plot.sop: Default SOP plotting

In SOP: Generalised Additive P-Spline Regression Models Estimation

Default SOP plotting

Description

Takes a fitted sop object produced by sop() and plots the component smooth functions that make it up, on the scale of the linear predictor.

Usage

## S3 method for class 'sop'
plot(x, rug = TRUE, pages = 0, select = NULL, grid, ...) 

Arguments

x	a fitted sop object as produced by sop().
rug	when TRUE (default) then the covariate to which the plot applies is displayed as a rug plot at the foot of each plot of a 1-d smooth. Setting to FALSE will speed up plotting for large datasets.

pages	(default 0) the number of pages over which to spread the output. For example, if pages=1 then all terms will be plotted on one page with the layout performed automatically. Set to 0 to have the routine leave all graphics settings as they are.
select	Allows the plot for a single model smooth term to be selected for printing. e.g. if you just want the plot for the second smooth term set select = 2.
grid	number of covariate values used for each 1-d plot - for a nice smooth plot this needs to be several times the estimated degrees of freedom for the smooth. Default value 100.

...	other graphics parameters to pass on to plotting commands. See details for smooth plot specific options.

Details

Produces default plot showing the smooth and random components of a fitted SOP.

For smooth terms plot.sop actually calls plot method functions depending on the dimension of the smooth function.

For plots of smooths in one dimension, the x axis of each plot is labelled with the covariate name, while the y axis is labelled 'f(cov),edf' where cov is the covariate name, and edf the estimated degrees of freedom of the smooth.

Several smooth plots methods using image will accept a colors argument, which can be anything documented in topo.colors (in which case something like colors=rainbow(50) is appropriate), or the grey function (in which case something like colors=grey(0:50/50) is needed).

Value

The function main purpose is to generate plots. It also (silently) returns a list of the data used to produce the plots for the smooth terms. This function is inspired by the plot.gam function of the same name described in mgcv package (but is not a clone).

See Also

sop, predict.sop

Examples

library(SOP)
## Simulate the data
set.seed(123)
n <- 1000
sigma <- 0.5
x <- runif(n)
f0 <- function(x) 2*sin(pi*x)
f <- f0(x)
y <- f + rnorm(n, 0, sigma)
dat <- data.frame(x = x, y = y)

# Fit the model
m0 <- sop(formula = y ~ f(x, nseg = 10), data = dat)
summary(m0)

# Plot results
plot(m0)

## An example of use of SOP package with tensor product B-splines in 2D
# Simulate the data
set.seed(123)
n <- 1000
sigma <- 0.1
x1 <- runif(n, -1, 1)
x2 <- runif(n, -1, 1)
f0 <- function(x1, x2) cos(2*pi*sqrt((x1 - 0.5)^2 + (x2 - 0.5)^2))
f <- f0(x1, x2)
y <- f + rnorm(n, 0, sigma)

dat <- data.frame(x1 = x1, x2 = x2, y = y)

m0 <- sop(formula = y ~ f(x1, x2, nseg = 10), data = dat, 
        control = list(trace = FALSE))

summary(m0)
plot(m0, col = topo.colors(100))

plot(m0, col = grey(0:100/100))

aux <- plot(m0)
names(aux)

SOP documentation built on Sept. 16, 2023, 1:07 a.m.