hspider.RdAbundance of hunting spiders in a Dutch dune area.
data(hspider)A data frame with 28 observations (sites) on the following 18 variables.
Log percentage of soil dry mass.
Log percentage cover of bare sand.
Log percentage cover of fallen leaves and twigs.
Log percentage cover of the moss layer.
Log percentage cover of the herb layer.
Reflection of the soil surface with cloudless sky.
Abundance of Alopecosa accentuata.
Abundance of Alopecosa cuneata.
Abundance of Alopecosa fabrilis.
Abundance of Arctosa lutetiana.
Abundance of Arctosa perita.
Abundance of Aulonia albimana.
Abundance of Pardosa lugubris.
Abundance of Pardosa monticola.
Abundance of Pardosa nigriceps.
Abundance of Pardosa pullata.
Abundance of Trochosa terricola.
Abundance of Zora spinimana.
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.
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)
} # }