Robust estimators for Functional Partial Linear Models

Matias Salibian-Barrera 2020-06-29

This repository contains R code to compute the robust MM-estimators for Functional Partial Linear models in Boente G, Salibian-Barrera M, Vena P. Robust estimation for semi-functional linear regression models. To appear in Computational Statistics and Data Science. Original code by Pablo Vena.

Installing the RobustFPLM package for R

The package can be installed directly from this repository using the following command in R:

devtools::install_github("msalibian/RobustFPLM", ref = "master")

Tecator example

Because the regression model of the Tecator data includes an interaction between the non-parametric term g and a scalar covariate w (specifically, the model is y = <X, beta> + w * g(z) + e, where X is the functional predictor, w and z are scalar variables, and g is a smooth unknown function), setting the appropriate design matrices (including splitting the data into a training and a testing set, and computing the 2nd derivatives of the absorbance spectra) requires special care. Thus, we prepared a suite of specific functions and scripts to reproduce our analysis in the paper. See here and here.

A detailed example

Here we will fit a FPLM to the Tecator data set. It is also available from the fda.usc package.

The main function in our Robust FPLM suite of functions is FPLM-Bsplines(). Its arguments include:

• y: the vector of scalar responses;
• x: a matrix of the functional covariates, where each row contains the functions evaluated on a (common) grid;
• t: the grid over which the functional covariates were evaluated;
• u: the values of the explanatory variable that enters the model non-parametrically;
• range_freq: a vector of B-spline basis sizes to try for the functional regression coefficient;
• range_spl: a vector of B-spline basis sizes to try for the non-parametric component;
• norder: the order of the B-splines;
• fLoss: the loss function to be minimized; and
• trace: a logical argument indicating whether partial results are printed.

We load our functions from the file FPLM-Bsplines-functions.R. Note that to compute the robust estimators for the FPLM model we need the robustbase package.

library(RobustFPLM)

Next, we load the tecator data from the fda.usc package and extract the response vector of fat content, the matrix of functional explanatory functional variables (the second derivatives of the absorption spectra), the grid (t) on which the x’s were evaluated, and the vector (u) of protein values (the covariate that is modelled non-parametrically). The model is fat = < beta, absorp2> + eta(protein) + e.

library(fda.usc)
data(tecator)
absorp2 <- fdata.deriv(tecator$absorp.fdata, nderiv = 2)
y <- tecator$y$Fat[1:155]
u <- tecator$y$Protein[1:155]
t <- absorp2$argvals
x <- absorp2$data[1:155, ]

We consider B-splines of order 4, with bases sizes between 4 and 13, and use the Robust BIC criterion to determine the optimal B-splines dimensions for each smooth component

norder <- 4
range_beta <- 4:13
range_eta <- 4:13
criterion <- "bic1"

Now we fit the robust MM estimator:

set.seed(124)  # for lmrob()
a <- FPLMBsplines(y = y, x = x, u = u, t = t, range_freq = range_beta, range_spl = range_eta, 
    norder = norder, fLoss = "lmrob", trace = FALSE)

To fit the M and LS estimators we simply change the argument fLoss:

d <- FPLMBsplines(y = y, x = x, u = u, t = t, range_freq = range_beta, range_spl = range_eta, 
    norder = norder, fLoss = "huang", trace = FALSE)

b <- FPLMBsplines(y = y, x = x, u = u, t = t, range_freq = range_beta, range_spl = range_eta, 
    norder = norder, fLoss = "ls", trace = FALSE)

The following plots compare the different fits. First we look at the non-parametric component:

u_order <- order(u)
ma <- max(rra <- c(a$fit$eta_est, b$fit$eta_est, d$fit$eta_est))
mi <- min(rra)
plot(u[u_order], a$fit$eta_est[u_order], type = "l", xlab = "Protein", pch = 1, 
    lwd = 6, col = "black", ylab = expression(hat(eta)), lty = 1, ylim = c(mi, 
        ma))
lines(u[u_order], d$fit$eta_est[u_order], lwd = 6, col = "tomato3")
lines(u[u_order], b$fit$eta_est[u_order], lwd = 6, col = "skyblue3")
legend("bottomright", lty = 1, lwd = 3, col = c("black", "tomato3", "skyblue3"), 
    legend = c("MM", "M", "LS"))

