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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 youre 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)