Quick tutorial for analysing your own data
--------------------------------------------------------

[0. Download R (http://cran.r-project.org) and install it 
if you haven't done so already. Install the shapes package.]

1. Start R on your machine

2. load in the shapes library by typing:

library(shapes)

3. Read in your data. This is often the hardest part!    
For 2D points the co-ordinates should be in a file in the form:

x1 y1 x2 y2 x3 y3 ... xk yk

with object 1 followed by object 2 followed by object 3 .... etc. 

See: http://www.maths.nott.ac.uk/~ild/bookdata  
for some examples. The co-ordinates should be white space 
separated and return characters
are ignored. For 3D points the data should be in a file in the form: 

x1 y1 z1 x2 y2 z2 x3 y3 z3 ... xk yk zk

for each object. 
One way to read in the data is to type in: 

k<-8                                                                                      #Number of points
m<-2                                                                                     #Number of dimensions
new.dat<-read.in("c:/Windows/Desktop/gorf.dat",k,m)        

#Note the use of / here                                      
#Note <- is the same thing as =                                         
Any text after a # sign is just a comment and need not be typed again of course!

There are some datasets already loaded into the shapes library  
(e.g. gorf.dat,gorm.dat,qset2.dat,qcet2.dat,qlet2.dat,macm.dat,macf.dat,digit3.dat). 


4. Carry out shape analysis - see the manual for examples. 
e.g. 

plotshapes(new.dat)                                                   #plot the data
out.procf<-procGPA(new.dat)                                    #carries out generalised Procrustes analysis of females
plotshapes(out.procf$rotated)                                    #plots the Procrustes rotated objects
shapepca(out.procf)                                                      #plots the first few PCs
out.procm<-procGPA(gorm.dat)                                    #carries out generalised Procrustes analysis of males
plotshapes(out.procm$rotated)                                    #plots the Procrustes rotated objects
shapepca(out.procm)                                                      #plots the first few PCs
tpsgrid(out.procf$mshape,out.procm$mshape,-0.6,-0.6,1.2)        #TPS grid from female mean to male mean
riemdist(out.procf$mshape,out.procm$mshape)         #the Riemannian shape distance between the 
                                                                                           #mean shapes 

5. For further information on each command, including options type 

help(procGPA)

For example if you want to carry out Procrustes analysis without scaling then type: 

out.noscale<-procGPA(new.dat,scale=FALSE)


6. Further examples can be seen in the help() commands or in the manual.
Also, there is a short demo obtained by typing:

demo(shapes)

Ian Dryden 18-12-03