| 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)