hspider {VGAM} | R Documentation |
Abundance of hunting spiders in a Dutch dune area.
data(hspider)
A data frame with 28 observations (sites) on the following 18 variables.
The data, which originally came from Van der Aart and Smeek-Enserink (1975) consists of abundances (numbers trapped over a 60 week period) and 6 environmental variables. There were 28 sites.
This data set has been often used to illustrate ordination, e.g., using
canonical correspondence analysis (CCA). In the example below, the
data is used for constrained quadratic ordination (CQO; formerly called
canonical Gaussian ordination or CGO), a numerically intensive method
that has many superior qualities. See cqo
for details.
Van der Aart, P. J. M. and Smeek-Enserink, N. (1975) Correlations between distributions of hunting spiders (Lycosidae, Ctenidae) and environmental characteristics in a dune area. Netherlands Journal of Zoology, 25, 1–45.
data(hspider) str(hspider) ## Not run: # Fit a rank-1 Poisson CQO set.seed(111) # This leads to the global solution hspider[,1:6]=scale(hspider[,1:6]) # Standardize the environmental variables p1 = cqo(cbind(Alopacce, Alopcune, Alopfabr, Arctlute, Arctperi, Auloalbi, Pardlugu, Pardmont, Pardnigr, Pardpull, Trocterr, Zoraspin) ~ WaterCon + BareSand + FallTwig + CoveMoss + CoveHerb + ReflLux, fam = poissonff, data = hspider, Crow1posit=FALSE) nos = ncol(p1@y) lvplot(p1, y=TRUE, lcol=1:nos, pch=1:nos, pcol=1:nos) Coef(p1) summary(p1) # Fit a rank-1 binomial CAO hsbin = hspider # Binary species data hsbin[,-(1:6)] = as.numeric(hsbin[,-(1:6)] > 0) set.seed(123) ahsb1 = cao(cbind(Alopcune,Arctlute,Auloalbi,Zoraspin) ~ WaterCon + ReflLux, family = binomialff(mv=TRUE), df1.nl = 2.2, Bestof=3, data = hsbin) par(mfrow=2:1, las=1) lvplot(ahsb1, type="predictors", llwd=2, ylab="logit p", lcol=1:9) persp(ahsb1, rug=TRUE, col=1:10, lwd=2) coef(ahsb1) ## End(Not run)