1d:T774,# Plotting function for `ps2DSignal` 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. ```r ## S3 method for class 'ps2dsignal' plot(x, ..., xlab = " ", ylab = " ", Resol = 200) ``` ## 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`). ## Returns - **Plot**: a plot of the 2D P-spline signal coefficent surface. ## Examples ```r 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) ``` ## 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. ## Author(s) Paul Eilers and Brian Marx

plot.ps2dsignal function