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### /home/biocbuild/bbs-3.9-bioc/R/bin/R CMD build --keep-empty-dirs --no-resave-data predictionet
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* checking for file ‘predictionet/DESCRIPTION’ ... OK
* preparing ‘predictionet’:
* checking DESCRIPTION meta-information ... OK
* cleaning src
* installing the package to process help pages
* building the PDF package manual
Hmm ... looks like a package
Converting Rd files to LaTeX .
Creating pdf output from LaTeX ...
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[]\T1/zi4/m/n/9 mytopo <- matrix(sample(0:1, 100, replace=TRUE, prob=c(0.7,0.3
)), nrow=10, dimnames=list(LETTERS[1:10], LETTERS[1:10]))[]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 cuts.discr <- lapply(apply(rbind("nbcat"=categories, data.ras)
, 2, function(x) { y <- x[1]; x <- x[-1]; return(list(quantile(x=x, probs=seq(0
, 1, length.out=y+1), na.rm=TRUE)[-c(1, y+1)])) }), function(x) { return(x[[1]]
) })[]
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[]\T1/ptm/m/n/10 Inferred topol-ogy, an edge be-tween to vari-ables X and Y cor
-re-sponds to net[X,Y]=1.
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[]\T1/zi4/m/n/10 net2pred(net, data, categories, predn, perturbations, method
= c("linear", "linear.penalized", "cpt"), seed)[]
[8]
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[]\T1/ptm/m/n/10 type of pre-dic-tive model to fit; \T1/zi4/m/n/10 linear \T1/p
tm/m/n/10 for lin-ear re-gres-sion model, \T1/zi4/m/n/10 linear.penalized
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 mynet <- netinf(data=mydata, perturbations=mypert, priors=mypr
iors, priors.count=TRUE, priors.weight=0.5, maxparents=3, method="regrnet", see
d)[]
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[]\T1/zi4/m/n/10 netinf(data, categories, perturbations, priors, predn, priors
.count = TRUE, priors.weight = 0.5, maxparents = 3, subset, method = c("regrnet
", "bayesnet"), ensemble = FALSE, ensemble.model = c("full","best"), ensemble.m
axnsol = 3, causal=TRUE, seed, bayesnet.maxcomplexity=0, bayesnet.maxiter=100,
verbose = FALSE)[]
[9]
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\T1/ptm/m/n/10 iden-tify all the sta-tis-ti-cally equiv-a-lent net-works, it \T
1/zi4/m/n/10 best \T1/ptm/m/n/10 only the top \T1/zi4/m/n/10 ensemble.maxnsol
[10]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 mynet <- netinf(data=mydata, perturbations=mypert, priors=mypr
iors, priors.count=TRUE, priors.weight=0.5, maxparents=3, method="regrnet", see
d=54321)[]
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[]\T1/zi4/m/n/9 xnet <- network(x=mytopo, matrix.type="adjacency", directed=TR
UE, loops=FALSE, vertex.attrnames=dimnames(mytopo)[[1]])[]
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[]\T1/zi4/m/n/9 plot.network(x=xnet, displayisolates=TRUE, displaylabels=TRUE,
boxed.labels=FALSE, label.pos=0, arrowhead.cex=2, vertex.cex=4, vertex.col="ro
yalblue", jitter=FALSE, pad=0.5)[]
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[]\T1/zi4/m/n/9 cuts.discr <- lapply(apply(rbind("nbcat"=categories, mydata),
2, function(x) { y <- x[1]; x <- x[-1]; return(list(quantile(x=x, probs=seq(0,
1, length.out=y+1), na.rm=TRUE)[-c(1, y+1)])) }), function(x) { return(x[[1]])
})[]
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[]\T1/zi4/m/n/9 ## Not run: mynet <- netinf(data=mydata, perturbations=mypert,
priors=mypriors, priors.count=TRUE, priors.weight=0.5, maxparents=3, method="b
ayesnet", seed=54321)[]
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[]\T1/zi4/m/n/9 ## Not run: xnet <- network(x=mytopo, matrix.type="adjacency",
directed=TRUE, loops=FALSE, vertex.attrnames=dimnames(mytopo)[[1]])[]
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[]\T1/zi4/m/n/9 ## Not run: plot.network(x=xnet, displayisolates=TRUE, display
labels=TRUE, boxed.labels=FALSE, label.pos=0, arrowhead.cex=2, vertex.cex=4, ve
