hard | R Documentation |

This dataset contains 137 samples of 17 variables including area surveyed (Area), easting, northing, prock, bathymetry (bathy), backscatter (bs), local Moran I (bathy.moran), plannar curvature (planar.curv), profile curvature (profile.curv), topographic relief (relief), slope (slope), surface area (surface), topographic position index (tpi), homogeneity of backscatter (homogeneity), local Moran I of backscatter (bs.moran), variance of backscatter (variance) and seabed hardness (hardness).

data("hard")

A data frame with 137 observations on the following 17 variables.

`Area`

a catergorical vector, no unit

`easting`

a numeric vector, m

`northing`

a numeric vector, m

`prock`

a numeric vector, no unit

`bathy`

a numeric vector, meter

`bs`

a numeric vector, dB

`bathy.moran`

a numeric vector, no unit

`planar.curv`

a numeric vector, no unit

`profile.curv`

a numeric vector, no unit

`relief`

a numeric vector, meter

`slope`

a numeric vector, no unit

`surface`

a numeric vector, no unit

`tpi`

a numeric vector, no unit

`homogeneity`

a numeric vector, no unit

`bs.moran`

a numeric vector, no unit

`variance`

a numeric vector, dB^2

`hardness`

a catergorical vector, no unit

For details, please see the source. This dataset was modified by removing 3 samples with missing values from Appendix AA of the book chapter listed in the source.

Li, J., J. Siwabessy, M. Tran, Z. Huang, and A. Heap. 2013. Predicting Seabed Hardness Using Random Forest in R. Pages 299-329 in Y. Zhao and Y. Cen, editors. Data Mining Applications with R. Elsevier.

spm documentation built on May 6, 2022, 9:06 a.m.

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