# Plotting function for `sim_vcpsr` Plotting function for varying-coefficient single-index signal regression using tensor product P-splines (using `sim_vcpsr` with `class simvcpsr`). ```r ## S3 method for class 'simvcpsr' plot(x, ..., xlab = " ", ylab = " ", Resol = 100) ``` ## Arguments - `x`: the P-spline object, usually from `sim_vcpsr`. - `...`: 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`. ## Returns - **Plot**: a plot of the estimated 2D P-spline signal coefficient surface along with the companion plot of the estimated 2D P-spline varying link function surface. Slices of these plots, at fixed levels of the indexing covariate, are also provided. ## Examples ```r # Load libraries library(fields) # Needed for plotting # Get the data Dat <- Mixture # Dimensions: observations, temperature index, signal m <- 34 p1 <- 401 p2 <- 12 # Stacking mixture data, each mixture has 12 signals stacked # The first differenced spectra are also computed. mixture_data <- matrix(0, nrow = p2 * m, ncol = p1) for (ii in 1:m) { mixture_data[((ii - 1) * p2 + 1):(ii * p2), 1:p1] <- t(as.matrix(Dat$xspectra[ii, , ])) d_mixture_data <- t(diff(t(mixture_data))) } # Response (typo fixed) and index for signal y_mixture <- Dat$fractions y_mixture[17, 3] <- 0.1501 index_mixture <- Dat$wl # Select response and replicated for the 12 temps # Column 1: water; 2: ethanediol; 3: amino-1-propanol y <- as.vector(y_mixture[, 2]) y <- rep(y, each = p2) bdegs = c(3, 3, 3, 3) pords <- c(2, 2, 2, 2) nsegs <- c(12, 5, 5, 5) # Set to c(27, 7, 7 ,7) for given lambdas mins <- c(700, 30) maxs <- c(1100, 70) lambdas <- c(1e-11, 100, 0.5, 1) # based on svcm search x_index <- seq(from = 701, to = 1100, by = 1) # for dX t_var_sub <- c(30, 35, 37.5, 40, 45, 47.5, 50, 55, 60, 62.5, 65, 70) t_var <- rep(t_var_sub, m) max_iter <- 2 # Set higher in practice, e.g. 100 int <- TRUE # Defining x as first differenced spectra, number of channels. x <- d_mixture_data # Single-index VC model using optimal tuning fit <- sim_vcpsr(y, x, t_var, x_index, nsegs, bdegs, lambdas, pords, max_iter = max_iter, mins = mins, maxs = maxs) plot(fit, xlab = "Wavelength (nm)", ylab = "Temp C") ``` ## References Marx, B. D. (2015). Varying-coefficient single-index signal regression. **Chemometrics and Intellegent Laboratory Systems**, 143, 111–121. 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.simvcpsr function