rtex.col="royalblue", jitter=FALSE, pad=0.5)[]
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[]\T1/zi4/m/n/10 netinf.cv(data, categories, perturbations, priors, predn, pri
ors.count = TRUE, priors.weight = 0.5, maxparents = 3, subset, method = c("regr
net", "bayesnet"), ensemble = FALSE, ensemble.maxnsol=3, predmodel = c("linear"
, "linear.penalized", "cpt"), nfold = 10, causal = TRUE, seed, bayesnet.maxcomp
lexity = 0, bayesnet.maxiter = 100, verbose = FALSE)[]
[12]
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[]\T1/ptm/m/n/10 type of pre-dic-tive model to fit; \T1/zi4/m/n/10 linear \T1/p
tm/m/n/10 for lin-ear re-gres-sion model, \T1/zi4/m/n/10 linear.penalized
[13]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 res <- netinf.cv(data=mydata, categories=3, perturbations=mype
rt, priors=mypriors, priors.weight=0.5, method="regrnet", nfold=3, seed=54321)[
]
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[]\T1/zi4/m/n/9 ## Not run: res <- netinf.cv(data=mydata, categories=3, pertur
bations=mypert, priors=mypriors, priors.count=TRUE, priors.weight=0.5, method="
bayesnet", nfold=3, seed=54321)[]
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[]\T1/zi4/m/n/10 netinf.predict(net, data, categories, perturbations, subset,
predn, method=c("linear", "linear.penalized", "cpt"))[]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 mynet <- netinf(data=mydata, priors=mypriors, priors.count=TRU
E, priors.weight=0.5, maxparents=3, method="regrnet", seed=54321)[]
[15]
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[]\T1/zi4/m/n/9 plot(mydata[ ,goi[1]], mypreds, xlab="Observed gene expression
", ylab="Predicted gene expression")[]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
[16]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 mynet <- netinf.cv(data=mydata, categories=3, priors=mypriors,
priors.count=TRUE, priors.weight=0.5, maxparents=3, method="regrnet", nfold=3,
seed=54321)[]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
[18]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 res <- netinf.cv(data=mydata, categories=3, perturbations=mype
rt, priors=mypriors, priors.weight=0.5, method="regrnet", nfold=3, seed=54321)[
]
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[]\T1/zi4/m/n/9 ## Not run: res <- netinf.cv(data=mydata, categories=3, pertur
bations=mypert, priors=mypriors, priors.count=TRUE, priors.weight=0.5, method="
bayesnet", nfold=3, seed=54321)[]
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[]\T1/zi4/m/n/10 predictionet.stability.cv(data, categories, perturbations, pr
iors, predn, priors.count = TRUE, priors.weight = 0.5, maxparents = 3, subset,
method = c("regrnet", "bayesnet"), ensemble = FALSE, ensemble.maxnsol = 3, nfol
d = 10, causal = TRUE, seed, bayesnet.maxcomplexity = 0, bayesnet.maxiter = 100
)[]
[19]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
[20]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 res <-netinf.cv(data=mydata, categories=3, perturbations=myper
t, priors=mypriors, priors.weight=0.5, method="regrnet", nfold=3, seed=54321)[]
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[]\T1/zi4/m/n/9 ## Not run: res <- netinf.cv(data=mydata, categories=3, pertur
bations=mypert, priors=mypriors, priors.count=TRUE, priors.weight=0.5, method="
bayesnet", nfold=3, seed=54321)[]
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[]\T1/zi4/m/n/9 mytopo <- matrix(sample(0:1, 100, replace=TRUE, prob=c(0.7,0.3
)), nrow=10, dimnames=list(LETTERS[1:10], LETTERS[1:10]))[]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 cuts.discr <- lapply(apply(rbind("nbcat"=categories, data.ras)
, 2, function(x) { y <- x[1]; x <- x[-1]; return(list(quantile(x=x, probs=seq(0
, 1, length.out=y+1), na.rm=TRUE)[-c(1, y+1)])) }), function(x) { return(x[[1]]
) })[]
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[]\T1/ptm/m/n/10 Inferred topol-ogy, an edge be-tween to vari-ables X and Y cor
-re-sponds to net[X,Y]=1.
