Soil characteristics data
soil.RdData used in Bondell and Reich's paper on soil characteristics used as predictors of forest diversity.
Usage
data("soil")Format
A data frame with 20 observations on the following 16 variables.
BaseSat% Base Saturation.
SumCationSum Cations (sums of cations like calcium, magnesium, potassium and sodium).
CECbufferCEC.
CaCalcium.
MgMagnesium.
KPotassium.
NaSodium.
PPhosphorus.
CuCopper.
ZnZinc.
MnManganese.
HumicMatterHumic Matter.
DensityDensity.
pHpH.
ExchAcExchangeable Acidity.
DiversityForest diversity (dependent variable).
References
Bondell, H.D. and Reich. B.J. (2008). Simultaneous regression shrinkage, variable selection, and supervised clustering of predictors with OSCAR. Biometrics, 64 (1), 115–23, doi: https://doi.org/10.1111/j.1541-0420.2007.00843.x.
Examples
head(soil, n=5)
#> BaseSat SumCation CECbuffer Ca Mg K Na P Cu Zn
#> 1 2.34 0.1576 0.614 0.0892 0.0328 0.0256 0.010 0.000 0.080 0.184
#> 2 1.64 0.0970 0.516 0.0454 0.0218 0.0198 0.010 0.000 0.064 0.112
#> 3 5.20 0.4520 0.828 0.3306 0.0758 0.0336 0.012 0.240 0.136 0.350
#> 4 4.10 0.3054 0.698 0.2118 0.0536 0.0260 0.014 0.030 0.126 0.364
#> 5 2.70 0.2476 0.858 0.1568 0.0444 0.0304 0.016 0.384 0.078 0.376
#> Mn HumicMatter Density pH ExchAc Diversity
#> 1 3.200 0.1220 0.0822 0.516 0.466 0.2765957
#> 2 2.734 0.0952 0.0850 0.512 0.430 0.2613982
#> 3 4.148 0.1822 0.0746 0.554 0.388 0.2553191
#> 4 3.728 0.1646 0.0756 0.546 0.408 0.2401216
#> 5 4.756 0.2472 0.0692 0.450 0.624 0.1884498
y = soil[,16]
x = soil[,-16]
x = cbind(rep(1, length(y)), x) # the design matrix has to have the intercept in the first column
multicollinearity(y, x)
#> System is computationally singular. Modify the design matrix before running the code.
multicollinearity(y, x[,-3]) # eliminating the problematic variable (SumCation)
#> RVIFs c0 c3 Scenario Affects
#> 1 4.407184e+02 6.150190e-03 1.480048e+00 b.1 Yes
#> 2 3.828858e+00 1.142356e-02 7.653413e+00 b.2 No
#> 3 1.093791e+05 1.254955e+02 7.236491e+04 b.1 Yes
#> 4 9.883235e+04 3.938383e+01 2.237445e+05 b.2 No
#> 5 1.767758e+05 1.101028e+03 3.609837e+05 b.2 No
#> 6 1.150029e+05 1.627349e+03 1.976176e+05 b.2 No
#> 7 4.627807e+04 5.960870e+02 2.033176e+06 b.2 No
#> 8 1.338591e+01 6.062571e-01 4.060382e+02 b.2 No
#> 9 3.113066e+02 4.089095e+01 5.246698e+05 b.2 No
#> 10 5.177176e+01 6.371216e+00 8.094828e+02 b.2 No
#> 11 1.905089e-01 3.907589e-02 9.787963e-01 b.2 No
#> 12 3.379360e+02 4.534540e+01 2.861964e+02 b.1 Yes
#> 13 4.761238e+04 8.453066e+01 3.828016e+08 b.2 No
#> 14 1.502903e+03 7.901580e+01 9.961215e+03 b.2 No
#> 15 1.066711e+05 2.369347e+02 4.802466e+07 b.2 No