plotRGL.mgcv.smooth.MD: Visualizing a 2D slice of a smooth effects in 3D

In mfasiolo/mgcViz: Visualisations for Generalized Additive Models

Visualizing a 2D slice of a smooth effects in 3D

Description

This method plots an interactive 3D representation of a 2-dimensional slice of a multi-dimensional smooth effect, using the rgl package.

Usage

## S3 method for class 'mgcv.smooth.MD'
plotRGL(
  x,
  fix,
  se = TRUE,
  n = 40,
  residuals = FALSE,
  type = "auto",
  maxpo = 1000,
  too.far = c(0, NA),
  xlab = NULL,
  ylab = NULL,
  main = NULL,
  xlim = NULL,
  ylim = NULL,
  se.mult = 1,
  trans = identity,
  seWithMean = FALSE,
  unconditional = FALSE,
  ...
)

Arguments

x	a smooth effect object, extracted using sm.
fix	a named vector indicating which variables must be kept fixed and to what values. When plotting a smooth in (d+2) dimensions, then d variables must be fixed.
se	when TRUE (default) upper and lower surfaces are added to the plot at se.mult (see below) standard deviations for the fitted surface.
n	sqrt of the number of grid points used to compute the effect plot.
residuals	if TRUE, then the partial residuals will be added.
type	the type of residuals that should be plotted. See residuals.gamViz.
maxpo	maximum number of residuals points that will be plotted. If number of datapoints > maxpo, then a subsample of maxpo points will be taken.
too.far	a numeric vector with two entries. The first has the same interpretation as in plot.mgcv.smooth.2D and it avoids plotting the smooth effect in areas that are too far form any observation. The distance will be calculated only using the variables which are not in fix (see above). Hence in two dimensions, not in the full d+2 dimensions. Set it to -1 to plot the whole smooth. The second entry determines which residuals and covariates pairs are closed enough to the selected slice. If left to NA on the 10\ closest (in terms of scaled Euclidean distance) to the current slice will be plotted. Set it to -1 to plot all the residuals.
xlab	if supplied then this will be used as the x label of the plot.
ylab	if supplied then this will be used as the y label of the plot.
main	used as title for the plot if supplied.
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.
se.mult	a positive number which will be the multiplier of the standard errors when calculating standard error surfaces.
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.

Value

Returns NULL invisibly.

References

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

Examples

# Example 1: taken from ?mgcv::te, shows how tensor pruduct deals nicely with 
# badly scaled covariates (range of x 5% of range of z )
library(mgcViz)
n <- 1e3
x <- rnorm(n); y <- rnorm(n); z <- rnorm(n)

ob <- (x-z)^2 + (y-z)^2 + rnorm(n)
b <- gam(ob ~ s(x, y, z))
v <- getViz(b)

plotRGL(sm(v, 1), fix = c("z" = 0))

# Need to load rgl at this point
## Not run: 
library(rgl)
rgl.close() # Close

plotRGL(sm(v, 1), fix = c("z" = 1), residuals = TRUE)

# We can still work on the plot, for instance change the aspect ratio
aspect3d(1, 2, 1)

rgl.close() # Close

## End(Not run)

mfasiolo/mgcViz documentation built on April 19, 2024, 8:16 a.m.