The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
This vignette describes basic usage of elastes in R.
Like many other R packages, the simplest way to obtain elastes is to install it directly from CRAN. Type the following command in your R console:
The purpose of this section is to give users a general sense of the package. We will briefly go over the main functions, basic operations and outputs
Firstly, load up the elastes package.
We load a set of 30 sparse handwritten digit 3’s from the shapes package for illustration. You may have to install the shapes package first.
The data still has the form of a three dimensional array. We will have to convert it to a list of data.frames, where each data.frame corresponds to one curve.
digit3 <- apply(digit3.dat, MARGIN = 3, FUN = function(curve){
data.frame(X1 = curve[,1], X2 = curve[,2])
})
We can compute a functional mean curve by calling
compute_elastic_shape_mean
by specifiying the set of knots
to be used and the type of mean. Here we estimate and plot a smooth mean
using 11 equidistant knots. The call to
plot.elastic_shape_mean
shows the estimated mean function,
together with the so called Procrustes fits - the centered and rotation
plus scaling aligned data curves.
mean_smooth <- compute_elastic_shape_mean(
digit3,
knots = seq(0, 1, length = 11),
type = "smooth"
)
plot(mean_smooth, main = "smooth mean")
Here we estimate and plot a polygonal mean using 13 equidistant knots.
mean_poly <- compute_elastic_shape_mean(
digit3,
knots = seq(0, 1, length = 13),
type = "polygon"
)
plot(mean_poly, col = "blue", main = "polygonal mean")
The estimated mean is the leading eigenfunction of the smoothed
Hermitian covariance surface of the data curves. In the covariance
estimation, the used penalty can be controlled by the
penalty
parameter.
The method can control for different types of measurement-error
variance along t, by setting the var_type
parameter.
See also the documentation for more information.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.