plot.psvcsignal {JOPS}

Plotting function for psVCSignal

Description

Plotting function for varying-coefficent signal regression P-spline smooth coefficients (using psVCSignal with class psvcsignal). Although se 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 class 'psvcsignal'
plot(x, ..., xlab = " ", ylab = " ", Resol = 100)

Arguments

x

the P-spline object, usually from psVCSignal.

...

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 for plotting, default Resol = 100.

Value

Plot

a two panel plot, one of the 2D P-spline signal coefficient surface and another that displays several slices of the smooth coefficient vectors at fixed levels of the varying index.

Author(s)

Paul Eilers and Brian Marx

References

Eilers, P. H. C. and Marx, B. D. (2003). Multivariate calibration with temperature interaction using two-dimensional penalized signal regression. Chemometrics and Intellegent Laboratory Systems, 66, 159–174.

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

Examples

library(fds)
data(nirc)
iindex <- nirc$x
X <- nirc$y
sel <- 50:650 # 1200 <= x & x<= 2400
X <- X[sel, ]
iindex <- iindex[sel]
dX <- diff(X)
diindex <- iindex[-1]
y <- as.vector(labc[1, 1:40]) # percent fat
t_var <- as.vector(labc[4, 1:40]) # percent flour
oout <- 23
dX <- t(dX[, -oout])
y <- y[-oout]
t_var = t_var[-oout]
Pars = rbind(c(min(diindex), max(diindex), 25, 3, 1e-7, 2),
c(min(t_var), max(t_var), 20, 3, 0.0001, 2))
fit1 <- psVCSignal(y, dX, diindex, t_var, Pars = Pars,
family = "gaussian", link = "identity", int = TRUE)
plot(fit1, xlab = "Coefficient Index", ylab = "VC: % Flour")
names(fit1)