04302a3ans - 2004 302 Assignment 3 model answers...

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2004 302 Assignment 3 model answers sfish=scale(fish) d=dist(sfish,method="manhattan") pco=cmdscale(d,k=(nrow(sfish)-1),eig=T) a) plot(pco$eig,type="b",xlab="Principal Axis",ylab="Eigenvalue") 0 5 10 15 20 25 0 50 100 150 200 Principal Axis Eigenvalue b) -4 -2 0 2 4 -4 -2 2 4 Principal Axis 1 Principal Axis 2 996 1997 1997 1997 1997 1998 1998 1998 1998 1999 1999 1999 1999 2000 2000 2000 2000 2001 2001 2001 2001 2002 2002 2002 2002 2003 2003
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Code: eqscplot(pco$points[,1:2],type="n",xlab="Principal Axis 1",ylab="Principal Axis 2") text(pco$points[,1:2],as.character(data[,1])) c) pco=cmdscale(d,k=2,eig=T) pco$GOF 55.7% of the absolute(variance) is explained by the first 2 principal axes. d) Could use bubble plots (but I will save trees). Correlations between PAs and variables: Albacore Yellowfin Skipjack Bigeye Sharks P.A.1 -0.438 0.237 0.764 0.709 0.804 P.A.2 -0.682 0.776 -0.205 -0.0436 -0.344 These identify Skipjack, Bigeye and Sharks as positively associated with principal axis 1. Principal axis 2 is a contrast between Albacore and Yellowfin ( Yellowfin positive). 2) Non metric MDS. dim=2 nm=isoMDS(d,k=dim) a) plot(c(14.69801,9.369532 , 6.297604,4.605904,3.409378, 2.749368),type=”b”)
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234567 4681 0 1 2 4 Dimension STRESS
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04302a3ans - 2004 302 Assignment 3 model answers...

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