hspider {VGAM}R Documentation

Hunting Spider Data

Description

Abundance of hunting spiders in a Dutch dune area.

Usage

data(hspider)

Format

A data frame with 28 observations (sites) on the following 18 variables.

WaterCon
Log percentage of soil dry mass.
BareSand
Log percentage cover of bare sand.
FallTwig
Log percentage cover of fallen leaves and twigs.
CoveMoss
Log percentage cover of the moss layer.
CoveHerb
Log percentage cover of the herb layer.
ReflLux
Reflection of the soil surface with cloudless sky.
Alopacce
Abundance of Alopecosa accentuata.
Alopcune
Abundance of Alopecosa cuneata.
Alopfabr
Abundance of Alopecosa fabrilis.
Arctlute
Abundance of Arctosa lutetiana.
Arctperi
Abundance of Arctosa perita.
Auloalbi
Abundance of Aulonia albimana.
Pardlugu
Abundance of Pardosa lugubris.
Pardmont
Abundance of Pardosa monticola.
Pardnigr
Abundance of Pardosa nigriceps.
Pardpull
Abundance of Pardosa pullata.
Trocterr
Abundance of Trochosa terricola.
Zoraspin
Abundance of Zora spinimana.

Details

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.

References

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.

Examples

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)

[Package VGAM version 0.7-7 Index]