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[]\T1/zi4/m/n/10 net2pred(net, data, categories, predn, perturbations, method
= c("linear", "linear.penalized", "cpt"), seed)[]
[8]
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[]\T1/ptm/m/n/10 type of pre-dic-tive model to fit; \T1/zi4/m/n/10 linear \T1/p
tm/m/n/10 for lin-ear re-gres-sion model, \T1/zi4/m/n/10 linear.penalized
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
[9]
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[]\T1/zi4/m/n/9 mynet <- netinf(data=mydata, perturbations=mypert, priors=mypr
iors, priors.count=TRUE, priors.weight=0.5, maxparents=3, method="regrnet", see
d)[]
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[]\T1/zi4/m/n/10 netinf(data, categories, perturbations, priors, predn, priors
.count = TRUE, priors.weight = 0.5, maxparents = 3, subset, method = c("regrnet
", "bayesnet"), ensemble = FALSE, ensemble.model = c("full","best"), ensemble.m
axnsol = 3, causal=TRUE, seed, bayesnet.maxcomplexity=0, bayesnet.maxiter=100,
verbose = FALSE)[]
[10]
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\T1/ptm/m/n/10 iden-tify all the sta-tis-ti-cally equiv-a-lent net-works, it \T
1/zi4/m/n/10 best \T1/ptm/m/n/10 only the top \T1/zi4/m/n/10 ensemble.maxnsol
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
[11]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 mynet <- netinf(data=mydata, perturbations=mypert, priors=mypr
iors, priors.count=TRUE, priors.weight=0.5, maxparents=3, method="regrnet", see
d=54321)[]
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[]\T1/zi4/m/n/9 xnet <- network(x=mytopo, matrix.type="adjacency", directed=TR
UE, loops=FALSE, vertex.attrnames=dimnames(mytopo)[[1]])[]
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[]\T1/zi4/m/n/9 plot.network(x=xnet, displayisolates=TRUE, displaylabels=TRUE,
boxed.labels=FALSE, label.pos=0, arrowhead.cex=2, vertex.cex=4, vertex.col="ro
yalblue", jitter=FALSE, pad=0.5)[]
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[]\T1/zi4/m/n/9 cuts.discr <- lapply(apply(rbind("nbcat"=categories, mydata),
2, function(x) { y <- x[1]; x <- x[-1]; return(list(quantile(x=x, probs=seq(0,
1, length.out=y+1), na.rm=TRUE)[-c(1, y+1)])) }), function(x) { return(x[[1]])
})[]
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[]\T1/zi4/m/n/9 ## Not run: mynet <- netinf(data=mydata, perturbations=mypert,
priors=mypriors, priors.count=TRUE, priors.weight=0.5, maxparents=3, method="b
ayesnet", seed=54321)[]
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[]\T1/zi4/m/n/9 ## Not run: xnet <- network(x=mytopo, matrix.type="adjacency",
directed=TRUE, loops=FALSE, vertex.attrnames=dimnames(mytopo)[[1]])[]
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[]\T1/zi4/m/n/9 ## Not run: plot.network(x=xnet, displayisolates=TRUE, display
labels=TRUE, boxed.labels=FALSE, label.pos=0, arrowhead.cex=2, vertex.cex=4, ve
rtex.col="royalblue", jitter=FALSE, pad=0.5)[]
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[]\T1/zi4/m/n/10 netinf.cv(data, categories, perturbations, priors, predn, pri
ors.count = TRUE, priors.weight = 0.5, maxparents = 3, subset, method = c("regr
net", "bayesnet"), ensemble = FALSE, ensemble.maxnsol=3, predmodel = c("linear"
, "linear.penalized", "cpt"), nfold = 10, causal = TRUE, seed, bayesnet.maxcomp
lexity = 0, bayesnet.maxiter = 100, verbose = FALSE)[]
