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This page was generated on 2021-08-02 12:05:21 -0400 (Mon, 02 Aug 2021).

BUILD results for predictionet on merida1

To the developers/maintainers of the predictionet package:
- Please allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/predictionet.git to
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raw results

Package 1408/2015HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
predictionet 1.39.0  (landing page)
Benjamin Haibe-Kains
Snapshot Date: 2021-08-01 14:50:04 -0400 (Sun, 01 Aug 2021)
URL: https://git.bioconductor.org/packages/predictionet
Branch: master
Last Commit: ffdf209
Last Changed Date: 2021-05-19 11:50:21 -0400 (Wed, 19 May 2021)
nebbiolo2Linux (Ubuntu 20.04.2 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
riesling1Windows Server 2019 Standard / x64... NOT SUPPORTED ...
merida1macOS 10.14.6 Mojave / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published

Summary

Package: predictionet
Version: 1.39.0
Command: /Library/Frameworks/R.framework/Resources/bin/R CMD build --keep-empty-dirs --no-resave-data predictionet
StartedAt: 2021-08-01 21:27:44 -0400 (Sun, 01 Aug 2021)
EndedAt: 2021-08-01 21:28:46 -0400 (Sun, 01 Aug 2021)
EllapsedTime: 62.3 seconds
RetCode: 0
Status:   OK  
PackageFile: predictionet_1.39.0.tar.gz
PackageFileSize: 2.619 MiB

Command output

##############################################################################
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###
### Running command:
###
###   /Library/Frameworks/R.framework/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 ...

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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]))[] 
[3] [4]
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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. 
[5] [6] [7]
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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 (234.75328pt too wide) in paragraph at lines 457--457
 []\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)[] 
[14]
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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)[] 
[17]
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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]))[] 
[3] [4]
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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]]
) })[] 
[5]
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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. 
[6] [7]
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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)[] 
[12]
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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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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")[] 
[16]
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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)[] 
[17] [18]
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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 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="
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Overfull \hbox (128.25635pt too wide) in paragraph at lines 137--137
 []\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]))[] 
[3] [4]
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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]]
) })[] 
[5]
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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. 
[6] [7]
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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]
Overfull \hbox (234.75328pt too wide) in paragraph at lines 457--457
 []\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)[] 
[12]
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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")[] 
[16]
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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)[] 
[17] [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)[] 

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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 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
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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]))[] 
[3] [4]
Overfull \hbox (146.21954pt too wide) in paragraph at lines 228--228
 []\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 (578.21034pt too wide) in paragraph at lines 233--233
 []\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]]
) })[] 
[5]
Overfull \hbox (13.78735pt too wide) in paragraph at lines 254--255
[]\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. 
[6] [7]
Overfull \hbox (128.08014pt too wide) in paragraph at lines 412--412
 []\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

Overfull \hbox (146.21954pt too wide) in paragraph at lines 438--438
 []\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 (62.26862pt too wide) in paragraph at lines 446--446
 []\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][] 
[9]
Overfull \hbox (234.75328pt too wide) in paragraph at lines 457--457
 []\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)[] 

Overfull \hbox (1117.99835pt too wide) in paragraph at lines 472--472
 []\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]
Overfull \hbox (30.51768pt too wide) in paragraph at lines 489--490
\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 (146.21954pt too wide) in paragraph at lines 521--521
 []\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]
Overfull \hbox (62.26862pt too wide) in paragraph at lines 529--529
 []\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][] 

Overfull \hbox (261.75328pt too wide) in paragraph at lines 540--540
 []\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]])[] 

Overfull \hbox (369.75328pt too wide) in paragraph at lines 546--546
 []\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/local/texlive/2020/texmf-dist/tex/latex/inconsolata/ts1zi4.fd)
Overfull \hbox (569.21034pt too wide) in paragraph at lines 557--557
 []\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]]) 
})[] 

Overfull \hbox (315.74408pt too wide) in paragraph at lines 561--561
 []\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)[] 

Overfull \hbox (176.25328pt too wide) in paragraph at lines 566--566
 []\T1/zi4/m/n/9 ## Not run: xnet <- network(x=mytopo, matrix.type="adjacency",
 directed=TRUE, loops=FALSE, vertex.attrnames=dimnames(mytopo)[[1]])[] 

Overfull \hbox (419.24408pt too wide) in paragraph at lines 567--567
 []\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)[] 
[12]
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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) 

Overfull \hbox (25.18864pt too wide) in paragraph at lines 601--602
[]\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 

Overfull \hbox (62.26862pt too wide) in paragraph at lines 644--644
 []\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][] 
[14]
Overfull \hbox (221.25328pt too wide) in paragraph at lines 655--655
 []\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)[
] 

Overfull \hbox (359.24101pt too wide) in paragraph at lines 668--668
 []\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)[] 

Overfull \hbox (161.42029pt too wide) in paragraph at lines 688--688
 []\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 

Overfull \hbox (62.26862pt too wide) in paragraph at lines 719--719
 []\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][] 

Overfull \hbox (164.25635pt too wide) in paragraph at lines 725--725
 []\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)[] 

Overfull \hbox (33.75635pt too wide) in paragraph at lines 734--734
 []\T1/zi4/m/n/9 plot(mydata[ ,goi[1]], mypreds, xlab="Observed gene expression
", ylab="Predicted gene expression")[] 
[16]
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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 (62.26862pt too wide) in paragraph at lines 791--791
 []\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][] 

Overfull \hbox (278.25021pt too wide) in paragraph at lines 797--797
 []\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)[] 
[17] [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 

Overfull \hbox (62.26862pt too wide) in paragraph at lines 900--900
 []\T1/zi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][] 

Overfull \hbox (221.25328pt too wide) in paragraph at lines 911--911
 []\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]
Overfull \hbox (359.24101pt too wide) in paragraph at lines 924--924
 []\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)[] 

Overfull \hbox (1037.98813pt too wide) in paragraph at lines 944--944
 []\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 

Overfull \hbox (62.26862pt too wide) in paragraph at lines 994--994
 []\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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Transcript written on Rd2.log.
Saving output to ‘/private/tmp/Rtmp4Quxtj/Rbuild801965280f0f/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
* building ‘predictionet_1.39.0.tar.gz’