* 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 .
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LaTeX2e <2009/09/24>
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rman-x-2009-06-19, ngerman-x-2009-06-19, afrikaans, ancientgreek, ibycus, arabi
c, armenian, basque, bulgarian, catalan, pinyin, coptic, croatian, czech, danis
h, dutch, ukenglish, usenglishmax, esperanto, estonian, ethiopic, farsi, finnis
h, french, galician, german, ngerman, swissgerman, monogreek, greek, hungarian,
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iya, panjabi, tamil, telugu, indonesian, interlingua, irish, italian, kurmanji,
lao, latin, latvian, lithuanian, mongolian, mongolianlmc, bokmal, nynorsk, pol
ish, portuguese, romanian, russian, sanskrit, serbian, serbianc, slovak, sloven
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/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/fi4/m/n/10 linear \T1/p
tm/m/n/10 for lin-ear re-gres-sion model, \T1/fi4/m/n/10 linear.penalized
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[]\T1/fi4/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/fi4/m/n/9 pert <- matrix(0, nrow=nrow(data.ras), ncol=ncol(data.ras), di
mnames=dimnames(data.ras))[]
Overfull \hbox (68.28088pt too wide) in paragraph at lines 426--426
[]\T1/fi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
Overfull \hbox (248.28088pt too wide) in paragraph at lines 437--437
[]\T1/fi4/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/fi4/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/fi4/m/n/10 best \T1/ptm/m/n/10 only the top \T1/fi4/m/n/10 ensemble.maxnsol
[10]
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[]\T1/fi4/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/fi4/m/n/9 pert <- matrix(0, nrow=nrow(data.ras), ncol=ncol(data.ras), di
mnames=dimnames(data.ras))[]
Overfull \hbox (68.28088pt too wide) in paragraph at lines 509--509
[]\T1/fi4/m/n/9 goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fo
ld.change"])), decreasing=TRUE)[1:genen]][]
Overfull \hbox (275.28088pt too wide) in paragraph at lines 520--520
[]\T1/fi4/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/fi4/m/n/9 xnet <- network(x=mytopo, matrix.type="adjacency", directed=TR
UE, loops=FALSE, vertex.attrnames=dimnames(mytopo)[[1]])[]
Overfull \hbox (383.28088pt too wide) in paragraph at lines 526--526
[]\T1/fi4/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)[]
[11]
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/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/fi4/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/fi4/m/n/10 linear \T1/p
tm/m/n/10 for lin-ear re-gres-sion model, \T1/fi4/m/n/10 linear.penalized
[13]
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[]\T1/fi4/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/fi4/m/n/9 pert <- matrix(0, nrow=nrow(data.ras), ncol=ncol(data.ras), di
mnames=dimnames(data.ras))[]
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/m/n/10 netinf.predict(net, data, categories, perturbations, subset,
predn, method=c("linear", "linear.penalized", "cpt"))[]
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/m/n/9 plot(mydata[ ,goi[1]], mypreds, xlab="Observed gene expression
", ylab="Predicted gene expression")[]
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/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/fi4/m/n/9 pert <- matrix(0, nrow=nrow(data.ras), ncol=ncol(data.ras), di
mnames=dimnames(data.ras))[]
[18]
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/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/fi4/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/fi4/m/n/9 pert <- matrix(0, nrow=nrow(data.ras), ncol=ncol(data.ras), di
mnames=dimnames(data.ras))[]
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[]\T1/fi4/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/fi4/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/fi4/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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rman-x-2009-06-19, ngerman-x-2009-06-19, afrikaans, ancientgreek, ibycus, arabi
c, armenian, basque, bulgarian, catalan, pinyin, coptic, croatian, czech, danis
h, dutch, ukenglish, usenglishmax, esperanto, estonian, ethiopic, farsi, finnis
h, french, galician, german, ngerman, swissgerman, monogreek, greek, hungarian,
icelandic, assamese, bengali, gujarati, hindi, kannada, malayalam, marathi, or
iya, panjabi, tamil, telugu, indonesian, interlingua, irish, italian, kurmanji,
lao, latin, latvian, lithuanian, mongolian, mongolianlmc, bokmal, nynorsk, pol
ish, portuguese, romanian, russian, sanskrit, serbian, serbianc, slovak, sloven
ian, spanish, swedish, turkish, turkmen, ukrainian, uppersorbian, welsh, loaded
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/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/fi4/m/n/10 linear \T1/p
tm/m/n/10 for lin-ear re-gres-sion model, \T1/fi4/m/n/10 linear.penalized
