| Soils | R Documentation |
Soil Compositions of Physical and Chemical Characteristics
Description
Soil characteristics were measured on samples from three types of contours (Top, Slope, and Depression) and at four depths (0-10cm, 10-30cm, 30-60cm, and 60-90cm). The area was divided into 4 blocks, in a randomized block design. (Suggested by Michael Friendly.)
Usage
Soils
Format
A data frame with 48 observations on the following 14 variables. There are 3 factors and 9 response variables.
Groupa factor with 12 levels, corresponding to the combinations of
ContourandDepthContoura factor with 3 levels:
DepressionSlopeTopDeptha factor with 4 levels:
0-1010-3030-6060-90Gpa factor with 12 levels, giving abbreviations for the groups:
D0D1D3D6S0S1S3S6T0T1T3T6Blocka factor with levels
1234pHsoil pH
Ntotal nitrogen in %
Densbulk density in gm/cm$^3$
Ptotal phosphorous in ppm
Cacalcium in me/100 gm.
Mgmagnesium in me/100 gm.
Kphosphorous in me/100 gm.
Nasodium in me/100 gm.
Conducconductivity
Details
These data provide good examples of MANOVA and canonical discriminant analysis in a somewhat
complex multivariate setting. They may be treated as a one-way design (ignoring Block),
by using either Group or Gp as the factor, or a two-way randomized block
design using Block, Contour and Depth (quantitative, so orthogonal
polynomial contrasts are useful).
Source
Horton, I. F.,Russell, J. S., and Moore, A. W. (1968) Multivariate-covariance and canonical analysis: A method for selecting the most effective discriminators in a multivariate situation. Biometrics 24, 845–858. Originally from ‘http://www.stat.lsu.edu/faculty/moser/exst7037/soils.sas’ but no longer available there.
References
Khattree, R., and Naik, D. N. (2000) Multivariate Data Reduction and Discrimination with SAS Software. SAS Institute.
Friendly, M. (2006) Data ellipses, HE plots and reduced-rank displays for multivariate linear models: SAS software and examples. Journal of Statistical Software, 17(6), doi: 10.18637/jss.v017.i06.