plot.fpca.dfrr {dfrr} | R Documentation |
Plot dfrr functional principal components
Description
Plot a fpca.dfrr
object.
Usage
## S3 method for class 'fpca.dfrr'
plot(
x,
plot.eigen.functions = TRUE,
select = NULL,
plot.contour = FALSE,
plot.3dsurface = FALSE,
plot.contour.pars = list(breaks = NULL, minor_breaks = NULL, n.breaks = NULL, labels =
NULL, limits = NULL, colors = NULL, xlab = NULL, ylab = NULL, title = NULL),
plot.3dsurface.pars = list(xlab = NULL, ylab = NULL, zlab = NULL, title = NULL, colors
= NULL),
ask.hit.return = TRUE,
...
)
Arguments
x |
a |
plot.eigen.functions |
a |
select |
a vector of length one or more of indices of eigenfunctions to be plotted. |
plot.contour |
a |
plot.3dsurface |
a |
plot.contour.pars |
a named list of graphical parameters passed to the function |
plot.3dsurface.pars |
a named list of graphical parameters passed to the function |
ask.hit.return |
a boolean indicating whether to wait for interaction of the user between any two plots. |
... |
graphical parameters passed to |
Details
This function plots the functional principal components, contour plot and 3d surface of the kernel function.
If ggplot2-package
is installed, the contour plot of
the kernel function is produced by setting the argument plot.contour=TRUE
.
Some graphical parameters of the contour plot can be modified by setting the (optional) argument
plot.contour.pars
.
If the package plotly
is installed, the 3d surface of
the kernel function is produced by setting the argument plot.3dsurface=TRUE
.
Some graphical parameters of the 3d surface can be modified by setting the (optional) argument
plot.3dsurface.pars
.
Value
This function generates the plot of principal components.
Examples
set.seed(2000)
N<-50;M<-24
X<-rnorm(N,mean=0)
time<-seq(0,1,length.out=M)
Y<-simulate_simple_dfrr(beta0=function(t){cos(pi*t+pi)},
beta1=function(t){2*t},
X=X,time=time)
#The argument T_E indicates the number of EM algorithm.
#T_E is set to 1 for the demonstration purpose only.
#Remove this argument for the purpose of converging the EM algorithm.
dfrr_fit<-dfrr(Y~X,yind=time,T_E=1)
fpcs<-fpca(dfrr_fit)
plot(fpcs,plot.eigen.functions=TRUE,plot.contour=TRUE,plot.3dsurface=TRUE)