Plotting two dimensional smooth effects

Source: R/plot_mgcv_smooth_2D.R, R/plot_multi_mgcv_smooth_2D.R

Plotting method for two dimensional smooth effects.

# S3 method for mgcv.smooth.2D
plot(
  x,
  n = 40,
  xlim = NULL,
  ylim = NULL,
  maxpo = 10000,
  too.far = 0.1,
  trans = identity,
  seWithMean = FALSE,
  unconditional = FALSE,
  ...
)

# S3 method for multi.mgcv.smooth.2D
plot(
  x,
  n = 30,
  xlim = NULL,
  ylim = NULL,
  maxpo = 10000,
  too.far = 0.1,
  trans = identity,
  seWithMean = FALSE,
  unconditional = FALSE,
  a.facet = list(),
  ...
)

Arguments

x

a smooth effect object, extracted using mgcViz::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.
trans

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

arguments to be passed to ggplot2::facet_wrap or ggplot2::facet_grid. The former gets called when fix contains one vector, the latter when fix contains two vectors.

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(2) ## simulate some data...
dat <- gamSim(1, n = 700, dist = "normal", scale = 2)
#> Gu & Wahba 4 term additive model
b <- gam(y ~ s(x0) + s(x1, x2) + s(x3), data = dat, method = "REML")
b <- getViz(b)

# Plot 2D effect with noised-up raster, contour and rug for design points 
# Opacity is proportional to the significance of the effect
plot(sm(b, 2)) + l_fitRaster(pTrans = zto1(0.05, 2, 0.1), noiseup = TRUE) +
  l_rug() + l_fitContour()

# Plot contour of effect joint density of design points
plot(sm(b, 2)) + l_dens(type = "joint") + l_points() + l_fitContour() +
  coord_cartesian(expand = FALSE) # Fill the plot

###
# Quantile GAM example
###
b <- mqgamV(y ~ s(x0) + s(x1, x2) + s(x3), qu = c(0.3, 0.7), data = dat)
#> Estimating learning rate. Each dot corresponds to a loss evaluation. 
#> qu = 0.7........done 
#> qu = 0.3.........done 

plot(sm(b, 2)) + l_fitRaster(noiseup = TRUE) + l_fitContour(colour = 2)