crypho: A haploid microsatellite dataset for the chestnut blight...

In ggene: Semivariogram and Exploratory Spatial Analysis of Genetic Data

Description

A set of 10 locus for 276 individuals of the chestnut blight fungus Cryphonectria parasitica.

Usage

data(crypho)

Format

An object of class ggene.

Note

The coordinates of the individuals were slightly jittered (a few centimeters) because some individuals were superimposed. Jittering removed the duplicated points, hence the various warning messages issued by svariog. There is no consequences on the variograms because jeterring implied distances much lower than lag distance.

Source

Dutech, C., J.-P. Rossi, O. Fabreguettes and C. Robin 2008. Geostatistical genetic analysis for inferring the dispersal pattern of a partially clonal species: example of the chestnut blight fungus. Molecular ecology 17: 4597-4607.

Examples

data(crypho)

# check sampling scheme
plot(crypho$coord[,1],crypho$coord[,2], asp=1)

# compute matrix of weights
count <- genocount(X=crypho)
mat <- genoweight(X=crypho,genotyp=count$vec)

# compute distance intervals
d <- distlag(dist=crypho$coord, dmin=0,distance.lag=50)

# compute weighted variogram
wva <- varioWeight(X=crypho, weights=mat,  uvec=d)

# plot the variogram for raw data
plot(wva$svario$u, wva$svario$gamma, col="black", type="b", 
	ylim=range(c(wva$svario$gamma,wva$svario$v)), xlab="distance", ylab="semivariance")


# add the weighted variogram
points(wva$svario$u, wva$svario$v, col="red", type="b", pch=4)

legend("top", legend=c("raw", "weighted"), col=c("black", "red"), lty="solid", pch=c(1,4), bty="n")


## Not run: 

#performs randomization on raw variogram
va <- svariog(X=crypho, plot=FALSE)
env <- randsvariog(var=va, X=crypho, nsim=9, bounds=NULL, save.sim=FALSE)

#compute the weighted variogram
wva <- varioWeight(X=crypho, weights=mat)

#performs the randomizations on weighted variogram
env2 <- randsvariog(var=wva, X=crypho, nsim=9, bounds=NULL, save.sim=FALSE, weights=mat)

# plot results
xx <- c(wva$svario$u, rev(wva$svario$u))
yy <- c(env$env[,1], rev(env$env[,2]))
plot(xx, yy, type = "n", xlab = "distance", ylab = "semivariance", 
 ylim=range(c(env$env[,1], env$env[,2], env2$env[,1], env2$env[,2])))
polygon(xx, yy, col = "lightgrey", border = "black")
xx <- c(wva$svario$u, rev(wva$svario$u))
yy <- c(env2$env[,1], env2$env[,2])
points(xx, yy, type = "l")
polygon(xx, yy, col = "lightblue", border = "blue")

points(wva$svario$u, wva$svario$v, col="blue", typ="b")
points(wva$svario$u, wva$svario$gamma, col="black", type="b", lty="solid", bty="n")


## End(Not run)

# fit exponential model to empirical variogram
va <- svariog(X=crypho, plot=TRUE, messages=FALSE)
fit <- fitsvariog(vario=va, ini.cov.pars=c(0.03,100), nugget=0.1, max.dist=300, plot = TRUE)
fit$param

###

# compute variogram map
 map <- svarmap(X=crypho,cutoff=1000, width=50) ; plot(map)

ggene documentation built on May 2, 2019, 5:54 p.m.