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BUILD report for predictionet on celaya2

This page was generated on 2020-01-16 13:34:05 -0500 (Thu, 16 Jan 2020).

Package 1295/1818HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
predictionet 1.33.0
Benjamin Haibe-Kains
Snapshot Date: 2020-01-15 16:46:30 -0500 (Wed, 15 Jan 2020)
URL: https://git.bioconductor.org/packages/predictionet
Branch: master
Last Commit: 7ab5f4a
Last Changed Date: 2019-10-29 13:35:57 -0500 (Tue, 29 Oct 2019)
malbec2 Linux (Ubuntu 18.04.3 LTS) / x86_64  OK  OK  OK UNNEEDED, same version exists in internal repository
tokay2 Windows Server 2012 R2 Standard / x64 ...NOT SUPPORTED...
celaya2 OS X 10.11.6 El Capitan / x86_64  OK [ OK ] OK  OK UNNEEDED, same version exists in internal repository

Summary

Package: predictionet
Version: 1.33.0
Command: /Library/Frameworks/R.framework/Versions/Current/Resources/bin/R CMD build --keep-empty-dirs --no-resave-data predictionet
StartedAt: 2020-01-15 20:38:51 -0500 (Wed, 15 Jan 2020)
EndedAt: 2020-01-15 20:39:56 -0500 (Wed, 15 Jan 2020)
EllapsedTime: 65.2 seconds
RetCode: 0
Status:  OK 
PackageFile: predictionet_1.33.0.tar.gz
PackageFileSize: 2.611 MiB

Command output

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### Running command:
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###   /Library/Frameworks/R.framework/Versions/Current/Resources/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 ...

This is pdfTeX, Version 3.14159265-2.6-1.40.19 (TeX Live 2018) (preloaded format=pdflatex)
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) (/usr/local/texlive/2018/texmf-dist/tex/latex/hyperref/nameref.sty
(/usr/local/texlive/2018/texmf-dist/tex/generic/oberdiek/gettitlestring.sty))
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ive/2018/texmf-var/fonts/map/pdftex/updmap/pdftex.map}] [2]
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]
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]]
) })[] 

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. 
[5] [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]
Overfull \hbox (25.18864pt too wide) in paragraph at lines 423--424
[]\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]][] 

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)[] 
[9]
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
[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 (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)[] 

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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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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]
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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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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]]) 
})[] 

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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 ## 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]))[] 
[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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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)[] 

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)[] 

Overfull \hbox (126.76248pt too wide) in paragraph at lines 545--545
 []\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/local/texlive/2018/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]
Overfull \hbox (1282.96768pt too wide) in paragraph at lines 583--583
 []\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]
Overfull \hbox (146.21954pt too wide) in paragraph at lines 636--636
 []\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]
Overfull \hbox (146.21954pt too wide) in paragraph at lines 714--714
 []\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]
Overfull \hbox (146.21954pt too wide) in paragraph at lines 786--786
 []\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]
Overfull \hbox (146.21954pt too wide) in paragraph at lines 892--892
 []\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]
Overfull \hbox (146.21954pt too wide) in paragraph at lines 986--986
 []\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]
Overfull \hbox (218.25635pt too wide) in paragraph at lines 1005--1005
 []\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)[]
 

Overfull \hbox (359.24101pt too wide) in paragraph at lines 1018--1018
 []\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)[] 
(./Rd2.ind [22]

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Output written on Rd2.pdf (23 pages, 142997 bytes).
Transcript written on Rd2.log.
Saving output to ‘/private/tmp/RtmpG63eMA/Rbuild13d7a1ae7429a/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_1.33.0.tar.gz’