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Statistics 200: Lab 8
This week we will concentrate on a fancy technique, trellis graphics, for constructing complicated but elegant graphics. Your task is
to hand in some beautiful pictures. Show us your best picture.
To get started:
Generate a large matrix (maybe 500 x 3) containing a sample using
rnorm. Multiply by some 3x3 matrix (something not too simple--maybe 9
random numbers) to get a matrix of observations on a multivariate
normal. Call the columns of the matrix xx, yy, and zz.
trellis.device(win.graph) #starts up the trellis device for windows
Get help on xyplot().
At some stage during the lab you should
assign the output from xyplot to an object, then look at the
attributes of that object.
First just use xyplot() to plot yy versus xx. Then try for something
a little fancier, by writing a panel function that plots the points
and draws the least-squares line.
Hint: look at panel.xyplot() for some ideas.
Use the equal.count() function to create a shingle called
zslices, dividing the zz range into four slightly overlapping
subranges. Repeat the exercise from Problem 1, using xyplot with the
yy,xx, and zslices arguments to create a display with four little
plots (don't use mfrow()--trellis takes care of the multiple
plots), each showing a scatterplot with least-squares line for yy
versus xx in the different ranges defined by zslices.
(Similar to last week's Problems 4 and 5, but this
time using trellis.)
From the library (mapsdata section, the oldmap.df), get the oldmaps data from
last week, but in the form of a single dataframe with the names of the
source maps as a factor.
Write a function that accepts vectors of oldmap coordinates,
(representing the 39 points from a map, with suspect observations
flagged by minus signs) as arguments. Your function should plot the
points for the old map, together with arrows (or line segments)
joining the points to the corresponding Actual locations. Be sure
that you have west pointing to the left. If you are ambitious, try to
superimpose a map of the US (using the usa() function), so that you
can see where the actual landmarks are. It would also be nice if you
could somehow indicate those points that are flagged (by the minus
signs) in the data set as suspect--maybe use different plotting
symbols to indicate suspect points.
Modify your function from Problem 3 so that it can be used as the
panel argument for xyplot. Generate a trellis display, one little
picture for each old map, with each picture looking like the
map drawn in Problem 3.
If you wish to be very fancy, try to achieve some of the following
Make nicer headings for each panel.
Add a grid showing parallels of latitude and
Draw the maps in chronological order.
Write a function to produce the following trellis display:
For each old map,
fit a linear model (~ Actual$lat + Actual$long) to the
latitude and longitude. For each panel, show the residuals for the old
map by little arrows that are anchored at the Actual locations of the
landmarks. Embellish appropriately.