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[]\T1/ptm/m/n/10 type of pre-dic-tive model to fit; \T1/zi4/m/n/10 linear \T1/p
tm/m/n/10 for lin-ear re-gres-sion model, \T1/zi4/m/n/10 linear.penalized
[13]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
[14]
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[]\T1/zi4/m/n/9 res <- netinf.cv(data=mydata, categories=3, perturbations=mype
rt, priors=mypriors, priors.weight=0.5, method="regrnet", nfold=3, seed=54321)[
]
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[]\T1/zi4/m/n/9 ## Not run: res <- netinf.cv(data=mydata, categories=3, pertur
bations=mypert, priors=mypriors, priors.count=TRUE, priors.weight=0.5, method="
bayesnet", nfold=3, seed=54321)[]
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[]\T1/zi4/m/n/10 netinf.predict(net, data, categories, perturbations, subset,
predn, method=c("linear", "linear.penalized", "cpt"))[]
[15]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 mynet <- netinf(data=mydata, priors=mypriors, priors.count=TRU
E, priors.weight=0.5, maxparents=3, method="regrnet", seed=54321)[]
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[]\T1/zi4/m/n/9 plot(mydata[ ,goi[1]], mypreds, xlab="Observed gene expression
", ylab="Predicted gene expression")[]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 mynet <- netinf.cv(data=mydata, categories=3, priors=mypriors,
priors.count=TRUE, priors.weight=0.5, maxparents=3, method="regrnet", nfold=3,
seed=54321)[]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 res <- netinf.cv(data=mydata, categories=3, perturbations=mype
rt, priors=mypriors, priors.weight=0.5, method="regrnet", nfold=3, seed=54321)[
]
[19]
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[]\T1/zi4/m/n/9 ## Not run: res <- netinf.cv(data=mydata, categories=3, pertur
bations=mypert, priors=mypriors, priors.count=TRUE, priors.weight=0.5, method="
bayesnet", nfold=3, seed=54321)[]
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[]\T1/zi4/m/n/10 predictionet.stability.cv(data, categories, perturbations, pr
iors, predn, priors.count = TRUE, priors.weight = 0.5, maxparents = 3, subset,
method = c("regrnet", "bayesnet"), ensemble = FALSE, ensemble.maxnsol = 3, nfol
d = 10, causal = TRUE, seed, bayesnet.maxcomplexity = 0, bayesnet.maxiter = 100
)[]
[20]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
[21]
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[]\T1/zi4/m/n/9 res <-netinf.cv(data=mydata, categories=3, perturbations=myper
t, priors=mypriors, priors.weight=0.5, method="regrnet", nfold=3, seed=54321)[]
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[]\T1/zi4/m/n/9 ## Not run: res <- netinf.cv(data=mydata, categories=3, pertur
bations=mypert, priors=mypriors, priors.count=TRUE, priors.weight=0.5, method="
bayesnet", nfold=3, seed=54321)[]
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[]\T1/zi4/m/n/9 mytopo <- matrix(sample(0:1, 100, replace=TRUE, prob=c(0.7,0.3
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 cuts.discr <- lapply(apply(rbind("nbcat"=categories, data.ras)
, 2, function(x) { y <- x[1]; x <- x[-1]; return(list(quantile(x=x, probs=seq(0
, 1, length.out=y+1), na.rm=TRUE)[-c(1, y+1)])) }), function(x) { return(x[[1]]
) })[]
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[]\T1/ptm/m/n/10 Inferred topol-ogy, an edge be-tween to vari-ables X and Y cor
-re-sponds to net[X,Y]=1.
