Hunting Spider Data — hspider • VGAM

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)
#>     WaterCon         BareSand        FallTwig        CoveMoss     
#>  Min.   :0.9555   Min.   :0.000   Min.   :0.000   Min.   :0.0000  
#>  1st Qu.:2.1040   1st Qu.:0.000   1st Qu.:0.000   1st Qu.:0.6931  
#>  Median :2.6494   Median :0.000   Median :0.000   Median :1.7918  
#>  Mean   :2.4713   Mean   :1.129   Mean   :1.529   Mean   :2.1145  
#>  3rd Qu.:3.0922   3rd Qu.:2.560   3rd Qu.:4.296   3rd Qu.:3.7424  
#>  Max.   :3.5175   Max.   :4.511   Max.   :4.605   Max.   :4.3307  
#>     CoveHerb         ReflLux          Alopacce         Alopcune     
#>  Min.   :0.6931   Min.   :0.0000   Min.   : 0.000   Min.   : 0.000  
#>  1st Qu.:3.0445   1st Qu.:0.9972   1st Qu.: 0.000   1st Qu.: 0.000  
#>  Median :3.4340   Median :2.6492   Median : 2.000   Median : 1.000  
#>  Mean   :3.2550   Mean   :2.3618   Mean   : 6.214   Mean   : 5.393  
#>  3rd Qu.:4.4684   3rd Qu.:3.6889   3rd Qu.:12.000   3rd Qu.: 6.250  
#>  Max.   :4.6151   Max.   :4.3820   Max.   :29.000   Max.   :43.000  
#>     Alopfabr         Arctlute          Arctperi         Auloalbi     
#>  Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
#>  1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
#>  Median : 0.000   Median : 0.0000   Median : 0.000   Median : 0.000  
#>  Mean   : 3.464   Mean   : 0.9286   Mean   : 1.393   Mean   : 4.643  
#>  3rd Qu.: 3.000   3rd Qu.: 0.2500   3rd Qu.: 0.000   3rd Qu.: 6.250  
#>  Max.   :20.000   Max.   :12.0000   Max.   :18.000   Max.   :30.000  
#>     Pardlugu         Pardmont        Pardnigr        Pardpull     
#>  Min.   : 0.000   Min.   : 0.00   Min.   :  0.0   Min.   :  0.00  
#>  1st Qu.: 0.000   1st Qu.: 0.75   1st Qu.:  0.0   1st Qu.:  0.00  
#>  Median : 1.000   Median : 4.50   Median :  1.0   Median :  0.50  
#>  Mean   : 4.536   Mean   :16.04   Mean   : 14.5   Mean   : 20.79  
#>  3rd Qu.: 3.500   3rd Qu.:22.50   3rd Qu.: 15.0   3rd Qu.: 39.00  
#>  Max.   :55.000   Max.   :96.00   Max.   :135.0   Max.   :105.00  
#>     Trocterr         Zoraspin     
#>  Min.   :  0.00   Min.   : 0.000  
#>  1st Qu.:  2.00   1st Qu.: 0.000  
#>  Median : 22.50   Median : 2.000  
#>  Mean   : 34.68   Mean   : 6.607  
#>  3rd Qu.: 63.50   3rd Qu.: 6.750  
#>  Max.   :118.00   Max.   :34.000  

if (FALSE) { # \dontrun{
# 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 = "logitlink(p)", lcol = 1:9)
persp(ahsb1, rug = TRUE, col = 1:10, lwd = 2)
coef(ahsb1)
} # }