mgcViz (version 0.2.0)

plot.mgcv.smooth.1D: Plotting one dimensional smooth effects

Description

Plotting method for one dimensional smooth effects.

Usage

# S3 method for mgcv.smooth.1D
plot(
  x,
  n = 100,
  xlim = NULL,
  maxpo = 10000,
  trans = identity,
  unconditional = FALSE,
  seWithMean = FALSE,
  nsim = 0,
  ...
)
# S3 method for multi.mgcv.smooth.1D
plot(
  x,
  n = 100,
  xlim = NULL,
  maxpo = 10000,
  trans = identity,
  unconditional = FALSE,
  seWithMean = FALSE,
  asFact = NULL,
  ...
)

Value

An objects of class plotSmooth.

Arguments

x

a smooth effect object, extracted using sm.

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.

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.

trans

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

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.

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.

nsim

number of smooth effect curves to be simulated from the posterior distribution. These can be plotted using the l_simLine layer. See Examples section below.

...

currently unused.

asFact

determines whether to use a factor or colour bar legend for plot.multi.mgcv.smooth.1D. For most models the default is TRUE. When working with QGAM models fitted with mqgamV, the default is FALSE for less than 10 quantiles, TRUE otherwise. For QGAM models there a third option, asFact = "force", which forces the use of a discrete colour scale.

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)
n  <- 1e3
x1 <- rnorm(n)
x2 <- rnorm(n)
dat <- data.frame("x1" = x1, "x2" = x2,
                  "y" = sin(x1) + 0.5 * x2^2 + pmax(x2, 0.2) * rnorm(n))
b <- bamV(y ~ s(x1)+s(x2), data = dat, method = "fREML", aGam = list(discrete = TRUE))

o <- plot(sm(b, 1), nsim = 50) # 50 posterior simulations 

if (FALSE) {
# Plot with fitted effect + posterior simulations + rug on x axis
( o <- o + l_simLine() + l_fitLine(colour = "red") + 
       l_rug(alpha = 0.8) )

# Add CI lines at 1*sigma and 5*sigma
( o <- o + l_ciLine(mul = 1) + l_ciLine(mul = 5, colour = "blue", linetype = 2) )

# Add partial residuals and change theme
( o + l_points(shape = 19, size = 1, alpha = 0.2) + theme_classic() )

# Get second effect plot
o2 <- plot( sm(b, 2) )

# Plot it with polygon for partial residuals
o2 + l_ciPoly(mul = 5, fill = "light blue") + 
  l_fitLine(linetype = 2, colour = "red")

# Plot is with conditional density of partial residuals
o2 + l_dens(type = "cond", alpha = 0.9)  + 
  l_fitLine(linetype = 2, colour = "red")
  
########
# Quantile GAM example
########
# Fit model
b <- mqgamV(y ~ s(x1) + s(x2), qu = c(0.2, 0.5, 0.8), data = dat)

plot(sm(b, 1)) + l_fitLine(linetype = 2) + l_rug(colour = "blue")
}