JOPS (version 0.2.0)

predict.pssignal: Predict function for psSignal

Description

Prediction function which returns both linear predictor and inverse link predictions, for an arbitrary matrix of signals (using psSignal with class pssignal).

Usage

# S3 method for pssignal
predict(object, ..., X_pred, type = "mu")

Value

pred

the estimated mean (inverse link function) (default) or the linear predictor prediction with type = "eta", for a matrix of signals in X_pred.

Arguments

object

an object using psSignal.

...

other parameters.

X_pred

a matrix of arbitrary signals with ncol(X) == length(x_index) locations for desired prediction.

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. (1999). Generalized linear regression for sampled signals and curves: A P-spline approach. Technometrics, 41(1): 1-13.

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

Examples

library(JOPS)
# Get the data
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])
oout=23
dX=t(dX[,-oout])
y=y[-oout]
fit1 = psSignal(y, dX, diindex, nseg = 25,lambda = 0.0001)
predict(fit1, X_pred = dX[1:5, ])
predict(fit1, X_pred = dX[1:5, ], type = 'eta')