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

    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 0.7-9 Index]