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BioC 3.1: BUILD report for predictionet on petty

This page was generated on 2015-10-09 09:34:47 -0700 (Fri, 09 Oct 2015).

Package 739/1024HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
predictionet 1.14.0
Benjamin Haibe-Kains , Catharina Olsen
Snapshot Date: 2015-10-08 17:20:21 -0700 (Thu, 08 Oct 2015)
URL: https://hedgehog.fhcrc.org/bioconductor/branches/RELEASE_3_1/madman/Rpacks/predictionet
Last Changed Rev: 102591 / Revision: 109384
Last Changed Date: 2015-04-16 12:42:01 -0700 (Thu, 16 Apr 2015)
zin2 Linux (Ubuntu 14.04.2 LTS) / x86_64  NotNeeded  OK  OK UNNEEDED, same version exists in internal repository
moscato2 Windows Server 2008 R2 Enterprise SP1 (64-bit) / x64 ...NOT SUPPORTED...NOT SUPPORTED...NOT SUPPORTED...
petty Mac OS X Snow Leopard (10.6.8) / x86_64  NotNeeded [ OK ] OK  OK UNNEEDED, same version exists in internal repository
morelia Mac OS X Mavericks (10.9.5) / x86_64  NotNeeded  OK  OK  OK UNNEEDED, same version exists in internal repository

Summary

Package: predictionet
Version: 1.14.0
Command: /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD build --keep-empty-dirs --no-resave-data predictionet
StartedAt: 2015-10-08 19:27:32 -0700 (Thu, 08 Oct 2015)
EndedAt: 2015-10-08 19:28:28 -0700 (Thu, 08 Oct 2015)
EllapsedTime: 56.2 seconds
RetCode: 0
Status:  OK 
PackageFile: predictionet_1.14.0.tar.gz
PackageFileSize: 2.597 MiB

Command output

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###
### Running command:
###
###   /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD build --keep-empty-dirs --no-resave-data predictionet
###
##############################################################################
##############################################################################


* 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 ...

This is pdfTeX, Version 3.14159265-2.6-1.40.15 (TeX Live 2014) (preloaded format=pdflatex)
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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[] 

Overfull \hbox (592.08102pt too wide) in paragraph at lines 228--228
 []\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]][] 

Overfull \hbox (240.18094pt too wide) in paragraph at lines 452--452
 []\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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Overfull \hbox (583.08102pt too wide) in paragraph at lines 552--552
 []\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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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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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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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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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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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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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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] 
[19]
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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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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

Overfull \hbox (158.281pt too wide) in paragraph at lines 516--516
 []\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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Saving output to ‘/private/tmp/RtmpGZ4AEn/Rbuild69bd501bae34/predictionet/build/predictionet.pdf’ ...
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