JOPS (version 0.1.19)

predict.ps2dsignal: Predict function for ps2DSignal

Description

Prediction function which returns both linear predictor and inverse link predictions for arbitrary 2D signals (using ps2DSignal with class ps2dsignal).

Usage

# S3 method for ps2dsignal
predict(object, ..., M_pred, M_type = "unfolded", type = "mu")

Value

pred

the estimated mean (inverse link function) or the linear predictor prediction with type = "eta", for arbitary 2D signals in M_pred.

Arguments

object

an object using ps2DSignal.

...

other parameters.

M_pred

a matrix of q arbitrary "stacked" or "unfolded" signal matrices of dimension (q by p1) by p2 or q by (p1 by p2, respectively, for desired prediction (default "unfolded").

M_type

"stacked" or "unfolded" (default).

type

the mean value type = "mu" (default) or linear predictor type = "eta".

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 = TRUE, ridge_adj = 0.0001,
  M_pred = x0 )


predict(fit, M_pred= x0, type = "mu", M_type = "unfolded")