrix(rnorm(M*n2),M,n2) # generate predictors for class 2
#  Standardize variables to mean 0 and variance 1
Res1 < t(apply(Res1, 1, function(x) { (x  mean(x)) / sd(x) } ))
Res2 < t(apply(Res2, 1, function(x) { (x  mean(x)) / sd(x) } ))
#  Run iJRF and obtain importance score for P permuted data sets
out<iJRF_Perm(X=list(Cov1,Cov2),Y=list(Res1,Res2),W=W,P=2)

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