plot.simvcpsr: Plotting function for 'sim_vcpsr'

In JOPS: Practical Smoothing with P-Splines

Plotting function for sim_vcpsr

Description

Plotting function for varying-coefficient single-index signal regression using tensor product P-splines (using sim_vcpsr with class simvcpsr).

Usage

## 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.

Value

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.

Author(s)

Paul Eilers and Brian Marx

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.

Examples

# 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")

JOPS documentation built on Sept. 8, 2023, 5:42 p.m.