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[]\T1/zi4/m/n/10 net2pred(net, data, categories, predn, perturbations, method
= c("linear", "linear.penalized", "cpt"), seed)[]
[8]
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[]\T1/ptm/m/n/10 type of pre-dic-tive model to fit; \T1/zi4/m/n/10 linear \T1/p
tm/m/n/10 for lin-ear re-gres-sion model, \T1/zi4/m/n/10 linear.penalized
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
[9]
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[]\T1/zi4/m/n/9 mynet <- netinf(data=mydata, perturbations=mypert, priors=mypr
iors, priors.count=TRUE, priors.weight=0.5, maxparents=3, method="regrnet", see
d)[]
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[]\T1/zi4/m/n/10 netinf(data, categories, perturbations, priors, predn, priors
.count = TRUE, priors.weight = 0.5, maxparents = 3, subset, method = c("regrnet
", "bayesnet"), ensemble = FALSE, ensemble.model = c("full","best"), ensemble.m
axnsol = 3, causal=TRUE, seed, bayesnet.maxcomplexity=0, bayesnet.maxiter=100,
verbose = FALSE)[]
[10]
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\T1/ptm/m/n/10 iden-tify all the sta-tis-ti-cally equiv-a-lent net-works, it \T
1/zi4/m/n/10 best \T1/ptm/m/n/10 only the top \T1/zi4/m/n/10 ensemble.maxnsol
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
[11]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 mynet <- netinf(data=mydata, perturbations=mypert, priors=mypr
iors, priors.count=TRUE, priors.weight=0.5, maxparents=3, method="regrnet", see
d=54321)[]
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[]\T1/zi4/m/n/9 xnet <- network(x=mytopo, matrix.type="adjacency", directed=TR
UE, loops=FALSE, vertex.attrnames=dimnames(mytopo)[[1]])[]
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[]\T1/zi4/m/n/9 plot.network(x=xnet, displayisolates=TRUE, displaylabels=TRUE,
boxed.labels=FALSE, label.pos=0, arrowhead.cex=2, vertex.cex=4, vertex.col="ro
yalblue", jitter=FALSE, pad=0.5)[]
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[]\T1/zi4/m/n/9 cuts.discr <- lapply(apply(rbind("nbcat"=categories, mydata),
2, function(x) { y <- x[1]; x <- x[-1]; return(list(quantile(x=x, probs=seq(0,
1, length.out=y+1), na.rm=TRUE)[-c(1, y+1)])) }), function(x) { return(x[[1]])
})[]
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[]\T1/zi4/m/n/9 ## Not run: mynet <- netinf(data=mydata, perturbations=mypert,
priors=mypriors, priors.count=TRUE, priors.weight=0.5, maxparents=3, method="b
ayesnet", seed=54321)[]
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[]\T1/zi4/m/n/9 ## Not run: xnet <- network(x=mytopo, matrix.type="adjacency",
directed=TRUE, loops=FALSE, vertex.attrnames=dimnames(mytopo)[[1]])[]
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[]\T1/zi4/m/n/9 ## Not run: plot.network(x=xnet, displayisolates=TRUE, display
labels=TRUE, boxed.labels=FALSE, label.pos=0, arrowhead.cex=2, vertex.cex=4, ve
rtex.col="royalblue", jitter=FALSE, pad=0.5)[]
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[]\T1/zi4/m/n/10 netinf.cv(data, categories, perturbations, priors, predn, pri
ors.count = TRUE, priors.weight = 0.5, maxparents = 3, subset, method = c("regr
net", "bayesnet"), ensemble = FALSE, ensemble.maxnsol=3, predmodel = c("linear"
, "linear.penalized", "cpt"), nfold = 10, causal = TRUE, seed, bayesnet.maxcomp
lexity = 0, bayesnet.maxiter = 100, verbose = FALSE)[]
