robustgam (version 0.1.7)

cplot.robustgam: Plot of Component Smooth Functions from robustgam fit

Description

This function plots the component smooth functions.

Usage

cplot.robustgam(fit, ranges, len=100)

Arguments

fit

the robustgam fit

ranges

matrix with each column containing the range of predictor in each plot

len

grid size for the plot; it is used for all component smooth functions

References

Raymond K. W. Wong, Fang Yao and Thomas C. M. Lee (2013) Robust Estimation for Generalized Additive Models. Journal of Graphical and Computational Statistics, to appear.

See Also

robustgam.GIC, robustgam.GIC.optim, robustgam.CV, pred.robustgam

Examples

# load library
library(robustgam)

# test function
test.fun <- function(x, ...) {
  return(2*sin(2*pi*(1-x)^2))
}

# some setting
set.seed(1234)
true.family <- poisson()
out.prop <- 0.05
n <- 100

# generating dataset for poisson case
x <- runif(n)
x <- x[order(x)]
true.eta <- test.fun(x)
true.mu <- true.family$linkinv(test.fun(x))
y <- rpois(n, true.mu) # for poisson case

# create outlier for poisson case
out.n <- trunc(n*out.prop)
out.list <- sample(1:n, out.n, replace=FALSE)
y[out.list] <- round(y[out.list]*runif(out.n,min=3,max=5)^(sample(c(-1,1),out.n,TRUE)))

# robust GAM fit
robustfit <- robustgam(x, y, family=true.family, p=3, c=1.6, sp=0.000143514, show.msg=FALSE,
 smooth.basis='tp')
## the smoothing parameter is selected by the RBIC, the command is:
# robustfit.gic <- robustgam.GIC.optim(x, y, family=true.family, p=3, c=1.6, show.msg=FALSE,
#   count.lim=400, smooth.basis='tp', lsp.initial=log(1e-2) ,lsp.min=-15, lsp.max=10,
#   gic.constant=log(n), method="L-BFGS-B"); robustfit <- robustfit.gic$optim.fit


# ordinary GAM fit
nonrobustfit <- gam(y~s(x, bs="tp", m=3),family=true.family) # m = p for 'tp'

# prediction
x.new <- seq(range(x)[1], range(x)[2], len=1000)
robustfit.new <- pred.robustgam(robustfit, data.frame(X=x.new))$predict.values
nonrobustfit.new <- as.vector(predict.gam(nonrobustfit,data.frame(x=x.new),type="response"))

# plot
plot(x, y)
lines(x.new, true.family$linkinv(test.fun(x.new)), col="blue")
lines(x.new, robustfit.new, col="red")
lines(x.new, nonrobustfit.new, col="green")
legend(0.6, 23, c("true mu", "robust fit", "nonrobust fit"), col=c("blue","red","green"),
  lty=c(1,1,1))

# plot component smooth function
cplot.robustgam(robustfit, ranges=range(x))