GP.plot.curves {BayesGPfit}  R Documentation 
Graphical representation of multiple curves in one and twodimensional curves
GP.plot.curves( curves, xlab = NULL, ylab = NULL, cols = NULL, lwd = NULL, type = NULL, leg_pos = NULL, xlim = NULL, ylim = NULL, col.regions = NULL, cut = NULL, ... )
curves 
A list object of multiple curves and each curve is a list with two elements:

xlab 
A character specifying the label of xaxis for 1D, 2D and 3D case. The default value is NULL and set to "x" for 1D case and "x1" for 2D case. 
ylab 
A character specifying the label of yaxis for 1D curve or coords for 2D and 3D case. The default value is NULL and set to "x2" for 2D case. 
cols 
A vector of integer numbers or characters to specify the plot colors for 1D curve. The default value is NULL and set to 1:length(curves). 
lwd 
A positive number to specify the width of lines for 1D curve. 
type 
A character specifying what type of plot should be drawn for 1D curve. Possible types are the same as plot. 
leg_pos 
A character spaecifying the position of legend for multiple 1D curves. Possible valeus are "topleft", "topright","bottemright","bottemleft". 
xlim 
A vector of two real numbers specifying the range of xaxis for 1D, 2D and 3D case. The default value is NULL and set to range(curve$x[,1]). 
ylim 
A vector of two real numbers specifying the range of yaxis only for 2D, 3D case. The default value is NULL and set to range(curve$x[,2]). 
col.regions 
A vector of RGB colors for 2D and 3D plots. See GP.create.cols. The default value is NULL and set to GP.create.cols(). 
cut 
An integer specifying the number of colors in 2D plots. The default value is NULL and set to length(col.regions)1. 
... 
All other parameters for plot (1D case) and levelplot (2D case). 
NULL for 1D case. An object of class "trellis" for 2D and 3D cases.
Jian Kang <jiankang@umich.edu>
library(BayesGPfit) library(lattice) set.seed(1227) dat = list() dat$x = GP.generate.grids(d=2L,num_grids = 100) curve = GP.simulate.curve.fast(dat$x,a=0.01,b=0.5,poly_degree=20L) dat$f = curve$f + rnorm(length(curve$f),sd=1) fast_fit = GP.fast.Bayes.fit(dat$f,dat$x,a=0.01,b=0.5,poly_degree=20L,progress_bar = TRUE) reg_fit = GP.Bayes.fit(dat$f,dat$x,a=0.01,b=0.5,poly_degree=20L,progress_bar = TRUE) curves = list(True = curve, Bayes_fast = fast_fit, Bayes = reg_fit) GP.plot.curves(curves, main="Comparisons of Bayesian model fitting")