flam (version 3.2)

flam-package: Fit the Fused Lasso Additive Model

Description

This package is called flam for "fused lasso additive model", which is proposed in Petersen, A., Witten, D., and Simon, N. (2014). Fused Lasso Additive Model. arXiv preprint arXiv:1409.5391. The fused lasso additive model provides an approach to fit an additive model in which each component is estimated to be piecewise constant with a small number of adaptively-chosen knots.

The main functions are: (1) flam and (2) flamCV. The first function flam fits the fused lasso additive model for a range of tuning parameters and provides the fits for all of these tuning parameters. The second function flamCV considers a range of tuning parameters and provides the fits, but also returns the optimal tuning parameters, as chosen using K-fold cross-validation.

Details

Package:	flam
Type:	Package
Version:	3.0
Date:	2015-07-26
License:	GPL (>= 2)

The package includes the following functions: flam, flamCV, plot.flam, plot.flamCV, plot.flamSparsity, predict.flam, summary.flam, summary.flamCV, flamDOF, and sim.data.

References

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

Examples

# NOT RUN {
#general example illustrating all functions
#see specific function help pages for details of using each function

#generate data
set.seed(1)
data <- sim.data(n = 50, scenario = 1, zerof = 10, noise = 1)

#fit model for a range of tuning parameters, lambda and alpha
#lambda sequence is chosen automatically if not specified
flam.out <- flam(x = data$x, y = data$y, alpha.seq = c(0.8, 0.9, 1))
#or fit model and select lambda using 2-fold cross-validation
#note: use larger 'n.fold' (e.g., 10) in practice
flamCV.out <- flamCV(x = data$x, y = data$y, alpha = 1, n.fold = 2)

#summarize all of the fits (in particular, the percent sparsity achieved)
#note: percent sparsity is the percentage of features estimated
#to have no relationship with outcome
summary(flam.out)
#the percent sparsity over the range of tuning parameters can also 
#be displayed in a plot
plot(summary(flam.out))
#or just summarize a single fit 
#we'll examine the fit with an index of 25. that is, lambda and alpha of
flam.out$all.lambda[25]; flam.out$all.alpha[25]
summary(flam.out, index = 25)
#lastly, we can summarize the fit chosen using cross-validation
summary(flamCV.out$flam.out, index = flamCV.out$index.cv)
#the lambda chosen by cross-validation is also available using
flamCV.out$lambda.cv

#plot the estimated relationships between predictors and outcome
#do this for a specific fit
plot(flam.out, index = 25)
#or for the fit chosen using cross-validation
plot(flamCV.out$flam.out, index = flamCV.out$index.cv)
#by default, up to 10 non-sparse features with the largest L2 norms will 
#be plotted, see '?plot.flam' for other optional function arguments

#this data is simulated, so with a little more work, we can compare the 
#true generating functions to the estimated function fits
#we do this for the truly non-zero functions (i.e., the first four predictors)
#generate data from same model but larger n, just used to plot true functions
temp.data <- sim.data(n = 500, scenario = 1, zerof = 10, noise = 1)
col.vec = c("dodgerblue1","orange","seagreen1","hotpink")
theta.hat = flamCV.out$flam.out$theta.hat.list[[flamCV.out$index.cv]]
par(mfrow=c(2,2))
for (i in 1:4) {
	rgb.num = col2rgb(col.vec[i])
	col=rgb(rgb.num[1], rgb.num[2], rgb.num[3], 100, max=256)
	plot(1,type="n",xlim=c(-2.5,2.5),ylim=c(-2,2),xlab=paste("x",i,sep=""),
		ylab=paste("f",i,"(x",i,")",sep=""),main="")
	points(sort(temp.data$x[,i]), temp.data$theta[order(temp.data$x[,i]),i],type="l",lwd=3)
	points(sort(data$x[,i]), theta.hat[order(data$x[,i]),i],col=col,type="l",lwd=3)
}

#we can make predictions for a covariate matrix with new observations
#choose the alpha and lambda of interest
alpha <- flamCV.out$alpha; lambda <- flamCV.out$lambda.cv
#new.x with 20 observations and the same number of features as flam.out$x
new.data <- sim.data(n = 20, scenario = 1, zerof = 10, noise = 1)
new.x <- new.data$x
#these will give the same predictions:
yhat1 <- predict(flam.out, new.x = new.x, lambda = lambda, alpha = alpha)
yhat2 <- predict(flamCV.out$flam.out, new.x = new.x, lambda = lambda, alpha = alpha)

#we can summarize the cross-validation function call
summary(flamCV.out)
#and also plot the cross-validation error
plot(flamCV.out)
#or calculate degrees of freedom for the model chosen using cross-validation
flamDOF(object = flamCV.out$flam.out, index = flamCV.out$index.cv)
#or for any fit of a 'flam' object
flamDOF(object = flam.out, index = 25)
# }