* 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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[]\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/9 ## load gene expression data for colon cancer data, list of ge
nes related to RAS signaling pathway and the corresponding priors[]
[5]
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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(data=mydata, categories=3, priors=mypriors, prio
rs.weight=0.5, method="regrnet", seed=54321)[]
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[]\T1/fi4/m/n/10 net.stab(data, categories, perturbations, priors, priors.coun
t = TRUE, priors.weight = 0.5, maxparents = 3, subset, method = c("bayesnet", "
regrnet"))[]
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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 res <- netinf(data=mydata, categories=3, priors=mypriors, prio
rs.weight=0.5, method="regrnet", seed=54321)[]
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[]\T1/fi4/m/n/10 netinf(data, categories, perturbations, priors, predn, priors
.count = TRUE, priors.weight = 0.5, maxparents = 3, maxparents.push = FALSE, su
bset, method = c("regrnet", "bayesnet"), ensemble = FALSE, regrmodel = c("linea
r", "linear.penalized"), ensemble.model = c("full","best"), ensemble.maxnsol =
3, causal=TRUE, seed=54321, bayesnet.maxcomplexity=0, bayesnet.maxiter=100)[]
[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
[11]
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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 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)[]
[12]
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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 mynet <- netinf(data=mydata, perturbations=mypert, priors=mypr
iors, priors.count=TRUE, priors.weight=0.5, maxparents=3, method="bayesnet", se
ed=54321)[]
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[]\T1/fi4/m/n/9 xnet <- network(x=mytopo, matrix.type="adjacency", directed=TR
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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, maxparents.push = FALSE,
subset, method = c("regrnet", "bayesnet"), ensemble = FALSE, ensemble.maxnsol=
3, regrmodel = c("linear", "linear.penalized"), nfold = 10, causal = TRUE, seed
, bayesnet.maxcomplexity = 0, bayesnet.maxiter = 100)[]
[13] [14]
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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 res <- netinf.cv(data=mydata, categories=3, perturbations=mype
rt, priors=mypriors, priors.count=TRUE, priors.weight=0.5, method="bayesnet", n
fold=3, seed=54321)[]
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[]\T1/fi4/m/n/10 netinf.predict(net, data, categories, perturbations, subset,
predn, ensemble=FALSE)[]
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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 ## 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]][]
[17]
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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/10 pred.score(data, pred, categories, method = c("r2", "nrmse",
"mcc") ,ensemble=FALSE)[]
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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/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 res <- netinf(data=mydata, categories=3, priors=mypriors, prio
rs.weight=0.5, method="regrnet", seed=54321)[]
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[]\T1/fi4/m/n/10 net.stab(data, categories, perturbations, priors, priors.coun
t = TRUE, priors.weight = 0.5, maxparents = 3, subset, method = c("bayesnet", "
regrnet"))[]
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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 res <- netinf(data=mydata, categories=3, priors=mypriors, prio
rs.weight=0.5, method="regrnet", seed=54321)[]
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[]\T1/fi4/m/n/10 netinf(data, categories, perturbations, priors, predn, priors
.count = TRUE, priors.weight = 0.5, maxparents = 3, maxparents.push = FALSE, su
bset, method = c("regrnet", "bayesnet"), ensemble = FALSE, regrmodel = c("linea
r", "linear.penalized"), ensemble.model = c("full","best"), ensemble.maxnsol =
3, causal=TRUE, seed=54321, bayesnet.maxcomplexity=0, bayesnet.maxiter=100)[]
[11]
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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[]
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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]][]
[12]
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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 mynet <- netinf(data=mydata, perturbations=mypert, priors=mypr
iors, priors.count=TRUE, priors.weight=0.5, maxparents=3, method="bayesnet", se
ed=54321)[]
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[]\T1/fi4/m/n/9 xnet <- network(x=mytopo, matrix.type="adjacency", directed=TR
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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, maxparents.push = FALSE,
subset, method = c("regrnet", "bayesnet"), ensemble = FALSE, ensemble.maxnsol=
3, regrmodel = c("linear", "linear.penalized"), nfold = 10, causal = TRUE, seed
, bayesnet.maxcomplexity = 0, bayesnet.maxiter = 100)[]
[14]
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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]][]
[15]
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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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rt, priors=mypriors, priors.count=TRUE, priors.weight=0.5, method="bayesnet", n
fold=3, seed=54321)[]
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[]\T1/fi4/m/n/10 netinf.predict(net, data, categories, perturbations, subset,
predn, ensemble=FALSE)[]
[16]
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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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ld.change"])), decreasing=TRUE)[1:genen]][]
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[]\T1/fi4/m/n/10 pred.score(data, pred, categories, method = c("r2", "nrmse",
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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/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 res <- netinf(data=mydata, categories=3, priors=mypriors, prio
rs.weight=0.5, method="regrnet", seed=54321)[]
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[]\T1/fi4/m/n/10 net.stab(data, categories, perturbations, priors, priors.coun
t = TRUE, priors.weight = 0.5, maxparents = 3, subset, method = c("bayesnet", "
regrnet"))[]
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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 res <- netinf(data=mydata, categories=3, priors=mypriors, prio
rs.weight=0.5, method="regrnet", seed=54321)[]
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[]\T1/fi4/m/n/10 netinf(data, categories, perturbations, priors, predn, priors
.count = TRUE, priors.weight = 0.5, maxparents = 3, maxparents.push = FALSE, su
bset, method = c("regrnet", "bayesnet"), ensemble = FALSE, regrmodel = c("linea
r", "linear.penalized"), ensemble.model = c("full","best"), ensemble.maxnsol =
3, causal=TRUE, seed=54321, bayesnet.maxcomplexity=0, bayesnet.maxiter=100)[]
[11]
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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[]
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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]][]
[12]
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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 mynet <- netinf(data=mydata, perturbations=mypert, priors=mypr
iors, priors.count=TRUE, priors.weight=0.5, maxparents=3, method="bayesnet", se
ed=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/10 netinf.cv(data, categories, perturbations, priors, predn, pri
ors.count = TRUE, priors.weight = 0.5, maxparents = 3, maxparents.push = FALSE,
subset, method = c("regrnet", "bayesnet"), ensemble = FALSE, ensemble.maxnsol=
3, regrmodel = c("linear", "linear.penalized"), nfold = 10, causal = TRUE, seed
, bayesnet.maxcomplexity = 0, bayesnet.maxiter = 100)[]
[14]
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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]][]
[15]
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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 res <- netinf.cv(data=mydata, categories=3, perturbations=mype
rt, priors=mypriors, priors.count=TRUE, priors.weight=0.5, method="bayesnet", n
fold=3, seed=54321)[]
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[]\T1/fi4/m/n/10 netinf.predict(net, data, categories, perturbations, subset,
predn, ensemble=FALSE)[]
[16]
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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 ## 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/10 pred.score(data, pred, categories, method = c("r2", "nrmse",
"mcc") ,ensemble=FALSE)[]
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Saving output to '/private/tmp/RtmpxYe8My/Rbuild4a85216b9c53/predictionet/build/predictionet.pdf' ...
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