plot.flam: Plots Function Estimates for Fit of Class "flam"
In flam: Fits Piecewise Constant Models with Data-Adaptive Knots

Description Usage Arguments Details Author(s) References See Also Examples

View source: R/flam_fcns.R

This function plots the estimated functions from a model estimated using flam. The user specifies the model of interest (i.e., the tuning parameters) and a plot is made for the estimated association between all non-sparse (or a chosen subset of) features and the outcome.

## S3 method for class 'flam'
plot(x, index, n.plot = 10, predictor.indicators = NULL,
predictor.labels = NULL, outcome.label = "outcome", ticks = F, 
col = "dodgerblue", n.panel.width = NULL, n.panel.height = NULL, ...)

`x`	an object of class "flam".
`index`	the index for the model of interest to be plotted. Note that `index` of i corresponds to the model with tuning parameters `x$all.alpha[i]` and `x$all.lambda[i]`.
`n.plot`	the number of predictors to be plotted (default of 10). Note that only non-sparse predictors are plotted, however, this argument is ignored if `predictor.indicators` is specified.
`predictor.indicators`	a vector indicating which predictor function estimates to plot. The vector should contain the column numbers of `x$x` whose function estimates are to be plotted. By default, the `n.plot` predictors with the largest L2 norm are plotted.
`predictor.labels`	a vector containing the predictor labels of the same length as `predictor.indicators` if specified, otherwise of length `ncol(x$x)`. By default, "Predictor 1", "Predictor 2", ... are used.
`outcome.label`	the name of the outcome used in the y-axis label. By default, the label is "outcome".
`ticks`	a logical (`TRUE` or `FALSE`) for whether tick marks indicating the distribution of the predictor should be included at the bottom of each plot.
`col`	a vector of colors used to plot the function estimates. If `col` has length 1, then the same color is used for all predictors. Otherwise, `col` should indicate the color for each predictor and have the same length as `predictor.indicators` if specified, otherwise the number of columns of `x$x`.
`n.panel.width`	the plots will be plotted with `par(mfrow=c(n.panel.height,n.panel.width))`. If specified, `n.panel.height` must also be specified. By default, an appropriate grid of plots is used.
`n.panel.height`	the plots will be plotted with `par(mfrow=c(n.panel.height,n.panel.width))`. If specified, `n.panel.width` must also be specified. By default, an appropriate grid of plots is used.
`...`	additional arguments to be passed. These are ignored in this function.

The estimated function fits are drawn by connecting the predictions for all of the observations in x$x. This may result in fits that appear not to be piecewise consant if only a single observation is observed for a range of x-values.

Ashley Petersen

Petersen, A., Witten, D., and Simon, N. (2014). Fused Lasso Additive Model. arXiv preprint arXiv:1409.5391.

flam

#See ?'flam-package' for a full example of how to use this package

#generate data
set.seed(1)
data <- sim.data(n = 50, scenario = 1, zerof = 10, noise = 1)
#fit model for a range of tuning parameters
flam.out <- flam(x = data$x, y = data$y, alpha.seq = c(0.8, 0.9, 1))

#we plot the predictor fits for a specific index, e.g. 25
#that is, lambda and alpha of
flam.out$all.lambda[25]; flam.out$all.alpha[25]
plot(flam.out, index = 25)
#the fit only has 5 non-sparse features

#by default, up to 10 non-sparse features with the largest L2 norms are 
#plotted, but we can plot a different number of features if desired
plot(flam.out, index = 40, n.plot = 12)
#or we can plot specific predictors of interest
plot(flam.out, index = 40, predictor.indicators = c(1:4, 6, 8, 11, 12))