# plot.pssignal: Plotting function for 'psSignal' In JOPS: Practical Smoothing with P-Splines

 plot.pssignal R Documentation

## Plotting function for `psSignal`

### Description

Plotting function for signal regression P-spline smooth coefficients (using `psSignal` with `class pssignal`), with or without standard error bands.

### Usage

``````## S3 method for class 'pssignal'
plot(x, ..., se = 2, xlab = "", ylab = "", col = "black", lty = 1)
``````

### Arguments

 `x` the P-spline x, usually from `psSignal`. `...` other parameters. `se` a scalar, e.g. `se = 2` to produce twice se bands, set `se` > 0 (or set `se = 0` to supress). `xlab` label for the x-axis, e.g. "my x" (quotes required). `ylab` label for the y-axis, e.g. "my y" (quotes required). `col` color. `lty` line type for plotting e.g. `lty = 2`.

### Value

 `Plot` a plot of the smooth P-spline signal coefficent vector, with or without standard error bands.

### Author(s)

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]
fit2 = psSignal(y, dX, diindex, nseg = 25,lambda = 0.0001)
plot(fit2, se = 2, xlab = 'Coefficient Index', ylab= "ps Smooth Coeff")
title(main='25 B-spline segments with tuning=0.0001')
names(fit2)

``````

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