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[]\T1/ptm/m/n/10 type of pre-dic-tive model to fit; \T1/zi4/m/n/10 linear \T1/p
tm/m/n/10 for lin-ear re-gres-sion model, \T1/zi4/m/n/10 linear.penalized
[13]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
[14]
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[]\T1/zi4/m/n/9 res <- netinf.cv(data=mydata, categories=3, perturbations=mype
rt, priors=mypriors, priors.weight=0.5, method="regrnet", nfold=3, seed=54321)[
]
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[]\T1/zi4/m/n/9 ## Not run: res <- netinf.cv(data=mydata, categories=3, pertur
bations=mypert, priors=mypriors, priors.count=TRUE, priors.weight=0.5, method="
bayesnet", nfold=3, seed=54321)[]
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[]\T1/zi4/m/n/10 netinf.predict(net, data, categories, perturbations, subset,
predn, method=c("linear", "linear.penalized", "cpt"))[]
[15]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 mynet <- netinf(data=mydata, priors=mypriors, priors.count=TRU
E, priors.weight=0.5, maxparents=3, method="regrnet", seed=54321)[]
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[]\T1/zi4/m/n/9 plot(mydata[ ,goi[1]], mypreds, xlab="Observed gene expression
", ylab="Predicted gene expression")[]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 mynet <- netinf.cv(data=mydata, categories=3, priors=mypriors,
priors.count=TRUE, priors.weight=0.5, maxparents=3, method="regrnet", nfold=3,
seed=54321)[]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 res <- netinf.cv(data=mydata, categories=3, perturbations=mype
rt, priors=mypriors, priors.weight=0.5, method="regrnet", nfold=3, seed=54321)[
]
[19]
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[]\T1/zi4/m/n/9 ## Not run: res <- netinf.cv(data=mydata, categories=3, pertur
bations=mypert, priors=mypriors, priors.count=TRUE, priors.weight=0.5, method="
bayesnet", nfold=3, seed=54321)[]
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[]\T1/zi4/m/n/10 predictionet.stability.cv(data, categories, perturbations, pr
iors, predn, priors.count = TRUE, priors.weight = 0.5, maxparents = 3, subset,
method = c("regrnet", "bayesnet"), ensemble = FALSE, ensemble.maxnsol = 3, nfol
d = 10, causal = TRUE, seed, bayesnet.maxcomplexity = 0, bayesnet.maxiter = 100
)[]
[20]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
[21]
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[]\T1/zi4/m/n/9 res <-netinf.cv(data=mydata, categories=3, perturbations=myper
t, priors=mypriors, priors.weight=0.5, method="regrnet", nfold=3, seed=54321)[]
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[]\T1/zi4/m/n/9 ## Not run: res <- netinf.cv(data=mydata, categories=3, pertur
bations=mypert, priors=mypriors, priors.count=TRUE, priors.weight=0.5, method="
bayesnet", nfold=3, seed=54321)[]
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[]\T1/zi4/m/n/9 mytopo <- matrix(sample(0:1, 100, replace=TRUE, prob=c(0.7,0.3
)), nrow=10, dimnames=list(LETTERS[1:10], LETTERS[1:10]))[]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 cuts.discr <- lapply(apply(rbind("nbcat"=categories, data.ras)
, 2, function(x) { y <- x[1]; x <- x[-1]; return(list(quantile(x=x, probs=seq(0
, 1, length.out=y+1), na.rm=TRUE)[-c(1, y+1)])) }), function(x) { return(x[[1]]
) })[]
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[]\T1/ptm/m/n/10 Inferred topol-ogy, an edge be-tween to vari-ables X and Y cor
-re-sponds to net[X,Y]=1.
