JOPS (version 0.1.19)

plot.ps2dsignal: Plotting function for ps2DSignal

Description

Plotting function for 2D P-spline signal regression coefficients (using ps2DSignal with class ps2dsignal). Although standard error surface bands can be comuputed they are intentially left out as they are not interpretable, and there is generally little data to steer such a high-dimensional parameterization.

Usage

# S3 method for ps2dsignal
plot(x, ..., xlab = " ", ylab = " ", Resol = 200)

Value

Plot

a plot of the 2D P-spline signal coefficent surface.

Arguments

x

the P-spline object, usually from ps2DSignal.

...

other parameters.

xlab

label for the x-axis, e.g. "my x" (quotes required).

ylab

label for the y-axis, e.g. "my y" (quotes required).

Resol

Resolution of bgrid (default Resol = 200).

Author

Paul Eilers and Brian Marx

References

Marx, B.D. and Eilers, P.H.C. (2005). Multidimensional penalized signal regression, Technometrics, 47: 13-22.

Eilers, P.H.C. and Marx, B.D. (2021). Practical Smoothing, The Joys of P-splines. Cambridge University Press.

Examples

library(fields)
library(JOPS)

# Get the data
x0 <- Sugar$X
x0 <- x0 - apply(x0, 1, mean) # center Signal
y <- as.vector(Sugar$y[, 3]) # Response is Ash

# Inputs for two-dimensional signal regression
nseg <- c(7, 37)
pord <- c(3, 3)
min_ <- c(230, 275)
max_ <- c(340, 560)
M1_index <- rev(c(340, 325, 305, 290, 255, 240, 230))
M2_index <- seq(from = 275, to = 560, by = .5)
p1 <- length(M1_index)
p2 <- length(M2_index)

# Fit optimal model based on LOOCV
opt_lam <- c(8858.6679, 428.1332) # Found via svcm
Pars_opt <- rbind(
 c(min_[1], max_[1], nseg[1], 3, opt_lam[1], pord[1]),
 c(min_[2], max_[2], nseg[2], 3, opt_lam[2], pord[2]))

fit <- ps2DSignal(y, x0, p1, p2, "unfolded", M1_index, M2_index,
       Pars_opt, int = FALSE, ridge_adj = 1e-4 )

# Plotting coefficient image
plot(fit)