mgcViz (version 0.2.1)

plot.fs.interaction.1D: Plotting one dimensional smooth factor interactions

Description

This method should be used to plot smooth effects of class "fs.interaction.1D", that is smooth constructed using the basis bs="tp". See mgcv::s.

Usage

# S3 method for fs.interaction.1D
plot(x, n = 100, xlim = NULL, trans = identity, ...)

Value

An object of class c("plotSmooth", "gg").

Arguments

x

a smooth effect object.

n

number of grid points used to compute main effect and c.i. lines. For a nice smooth plot this needs to be several times the estimated degrees of freedom for the smooth.

xlim

if supplied then this pair of numbers are used as the x limits for the plot.

trans

monotonic function to apply to the smooth and residuals, before plotting. Monotonicity is not checked.

...

currently unused.

Examples

library(mgcViz)
set.seed(0)
## simulate data...
f0 <- function(x) 2 * sin(pi * x)
f1 <- function(x, a = 2, b = -1) exp(a * x) + b
f2 <- function(x) 0.2 * x^11 * (10 * (1 - x))^6 + 10 *
  (10 * x)^3 * (1 - x)^10
n <- 500; nf <- 25
fac <- sample(1:nf, n, replace = TRUE)
x0 <- runif(n); x1 <- runif(n); x2 <- runif(n)
a <- rnorm(nf) * .2 + 2; b <- rnorm(nf) * .5
f <- f0(x0) + f1(x1, a[fac], b[fac]) + f2(x2)
fac <- factor(fac)
y <- f + rnorm(n) * 2
## so response depends on global smooths of x0 and
## x2, and a smooth of x1 for each level of fac.

## fit model (note p-values not available when fit
## using gamm)...
bm <- gamm(y ~ s(x0)+ s(x1, fac, bs = "fs", k = 5) + s(x2, k = 20))
v <- getViz(bm$gam)

# Plot with fitted effects and changing title 
plot(sm(v, 2)) + l_fitLine(alpha = 0.6) + labs(title = "Smooth factor interactions")

# Changing plotting limits
plot(sm(v, 2)) + l_fitLine() + ylim(-0.5, 0.5) + xlim(0.25, 0.75)

# Change line type and remove legend
plot(sm(v, 2)) + l_fitLine(size = 1.3, linetype="dotted") + 
                 theme(legend.position="none")
                 
# Clustering smooth effects in 3 groups
plot(sm(v, 2)) + l_fitLine(colour = "grey") + 
                 l_clusterLine(centers = 3, a.clu = list(nstart = 100))