plot.mgcv.smooth.2D: Plotting two dimensional smooth effects

In mfasiolo/mgcViz: Visualisations for Generalized Additive Models

Plotting two dimensional smooth effects

Description

Plotting method for two dimensional smooth effects.

Usage

## S3 method for class '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 class '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 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)
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)

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

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