plot.FLLat: 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 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

A fitted FLLat model. That is, an object of class FLLat, as returned by FLLat.

type

The choice of whether to plot the estimated features \(\hat{B}\) or a heatmap of the estimated weights \(\hat{\Theta}\). Default is "features".

f.mar

The margins for the plot of each estimated feature.

f.xlab

The label for the \(x\)-axis for the plot of each estimated feature.

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 \(\hat{\Theta}\). See hclust.

…

Further graphical parameters, for the plot function when type="features" and for the image function when type="weights".

Details

This function plots the estimated features \(\hat{B}\) or a heatmap of the estimated weights \(\hat{\Theta}\) from a fitted FLLat model. The features are plotted in order of decreasing total magnitude, where the magnitude is given by \(\sum_{l=1}^L\hat{\beta}_{lj}^2\) with \(\hat{\beta}_{lj}\) for \(l=1,\ldots,L\) denoting the \(j\)th estimated feature (column of \(\hat{B}\)). 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 \(\hat{\Theta}\)).

For more details, please see Nowak and others (2011) and the package vignette.

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

Examples

Run this code

## 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")