plot.PHMM {aphid} | R Documentation |
Plot profile hidden Markov models.
Description
plot.PHMM
provides a visual representation of a profile hidden
Markov model.
Usage
## S3 method for class 'PHMM'
plot(
x,
from = "start",
to = "end",
just = "center",
arrexp = 1,
textexp = 1,
...
)
Arguments
x |
an object of class |
from |
an integer giving the module number to start the plot sequence from. Also accepts the chracter string "start" (module 0; default). |
to |
an integer giving the module number to terminate the plot sequence. Also accepts the chracter string "end" (default). |
just |
a character string giving the justfication of the plot relative to the device. Accepted values are "left", "center" and "right". |
arrexp |
the expansion factor to be applied to the arrows in the plot. |
textexp |
the expansion factor to be applied to the text in the plot. |
... |
additional arguments to be passed to |
Details
"plot.PHMM"
Plots a "PHMM"
object as a directed graph
with sequential modules consisting of squares, diamonds and circles
representing match, insert and delete states, respectively.
Modules are interconnected by directed
lines with line-weights proportional to the transition probabilities between
the states. Since the plotted models are generally much longer than they are
high, it is usually better to output the plot to a PDF file as demonstrated
in the example below.
Value
NULL (invisibly).
Author(s)
Shaun Wilkinson
References
Durbin R, Eddy SR, Krogh A, Mitchison G (1998) Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge University Press, Cambridge, United Kingdom.
See Also
Examples
## Small globin alignment example from Durbin et al (1998) Figure 5.3
data(globins)
## derive a profile hidden Markov model from the alignment
globins.PHMM <- derivePHMM(globins, residues = "AMINO", seqweights = NULL)
## plot the PHMM
plot(globins.PHMM, main = "Profile hidden Markov model for globins")
##
## derive a profile hidden Markov model from the woodmouse dataset in the
## ape package
library(ape)
data(woodmouse)
woodmouse.PHMM <- derivePHMM(woodmouse)
## plot partial model to viewer device
plot(woodmouse.PHMM, from = 0, to = 5)
## plot the entire model to a PDF in the current working directory
tmpf <- tempfile(fileext = ".pdf")
nr <- ceiling((woodmouse.PHMM$size + 2)/10)
pdf(file = tmpf, width = 8.27, height = nr * 2)
par(mfrow = c(nr, 1), mar = c(0, 0, 0, 0) + 0.1)
from <- 0
to <- 10
for(i in 1:nr){
plot(woodmouse.PHMM, from = from, to = to, just = "left")
from <- from + 10
to <- min(to + 10, woodmouse.PHMM$size + 1)
}
dev.off()