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[]\T1/zi4/m/n/10 net2pred(net, data, categories, predn, perturbations, method
= c("linear", "linear.penalized", "cpt"), seed)[]
[8]
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[]\T1/ptm/m/n/10 type of pre-dic-tive model to fit; \T1/zi4/m/n/10 linear \T1/p
tm/m/n/10 for lin-ear re-gres-sion model, \T1/zi4/m/n/10 linear.penalized
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
[9]
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[]\T1/zi4/m/n/9 mynet <- netinf(data=mydata, perturbations=mypert, priors=mypr
iors, priors.count=TRUE, priors.weight=0.5, maxparents=3, method="regrnet", see
d)[]
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[]\T1/zi4/m/n/10 netinf(data, categories, perturbations, priors, predn, priors
.count = TRUE, priors.weight = 0.5, maxparents = 3, subset, method = c("regrnet
", "bayesnet"), ensemble = FALSE, ensemble.model = c("full","best"), ensemble.m
axnsol = 3, causal=TRUE, seed, bayesnet.maxcomplexity=0, bayesnet.maxiter=100,
verbose = FALSE)[]
[10]
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\T1/ptm/m/n/10 iden-tify all the sta-tis-ti-cally equiv-a-lent net-works, it \T
1/zi4/m/n/10 best \T1/ptm/m/n/10 only the top \T1/zi4/m/n/10 ensemble.maxnsol
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
[11]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 mynet <- netinf(data=mydata, perturbations=mypert, priors=mypr
iors, priors.count=TRUE, priors.weight=0.5, maxparents=3, method="regrnet", see
d=54321)[]
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[]\T1/zi4/m/n/9 xnet <- network(x=mytopo, matrix.type="adjacency", directed=TR
UE, loops=FALSE, vertex.attrnames=dimnames(mytopo)[[1]])[]
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[]\T1/zi4/m/n/9 plot.network(x=xnet, displayisolates=TRUE, displaylabels=TRUE,
boxed.labels=FALSE, label.pos=0, arrowhead.cex=2, vertex.cex=4, vertex.col="ro
yalblue", jitter=FALSE, pad=0.5)[]
(/usr/share/texlive/texmf-dist/tex/latex/inconsolata/ts1zi4.fd)
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[]\T1/zi4/m/n/9 cuts.discr <- lapply(apply(rbind("nbcat"=categories, mydata),
2, function(x) { y <- x[1]; x <- x[-1]; return(list(quantile(x=x, probs=seq(0,
1, length.out=y+1), na.rm=TRUE)[-c(1, y+1)])) }), function(x) { return(x[[1]])
})[]
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[]\T1/zi4/m/n/9 ## Not run: mynet <- netinf(data=mydata, perturbations=mypert,
priors=mypriors, priors.count=TRUE, priors.weight=0.5, maxparents=3, method="b
ayesnet", seed=54321)[]
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[]\T1/zi4/m/n/9 ## Not run: xnet <- network(x=mytopo, matrix.type="adjacency",
directed=TRUE, loops=FALSE, vertex.attrnames=dimnames(mytopo)[[1]])[]
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[]\T1/zi4/m/n/9 ## Not run: plot.network(x=xnet, displayisolates=TRUE, display
labels=TRUE, boxed.labels=FALSE, label.pos=0, arrowhead.cex=2, vertex.cex=4, ve
rtex.col="royalblue", jitter=FALSE, pad=0.5)[]
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[]\T1/zi4/m/n/10 netinf.cv(data, categories, perturbations, priors, predn, pri
ors.count = TRUE, priors.weight = 0.5, maxparents = 3, subset, method = c("regr
net", "bayesnet"), ensemble = FALSE, ensemble.maxnsol=3, predmodel = c("linear"
, "linear.penalized", "cpt"), nfold = 10, causal = TRUE, seed, bayesnet.maxcomp
lexity = 0, bayesnet.maxiter = 100, verbose = FALSE)[]
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[]\T1/ptm/m/n/10 type of pre-dic-tive model to fit; \T1/zi4/m/n/10 linear \T1/p
tm/m/n/10 for lin-ear re-gres-sion model, \T1/zi4/m/n/10 linear.penalized
