The VIT (Visual Inference Tools) package creates animations that are used for teaching statistical concepts such as bootstrap confidence interval construction, permutation testing and confidence intervals. One of the animations it can create is used for teaching the concept of sampling variation.
This example differs from the LOESS and
ARIMA examples because it requires far more
coordination once data has been given to the browser. This additional
complexity is reduced immensely when the
animaker package is used to describe
and apply the animation sequencing. This is a huge advantage that a gridSVG
implementation has over VIT. VIT has no way of keeping track of time because it
repeatedly draws as quickly as possible. This means that VIT animations vary in
length depending on how complex the grid scene is in addition to being
“choppy”. These limitations are not present when animating SVG content because
we can declaratively say what, when and for how long graphical content is being
The following video demonstrates the
sampvar example found in the
sjpMScThesis package, which can be downloaded at my Master’s Thesis page.
To run this example yourself, open R and run the following
library(sjpMScThesis) ; thesisExample("sampvar")
This video is best viewed in full-screen.
This video example is available in both MP4 (x264) and WebM formats: