plot.FLLat {FLLat} | R Documentation |
Plots Results from the Fused Lasso Latent Feature Model
Description
Plots either the estimated features or a heatmap of the
estimated weights from a fitted Fused Lasso Latent Feature (FLLat)
model (i.e., an object of class FLLat
).
Usage
## S3 method for class 'FLLat'
plot(x, type=c("features","weights"), f.mar=c(5,3,4,2), f.xlab="Probe",
w.mar=c(3,5,0,2), samp.names=1:ncol(x$Theta), hc.meth="complete", ...)
Arguments
x |
A fitted FLLat model. That is, an object of class
|
type |
The choice of whether to plot the estimated features
|
f.mar |
The margins for the plot of each estimated feature. |
f.xlab |
The label for the |
w.mar |
The margins for the heatmap of the estimated weights. |
samp.names |
The sample names used to label the columns in the heatmap of the estimated weights. |
hc.meth |
The agglomeration method to be used in the hierarchical
clustering of the columns of |
... |
Further graphical parameters, for the |
Details
This function plots the estimated features or a heatmap
of the estimated weights
from a fitted FLLat model.
The features are plotted in order of decreasing total magnitude, where
the magnitude is given by
with
for
denoting the
th estimated feature (column of
).
Similarly, the rows of the heatmap of the estimated weights are
re-ordered in the same way. The heatmap also includes a dendrogram of
a hierarchical clustering of the samples based on their estimated
weights (columns of
).
For more details, please see Nowak and others (2011) and the package vignette.
Author(s)
Gen Nowak gen.nowak@gmail.com, Trevor Hastie, Jonathan R. Pollack, Robert Tibshirani and Nicholas Johnson.
References
G. Nowak, T. Hastie, J. R. Pollack and R. Tibshirani. A Fused Lasso Latent Feature Model for Analyzing Multi-Sample aCGH Data. Biostatistics, 2011, doi: 10.1093/biostatistics/kxr012
See Also
Examples
## Load simulated aCGH data.
data(simaCGH)
## Run FLLat for J = 5, lam1 = 1 and lam2 = 9.
result <- FLLat(simaCGH,J=5,lam1=1,lam2=9)
## Plot the estimated features.
plot(result)
## Plot a heatmap of the estimated weights.
plot(result,type="weights")