And finally we look at the estimates for the functional regression coefficient:

ma <- max(rrb <- c(a$fit$slope_fun, b$fit$slope_fun, d$fit$slope_fun))
mi <- min(rrb)
plot(t, a$fit$slope_fun, col = "black", lwd = 6, type = "l", xlab = "Wavelength", 
    ylab = expression(hat(beta)), lty = 1, ylim = c(mi, ma))
lines(t, d$fit$slope_fun, col = "tomato3", lwd = 6, lty = 1)
lines(t, b$fit$slope_fun, col = "skyblue3", lwd = 6, lty = 1)
legend("bottom", lty = 1, lwd = 3, col = c("black", "tomato3", "skyblue3"), 
    legend = c("MM", "M", "LS"))

We can now compare the fit obtained with the robust estimator to that obtained with the cleaned'' data, that is: we remove potential outliers and fit the LS estimator to thisclean’’ data. To identify possibly atypical observations, we look at the residuals from the robust fit, and flag those with large residuals (in absolute value):

res <- y - a$fit$fitted
names(res) <- 1:length(res)
outl <- as.numeric(names(boxplot(res, col = "gray70")$out))

We remove these suspect observations and re-fit the M- and LS-estimators on the remaining ``clean’’ data:

y2 <- y[-outl]
u2 <- u[-outl]
x2 <- x[-outl, ]
d2 <- FPLMBsplines(y = y2, x = x2, u = u2, t = t, range_freq = range_beta, range_spl = range_eta, 
    norder = norder, fLoss = "huang", trace = FALSE)

b2 <- FPLMBsplines(y = y2, x = x2, u = u2, t = t, range_freq = range_beta, range_spl = range_eta, 
    norder = norder, fLoss = "ls", trace = FALSE)

We now plot the robust estimator computed earlier on the whole data set, and the M- and LS-estimators re-computed on the ``cleaned’’ data:

u2_order <- order(u2)
ma <- max(rra <- c(a$fit$eta_est, b$fit$eta_est, d$fit$eta_est))
mi <- min(rra)
plot(u[u_order], a$fit$eta_est[u_order], type = "l", xlab = "Protein", pch = 1, 
    lwd = 6, col = "black", ylab = expression(hat(eta)), lty = 1, ylim = c(mi, 
        ma))
lines(u2[u2_order], d2$fit$eta_est[u2_order], lwd = 6, col = "tomato3")
lines(u2[u2_order], b2$fit$eta_est[u2_order], lwd = 3, col = "skyblue3")
legend("bottomright", lty = 1, lwd = 3, col = c("black", "tomato3", "skyblue3"), 
    legend = c("MM", "M2", "LS2"))

and

ma <- max(rrb <- c(a$fit$slope_fun, b$fit$slope_fun, d$fit$slope_fun))
mi <- min(rrb)
plot(t, a$fit$slope_fun, col = "black", lwd = 6, type = "l", xlab = "Wavelength", 
    ylab = expression(hat(beta)), lty = 1, ylim = c(mi, ma))
lines(t, d2$fit$slope_fun, col = "tomato3", lwd = 6, lty = 1)
lines(t, b2$fit$slope_fun, col = "skyblue3", lwd = 3, lty = 1)
legend("bottom", lty = 1, lwd = 3, col = c("black", "tomato3", "skyblue3"), 
    legend = c("MM", "M2", "LS2"))

Note that all estimators are now essentially identical to each other. In other words, we see that the robust estimator results in a fit that is intuitively equivalent to using LS on a ``cleaned’’ data set. However, the robust procedure accomplishes this automatically, and does not involve diagnostic plots or manually removing observations from the data set.

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