plot.sos.smooth: Plotting smooths on the sphere

In mfasiolo/mgcViz: Visualisations for Generalized Additive Models

Plotting smooths on the sphere

Description

This is the plotting method for smooth effects on the sphere.

Usage

## S3 method for class 'sos.smooth'
plot(
  x,
  n = 40,
  xlim = NULL,
  ylim = NULL,
  maxpo = 10000,
  too.far = 0.1,
  phi = 30,
  theta = 30,
  trans = identity,
  scheme = 0,
  seWithMean = FALSE,
  unconditional = FALSE,
  ...
)

Arguments

x	a smooth effect object, extracted using sm.
n	sqrt of the number of grid points used to compute the effect plot.
xlim	if supplied then this pair of numbers are used as the x limits for the plot.
ylim	if supplied then this pair of numbers are used as the y limits for the plot.
maxpo	maximum number of residuals points that will be used by layers such as resRug() and resPoints(). If number of datapoints > maxpo, then a subsample of maxpo points will be taken.
too.far	if greater than 0 then this is used to determine when a location is too far from data to be plotted. This is useful since smooths tend to go wild away from data. The data are scaled into the unit square before deciding what to exclude, and too.far is a distance within the unit square. Setting to zero can make plotting faster for large datasets, but care then needed with interpretation of plots.
phi	one of the plotting angles, relevant only if scheme = 0.
theta	the other plotting angle, relevant only if scheme = 0.
trans	monotonic function to apply to the smooth and residuals, before plotting. Monotonicity is not checked.
scheme	if 0 the smooth effect is plotted on the sphere. If 1 the smooth effect is plotted on the two hemispheres.
seWithMean	if TRUE the component smooths are shown with confidence intervals that include the uncertainty about the overall mean. If FALSE then the uncertainty relates purely to the centred smooth itself. Marra and Wood (2012) suggests that TRUE results in better coverage performance, and this is also suggested by simulation.
unconditional	if TRUE then the smoothing parameter uncertainty corrected covariance matrix is used to compute uncertainty bands, if available. Otherwise the bands treat the smoothing parameters as fixed.
...	currently unused.

Value

An objects of class plotSmooth.

References

Marra, G and S.N. Wood (2012) Coverage Properties of Confidence Intervals for Generalized Additive Model Components. Scandinavian Journal of Statistics.

Examples

library(mgcViz)
set.seed(0)
n <- 400

f <- function(la,lo) { ## a test function...
  sin(lo)*cos(la-.3)
}

## generate with uniform density on sphere...  
lo <- runif(n)*2*pi-pi ## longitude
la <- runif(3*n)*pi-pi/2
ind <- runif(3*n)<=cos(la)
la <- la[ind];
la <- la[1:n]

ff <- f(la,lo)
y <- ff + rnorm(n)*.2 ## test data

## generate data for plotting truth...
lam <- seq(-pi/2,pi/2,length=30)
lom <- seq(-pi,pi,length=60)
gr <- expand.grid(la=lam,lo=lom)
fz <- f(gr$la,gr$lo)
zm <- matrix(fz,30,60)

require(mgcv)
dat <- data.frame(la = la *180/pi,lo = lo *180/pi,y=y)

## fit spline on sphere model...
bp <- gam(y~s(la,lo,bs="sos",k=60),data=dat)
bp <- getViz(bp)

# Plot on sphere
plot(sm(bp, 1), scheme=0) + l_fitRaster() + l_fitContour() + 
   l_points(shape = 19) + l_rug() + l_coordContour() + l_bound() 
 
# Plotting as in standard 2D plots
plot(sm(bp, 1), scheme=1) + l_fitRaster() + l_fitContour() + 
           l_points(shape = 19) + l_rug()

mfasiolo/mgcViz documentation built on April 14, 2024, 4:20 a.m.