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STAT660/FES758b
Multivariate Statistics
Homework #6 OPTION A: Ordination
Due : Wednesday, 4/26/17 – Submit on CANVAS by midnight
For this assignment, you can either use your own data or the data described below.
Use any combination of R/SAS/MINITAB/SPSS/STATA that you like.Whichever data
you choose, do the following:
1) Fit Correspondence Analysis to your data.
2) Discuss the inertia, make a two dimensional plot of the first two CA directions.
3) Comment on whether or not there is any evidence of ‘data snaking’ in higher
dimensional space.
4) In a few sentences, describe what you conclude from your plot.
5) Perform Multidimensional Scaling (metric or non-metric) for 1, 2, and 3
dimensions.
6) Discuss the stress (or SStress) of each dimensional solution.Make a scree plot
if you’re able.
7) Make a two dimensional plot of your results.
8) If possible, overlay some other variables to interpret your ordination axes.
9) BONUS – try canonical correspondence analysis, or calculate p-values for the
overlaid additional variables.
Loaner Data : choose ONE
Cereal.attitudes.csv : Marketing Survey Attitudes toward Cereals
8 Cereals
11 Questions (come back to, tastes nice, popular with all the family, very
easy to digest, nourishing, natural flavor, reasonably priced, a lot of food
value, stays crispy in milk, helps to keep you fit, fun for children to eat)
Values are percent of respondents who had a favorable response for a
particular cereal for that particular question.
T. K. Chakrapani and A. S. C. Ehrenberg, “An Alternative to Factor Analysis in
Marketing Research Part 2: Between Group Analysis”, PMRS Journal, Vol. 1, Issue 2,
October 1981, pp. 32-38.
R code to get you started :
#get the data
cereal <- read.csv(“http://reuningscherer.net/stat660/data/cereal.attitudes.csv”)Wiconsin.Forest.csv : Relative abundance of 14 species was measured on 10 plots. Plots were ordered from pioneer (early stage) to climax (late stage).The final column contains that stage of the forest on a scale from 1 to 10.Peet & Loucks (1977)R code to get you started : #get the data forest <- read.csv(“http://reuningscherer.net/stat660/data/Wisconsin.Forest.csv”) rownames(forest)=forest[,1] forestenv=matrix(forest[,17],ncol=1) rownames(forestenv)=forest[,1] colnames(forestenv)=c(“Stage”) forest=forest[,-c(1,17)] forestenv=data.frame(forestenv)
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