plot.pspfit: Plotting function for 'psNormal', 'psPoisson', 'psBinomial'

In JOPS: Practical Smoothing with P-Splines

Plotting function for psNormal, psPoisson, psBinomial

Description

Plotting function for P-spline smooth with normal, Poisson, or binomial responses (class pspfit), with or without standard error bands.

Usage

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

Arguments

x	the P-spline object, usually from psNormal, psPoisson, psBinomial.
...	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.
ylab	label for the y-axis.
col	color for points.
pch	point character.

Value

Plot	a plot of the mean (inverse link) smoothed normal, Poisson, or binomial responses, with or without se bands.

Author(s)

Paul Eilers and Brian Marx

References

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

Eilers, P.H.C., Marx, B.D., and Durban, M. (2015). Twenty years of P-splines, SORT, 39(2): 149-186.

Examples

library(JOPS)
#Extract data
library(MASS)
# Get the data
data(mcycle)
x = mcycle$times
y = mcycle$accel
fit1 = psNormal(x, y, nseg = 20, bdeg = 3, pord = 2, lambda = .8)
plot(fit1, se = 2, xlab = "time (ms)", ylab = "accel")

library(JOPS)
library(boot)
# Extract the data
Count = hist(boot::coal$date, breaks=c(1851:1963), plot = FALSE)$counts
Year = c(1851:1962)
xl = min(Year)
xr = max(Year)

# Poisson smoothing
nseg = 20
bdeg = 3
fit1=psPoisson(Year, Count, xl, xr, nseg, bdeg, pord = 2,
lambda = 1)
names(fit1)
plot(fit1, xlab = "Year", ylab = "Count", se = 2)

library(JOPS)
#Extract data
library(rpart)
Kyphosis = kyphosis$Kyphosis
Age  =kyphosis$Age
y = 1 * (Kyphosis == "present")  # make y 0/1
# Binomial smoothing
fit1 = psBinomial(Age, y, xl = min(Age), xr = max(Age), nseg = 20,
                 bdeg = 3, pord = 2, lambda = 1)
names(fit1)
plot(fit1, xlab = "Age", ylab = '0/1', se = 2)

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