## S3 method for class 'RJaCGH':
plot(x, k = NULL, model.averaging = TRUE, cex=1,
smoother=FALSE, \ldots)
## S3 method for class 'RJaCGH.Chrom':
plot(x, Chrom="Genome", model.averaging=TRUE,
cex=1, k=NULL, smoother=FALSE, \ldots)
## S3 method for class 'RJaCGH.Genome':
plot(x, k=NULL, model.averaging=TRUE, cex=1,
smoother=FALSE, \ldots)
## S3 method for class 'RJaCGH.array':
plot(x, show="frequency", weights=NULL, cex=1,
smoother=FALSE, \ldots)
model.averaging
is
performed. If FALSE, a call to link{states}
is made to get
hidden state sequence.plot.Q.NH
, and the last one shows the
original observations colored by thir hidden state and the probability
of being in that hidden state.On every plot, the 'Normal' state is coloured black. The 'Gain' states are red and the 'Loss' ones green.
If the object is 'RJaCGH.Chrom', only the last one of the plots are shown. For the complete panel, call the method on every chromosome.
If the object is 'RJaCGH.array' and show
is 'average', the last
one of the plots is drawn, but the hidden state sequence and its
probability is computed averaging on all the arrays with weights
according to weights
vector. If show
is 'frequency',
again the last plot is drawn, but the percentage of arrays in
which every gene is Gain/Lost is shown, weighted by the
weights
vector.
If smoother
is TRUE, the smoothed mean is drawn. See
smoothMeans
(except in plot.RJaCGH.array
when
method is not 'averaging').
RJaCGH
,
smoothMeans
,
summary.RJaCGH
, model.averaging
,
states
, trace.plot
,
gelman.rubin.plot
, collapseChain
y <- c(rnorm(100, 0, 1), rnorm(10, -3, 1), rnorm(20, 3, 1), rnorm(100,
0, 1))Pos <- sample(1:10, 230, replace=TRUE)
Pos <- cumsum(Pos)
Chrom <- rep(1:23, rep(10, 23))
jp <- list(sigma.tau.mu=rep(0.5, 4), sigma.tau.sigma.2=rep(0.3, 4),
sigma.tau.beta=rep(0.7, 4), tau.split.mu=0.5)
fit.Chrom <- RJaCGH(y=y, Pos=Pos, Chrom=Chrom, model="Chrom",
burnin=100, TOT=1000, jump.parameters=jp, k.max=4)
fit.Genom <- RJaCGH(y=y, Pos=Pos, Chrom=Chrom, model="Genome", burnin=100, TOT=1000, jump.parameters=jp, k.max=4)
fit.none <- RJaCGH(y=y, Pos=Pos, Chrom=NULL, model="None",
burnin=100, TOT=1000, jump.parameters=jp, k.max=4)
plot(fit.Chrom)
plot(fit.Chrom[[1]])
plot(fit.Genom)
plot(fit.none)
y2 <- c(rnorm(100, 0, 1), rnorm(10, -3, 1), rnorm(20, 3, 1),
rnorm(100, 0, 1))
ya <- cbind(y, y2)
fit.Chrom.array <- RJaCGH(y=ya, Pos=Pos, Chrom=Chrom, model="Chrom",
burnin=100, TOT=1000, jump.parameters=jp, k.max=4)
fit.Genom.array <- RJaCGH(y=ya, Pos=Pos, Chrom=Chrom, model="Genome", burnin=100, TOT=1000, jump.parameters=jp, k.max=4)
fit.none.array <- RJaCGH(y=ya, Pos=Pos, Chrom=NULL, model="None",
burnin=100, TOT=1000, jump.parameters=jp, k.max=4)
plot(fit.Chrom.array)
plot(fit.Genom.array)
plot(fit.none.array)
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