Generate a single plot that summarizes the results of fitting the Bayesian variable selection model to the data. When the variables are genetic markers, the groups are chromosomes, and the posterior probabilities are plotted on the vertical axis (typically on the logarithmic scale), the figure resembles a "Manhattan plot" typically used to summarize the results of a genomewide association study or quantitative trait locus (QTL) mapping study.
1 2 3 4 5 6 7  ## S3 method for class 'varbvs'
plot(x, score, groups, vars = NULL, var.labels,
draw.threshold = NA, gap = 0,col = "midnightblue", pch = 20,
xlab = "", ylab = "",
abline.args = list(lty = "dotted",col = "orangered"),
vars.xyplot.args = list(pch = 20,col = "magenta"),
vars.ltext.args = list(col = "black",pos = 4,cex = 0.5), ...)

x 
Output of function 
score 
Value to plot on vertical axis. Must be a numeric vector
with one entry for each variable. If missing, the posterior inclusion
probability for each variable is plotted in the vertical axis, in
which this probability is averaged over hyperparameter settings,
treating 
groups 
Group the variables in the plot according to this argument. This must be a vector with one entry for each variable. If missing, all variables are treated as a single group. This is useful for grouping the genetic markers by chromosome in a genomewide association study. 
vars 
Indices of variables to highlight and label. By default,

var.labels 
Labels to accompany the highlighted variables
only. If missing, labels are retrieved from 
draw.threshold 
Plot a horizontal line at this location on the vertical axis. 
gap 
Amount of space to leave between each group of variables in the plot. 
col 
Argument passed to 
pch 
Argument passed to 
xlab 
Argument passed to 
ylab 
Argument passed to 
abline.args 
Additional arguments passed to 
vars.xyplot.args 
Additional arguments passed to 
vars.ltext.args 
Additional arguments passed to 
... 
Additional arguments passed to 
Note that plot.varbvs
uses function xyplot
from the
lattice
package, and as.layer
from the
latticeExtra
package.
An object of class "trellis"
generated by functions
xyplot
and as.layer
.
Peter Carbonetto peter.carbonetto@gmail.com
P. Carbonetto and M. Stephens (2012). Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies. Bayesian Analysis 7, 73–108.
varbvs
, summary.varbvs
, varbvsindep
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
All documentation is copyright its authors; we didn't write any of that.