[13]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
[14]
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[]\T1/zi4/m/n/9 res <- netinf.cv(data=mydata, categories=3, perturbations=mype
rt, priors=mypriors, priors.weight=0.5, method="regrnet", nfold=3, seed=54321)[
]
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[]\T1/zi4/m/n/9 ## Not run: res <- netinf.cv(data=mydata, categories=3, pertur
bations=mypert, priors=mypriors, priors.count=TRUE, priors.weight=0.5, method="
bayesnet", nfold=3, seed=54321)[]
(/usr/share/texlive/texmf-dist/tex/latex/base/utf8.def)
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[]\T1/zi4/m/n/10 netinf.predict(net, data, categories, perturbations, subset,
predn, method=c("linear", "linear.penalized", "cpt"))[]
[15]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 mynet <- netinf(data=mydata, priors=mypriors, priors.count=TRU
E, priors.weight=0.5, maxparents=3, method="regrnet", seed=54321)[]
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[]\T1/zi4/m/n/9 plot(mydata[ ,goi[1]], mypreds, xlab="Observed gene expression
", ylab="Predicted gene expression")[]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 mynet <- netinf.cv(data=mydata, categories=3, priors=mypriors,
priors.count=TRUE, priors.weight=0.5, maxparents=3, method="regrnet", nfold=3,
seed=54321)[]
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(/usr/share/texlive/texmf-dist/tex/latex/base/utf8.def) [18]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/zi4/m/n/9 res <- netinf.cv(data=mydata, categories=3, perturbations=mype
rt, priors=mypriors, priors.weight=0.5, method="regrnet", nfold=3, seed=54321)[
]
[19]
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[]\T1/zi4/m/n/9 ## Not run: res <- netinf.cv(data=mydata, categories=3, pertur
bations=mypert, priors=mypriors, priors.count=TRUE, priors.weight=0.5, method="
bayesnet", nfold=3, seed=54321)[]
(/usr/share/texlive/texmf-dist/tex/latex/base/utf8.def)
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[]\T1/zi4/m/n/10 predictionet.stability.cv(data, categories, perturbations, pr
iors, predn, priors.count = TRUE, priors.weight = 0.5, maxparents = 3, subset,
method = c("regrnet", "bayesnet"), ensemble = FALSE, ensemble.maxnsol = 3, nfol
d = 10, causal = TRUE, seed, bayesnet.maxcomplexity = 0, bayesnet.maxiter = 100
)[]
[20]
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[]\T1/zi4/m/n/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
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[]\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
[21]
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[]\T1/zi4/m/n/9 res <-netinf.cv(data=mydata, categories=3, perturbations=myper
t, priors=mypriors, priors.weight=0.5, method="regrnet", nfold=3, seed=54321)[]
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[]\T1/zi4/m/n/9 ## Not run: res <- netinf.cv(data=mydata, categories=3, pertur
bations=mypert, priors=mypriors, priors.count=TRUE, priors.weight=0.5, method="
bayesnet", nfold=3, seed=54321)[]
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[23]) (/tmp/RtmpafvedH/Rbuild5ff12ddfd25b/predictionet/.Rd2pdf24561/Rd2.aux)
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)
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Output written on Rd2.pdf (23 pages, 142996 bytes).
Transcript written on Rd2.log.
Saving output to ‘/tmp/RtmpafvedH/Rbuild5ff12ddfd25b/predictionet/build/predictionet.pdf’ ...
Done
* creating vignettes ... OK
* cleaning src
* checking for LF line-endings in source and make files and shell scripts
* checking for empty or unneeded directories
* looking to see if a ‘data/datalist’ file should be added
* building ‘predictionet_1.29.0.tar.gz’
