BEDASSLE (version 1.6.1)

Covariance: The parametric covariance matrix

Description

This function parameterizes the decay in covariance of transformed allele frequencies between sampled populations/individuals over their pairwise geographic and ecological distance.

Usage

Covariance(a0, aD, aE, a2, GeoDist, EcoDist, delta)

Arguments

a0

This parameter controls the variance when pairwise distance is zero. It is the variance of the population-specific transformed allelic deviate (theta) when pairwise distances are zero (i.e. when \(D_{i,j} + E_{i,j} = 0\)).

aD

This parameter gives the effect size of geographic distance (\(D_{i,j}\)).

aE

This parameter gives the effect size(s) of ecological distance(s) (\(E_{i,j}\)).

a2

This parameter controls the shape of the decay in covariance with distance.

GeoDist

Pairwise geographic distance (\(D_{i,j}\)). This may be Euclidean, or, if the geographic scale of sampling merits it, great-circle distance.

EcoDist

Pairwise ecological distance(s) (\(E_{i,j}\)), which may be continuous (e.g. - difference in elevation) or binary (same or opposite side of some hypothesized barrier to gene flow).

delta

This gives the size of the "delta shift" on the off-diagonal elements of the parametric covariance matrix, used to ensure its positive-definiteness (even, for example, when there are separate populations sampled at the same geographic/ecological coordinates). This value must be large enough that the covariance matrix is positive-definite, but, if possible, should be smaller than the smallest off-diagonal distance elements, lest it have an undue impact on inference. If the user is concerned that the delta shift is too large relative to the pairwise distance elements in D and E, she should run subsequent analyses, varying the size of delta, to see if it has an impact on model inference.

Author

Gideon Bradburd

Examples

#With the HGDP dataset
	data(HGDP.bedassle.data)
	
#Draw random values of the {alpha} parameters from their priors
	alpha0 <- rgamma(1,shape=1,rate=1)
	alphaD <- rexp(1,rate=1)
	alphaE <- matrix(rexp(1,rate=1),nrow=1,ncol=1)
	alpha2 <- runif(1,0.1,2)

#Parameterize the covariance function using the HGDP dataset distances (Geo and Eco)
	example.covariance <- Covariance(a0 = alpha0,aD = alphaD,aE = alphaE,a2 = alpha2,
				GeoDist = HGDP.bedassle.data$GeoDistance,
				EcoDist = list(HGDP.bedassle.data$EcoDistance),
				delta = 0.001)

#Plot the example covariance against geographic distance
	plot(HGDP.bedassle.data$GeoDistance,
		example.covariance,
		pch=19,col=HGDP.bedassle.data$EcoDistance+1,
		main="Covariance in allele frequencies across the Himalayas")
			legend(x="topright",pch=19,col=c(1,2),
				legend=c("same side of Himalayas",
							"opposite sides of Himalayas"))