On this R-data statistics page, you will find information about the Soils data set which pertains to Soil Compositions of Physical and Chemical Characteristics. The Soils data set is found in the car R package. You can load the Soils data set in R by issuing the following command at the console data("Soils"). This will load the data into a variable called Soils. If R says the Soils data set is not found, you can try installing the package by issuing this command install.packages("car") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the Soils R data set. The size of this file is about 3,654 bytes.

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.