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

    summary(hspider)

    ## Not run:
    # Standardize the environmental variables:
    hspider[, 1:6] <- scale(subset(hspider, select = WaterCon:ReflLux))

    # 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(multiple.responses = 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 1.0-4 Index]