# R Dataset / Package car / Soils

Submitted by pmagunia on March 9, 2018 - 1:06 PM
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## 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.

Group

a factor with 12 levels, corresponding to the combinations of Contour and Depth

Contour

a factor with 3 levels: Depression Slope Top

Depth

a factor with 4 levels: 0-10 10-30 30-60 60-90

Gp

a factor with 12 levels, giving abbreviations for the groups: D0 D1 D3 D6 S0 S1 S3 S6 T0 T1 T3 T6

Block

a factor with levels 1 2 3 4

pH

soil pH

N

total nitrogen in %

Dens

bulk density in gm/cm$^3$

P

total phosphorous in ppm

Ca

calcium in me/100 gm.

Mg

magnesium in me/100 gm.

K

phosphorous in me/100 gm.

Na

sodium in me/100 gm.

Conduc

conductivity

### 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), http://www.jstatsoft.org/v17/i06.

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Dataset imported from https://www.r-project.org.