The spider data set

Description

Data set on abundances of spiders and environmental predictors. This is a subset of a larger data comprising 12 species and 6 environmental predictors. All variables are rated on a 0-9 scale.

Usage

data(spider6)

Format

A data frame with 28 observations on the following 9 variables.

Pard.lugu

a numeric vector

Pard.pull

a numeric vector

Troc.terr

a numeric vector

Pard.mont

a numeric vector

Alop.acce

a numeric vector

Alop.fabr

a numeric vector

Water

a numeric vector

Herbs

a numeric vector

Site

a factor with 28 levels

Source

package mvpart

References

De'ath G. (2002) Multivariate regression trees: A new technique for modeling species-environment relationships. Ecology, 2002, 83:1105–1117.

Examples

data(spider6)
summary(spider6)
fit0 <- mdm(y2p(spider6[,1:6])~1,data=spider6)
fit1 <- mdm(y2p(spider6[,1:6])~Water+Herbs,data=spider6)
fit2 <- mdm(y2p(spider6[,1:6])~Site,data=spider6,alpha=TRUE)
anova(fit0,fit1,fit2)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(MDM)
Loading required package: nnet
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MDM/spider6.Rd_%03d_medium.png", width=480, height=480)
> ### Name: spider6
> ### Title: The spider data set
> ### Aliases: spider6
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(spider6)
> summary(spider6)
   Pard.lugu       Pard.pull     Troc.terr      Pard.mont      Alop.acce  
 Min.   :0.000   Min.   :0.0   Min.   :0.00   Min.   :0.00   Min.   :0.0  
 1st Qu.:0.000   1st Qu.:0.0   1st Qu.:1.00   1st Qu.:0.75   1st Qu.:0.0  
 Median :1.000   Median :0.5   Median :4.00   Median :1.50   Median :1.0  
 Mean   :1.179   Mean   :2.5   Mean   :4.50   Mean   :2.50   Mean   :1.5  
 3rd Qu.:1.250   3rd Qu.:6.0   3rd Qu.:7.25   3rd Qu.:4.00   3rd Qu.:3.0  
 Max.   :7.000   Max.   :9.0   Max.   :9.00   Max.   :9.00   Max.   :5.0  
                                                                          
   Alop.fabr          Water           Herbs            Site   
 Min.   :0.0000   Min.   :0.000   Min.   :0.000   1      : 1  
 1st Qu.:0.0000   1st Qu.:3.750   1st Qu.:5.000   2      : 1  
 Median :0.0000   Median :6.000   Median :6.000   3      : 1  
 Mean   :0.9286   Mean   :5.464   Mean   :5.786   4      : 1  
 3rd Qu.:1.2500   3rd Qu.:8.000   3rd Qu.:9.000   5      : 1  
 Max.   :4.0000   Max.   :9.000   Max.   :9.000   6      : 1  
                                                  (Other):22  
> fit0 <- mdm(y2p(spider6[,1:6])~1,data=spider6)
# weights:  12 (5 variable)
> fit1 <- mdm(y2p(spider6[,1:6])~Water+Herbs,data=spider6)
# weights:  24 (15 variable)
initial  value 50.169265 
iter  10 value 36.791764
iter  20 value 35.415854
iter  30 value 35.415361
final  value 35.415361 
converged
> fit2 <- mdm(y2p(spider6[,1:6])~Site,data=spider6,alpha=TRUE)
# weights:  174 (140 variable)
> anova(fit0,fit1,fit2)
Deviances, Entropies and Diversities of Parametric Diversity Models

Response: y2p(spider6[, 1:6]) 

Model 1: y2p(spider6[, 1:6]) ~ 1
Model 2: y2p(spider6[, 1:6]) ~ Water + Herbs
Model 3: y2p(spider6[, 1:6]) ~ Site 

   DF DF-Diff    Dev Dev-Diff    Ent Ent-Diff    Div Div-Ratio
1 135         94.105          1.6804          5.3680          
2 125      10 70.831  23.2748 1.2648  0.41562 3.5425    1.5153
3   0     125 60.923   9.9075 1.0879  0.17692 2.9681    1.1935
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>