plotNetwork | R Documentation |
Plot the network representation of the associations between responses and predictors, based on the estimated gamma matrix and graph of responses from a "BayesSUR" class object.
plotNetwork(
x,
includeResponse = NULL,
excludeResponse = NULL,
includePredictor = NULL,
excludePredictor = NULL,
MatrixGamma = NULL,
PmaxPredictor = 0.5,
PmaxResponse = 0.5,
nodesizePredictor = 2,
nodesizeResponse = 15,
no.isolates = FALSE,
lineup = 1.2,
gray.alpha = 0.6,
edgewith.response = 5,
edgewith.predictor = 2,
edge.weight = FALSE,
label.predictor = NULL,
label.response = NULL,
color.predictor = NULL,
color.response = NULL,
name.predictors = NULL,
name.responses = NULL,
vertex.frame.color = NA,
layoutInCircle = FALSE,
header = "",
...
)
x |
an object of class |
includeResponse |
A vector of the response names which are shown in the network |
excludeResponse |
A vector of the response names which are not shown in the network |
includePredictor |
A vector of the predictor names which are shown in the network |
excludePredictor |
A vector of the predictor names which are not shown in the network |
MatrixGamma |
A matrix or dataframe of the latent indicator variable.
Default is |
PmaxPredictor |
cutpoint for thresholding the estimated latent indicator variable. Default is 0.5 |
PmaxResponse |
cutpoint for thresholding the learning structure matrix of multiple response variables. Default is 0.5 |
nodesizePredictor |
node size of Predictors in the output graph. Default is 15 |
nodesizeResponse |
node size of response variables in the output graph. Default is 25 |
no.isolates |
remove isolated nodes from responses graph and full graph, may get problem if there are also isolated Predictors |
lineup |
A ratio of the heights between responses' area and predictors' |
gray.alpha |
the opacity. The default is 0.6 |
edgewith.response |
the edge width between response nodes |
edgewith.predictor |
the edge width between the predictor and response node |
edge.weight |
draw weighted edges after thresholding at 0.5. The
default value |
label.predictor |
A vector of the names of predictors |
label.response |
A vector of the names of response variables |
color.predictor |
color of the predictor nodes |
color.response |
color of the response nodes |
name.predictors |
A subtitle for the predictors |
name.responses |
A subtitle for the responses |
vertex.frame.color |
color of the frame of the vertices. If you don't want vertices to have a frame, supply NA as the color name |
layoutInCircle |
place vertices on a circle, in the order of their
vertex ids. The default is |
header |
the main title |
... |
other arguments |
data("exampleEQTL", package = "BayesSUR")
hyperpar <- list(a_w = 2, b_w = 5)
set.seed(9173)
fit <- BayesSUR(
Y = exampleEQTL[["blockList"]][[1]],
X = exampleEQTL[["blockList"]][[2]],
data = exampleEQTL[["data"]], outFilePath = tempdir(),
nIter = 10, burnin = 0, nChains = 1, gammaPrior = "hotspot",
hyperpar = hyperpar, tmpFolder = "tmp/"
)
## check output
# draw network representation of the associations between responses and covariates
plotNetwork(fit)
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