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[]\T1/fi4/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/fi4/m/n/9 pert <- matrix(0, nrow=nrow(data.ras), ncol=ncol(data.ras), di
mnames=dimnames(data.ras))[]
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/m/n/10 best \T1/ptm/m/n/10 only the top \T1/fi4/m/n/10 ensemble.maxnsol
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[]\T1/fi4/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/fi4/m/n/9 pert <- matrix(0, nrow=nrow(data.ras), ncol=ncol(data.ras), di
mnames=dimnames(data.ras))[]
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/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/fi4/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/fi4/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/fi4/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/fi4/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/fi4/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/fi4/m/n/10 linear \T1/p
tm/m/n/10 for lin-ear re-gres-sion model, \T1/fi4/m/n/10 linear.penalized
[13]
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[]\T1/fi4/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/fi4/m/n/9 pert <- matrix(0, nrow=nrow(data.ras), ncol=ncol(data.ras), di
mnames=dimnames(data.ras))[]
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/m/n/10 netinf.predict(net, data, categories, perturbations, subset,
predn, method=c("linear", "linear.penalized", "cpt"))[]
[15]
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/m/n/9 plot(mydata[ ,goi[1]], mypreds, xlab="Observed gene expression
", ylab="Predicted gene expression")[]
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/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/fi4/m/n/9 pert <- matrix(0, nrow=nrow(data.ras), ncol=ncol(data.ras), di
mnames=dimnames(data.ras))[]
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/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/fi4/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/fi4/m/n/9 pert <- matrix(0, nrow=nrow(data.ras), ncol=ncol(data.ras), di
mnames=dimnames(data.ras))[]
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/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/fi4/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/fi4/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/fi4/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/fi4/m/n/10 linear \T1/p
tm/m/n/10 for lin-ear re-gres-sion model, \T1/fi4/m/n/10 linear.penalized
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[]\T1/fi4/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/fi4/m/n/9 pert <- matrix(0, nrow=nrow(data.ras), ncol=ncol(data.ras), di
mnames=dimnames(data.ras))[]
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/m/n/10 best \T1/ptm/m/n/10 only the top \T1/fi4/m/n/10 ensemble.maxnsol
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[]\T1/fi4/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/fi4/m/n/9 pert <- matrix(0, nrow=nrow(data.ras), ncol=ncol(data.ras), di
mnames=dimnames(data.ras))[]
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/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/fi4/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/fi4/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/fi4/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/fi4/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/fi4/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/fi4/m/n/10 linear \T1/p
tm/m/n/10 for lin-ear re-gres-sion model, \T1/fi4/m/n/10 linear.penalized
[13]
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[]\T1/fi4/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/fi4/m/n/9 pert <- matrix(0, nrow=nrow(data.ras), ncol=ncol(data.ras), di
mnames=dimnames(data.ras))[]
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/m/n/10 netinf.predict(net, data, categories, perturbations, subset,
predn, method=c("linear", "linear.penalized", "cpt"))[]
[15]
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/m/n/9 plot(mydata[ ,goi[1]], mypreds, xlab="Observed gene expression
", ylab="Predicted gene expression")[]
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/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/fi4/m/n/9 pert <- matrix(0, nrow=nrow(data.ras), ncol=ncol(data.ras), di
mnames=dimnames(data.ras))[]
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[]\T1/fi4/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/fi4/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/fi4/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/fi4/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/fi4/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/fi4/m/n/9 pert <- matrix(0, nrow=nrow(data.ras), ncol=ncol(data.ras), di
mnames=dimnames(data.ras))[]
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[]\T1/fi4/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/fi4/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/fi4/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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