petrel: A dataset of seabed sediments in the Petrel sub-basin in...
In spm: Spatial Predictive Modeling
petrel R Documentation
A dataset of seabed sediments in the Petrel sub-basin in Australia Exclusive Economic Zone
Description
This dataset contains 237 samples of 9 variables including longitude (long),
latitude (lat), mud content (mud), sand content (sand), gravel content (gravel),
bathymetry (bathy), disttance to coast (dist), seabe relief (relief),
seabed slope (slope).
Usage
data("petrel")
Format
A data frame with 237 observations on the following 9 variables.
longa numeric vector, decimal degree
lata numeric vector, decimal degree
muda numeric vector, percentage
sanda numeric vector, percentage
gravela numeric vector, percentage
bathya numeric vector, meter bellow sea level
dista numeric vector, degree
reliefa numeric vector, meter
slopea numeric vector, no unit
Details
For details, please check the reference.
Source
Li, J., 2013. Predicting the spatial distribution of seabed gravel content using random forest, spatial interpolation methods and their hybrid methods, The International Congress on Modelling and Simulation (MODSIM) 2013: Adelaide, pp. 394-400.
spm documentation built on May 6, 2022, 9:06 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| petrel | R Documentation |
A dataset of seabed sediments in the Petrel sub-basin in Australia Exclusive Economic Zone
Description
This dataset contains 237 samples of 9 variables including longitude (long), latitude (lat), mud content (mud), sand content (sand), gravel content (gravel), bathymetry (bathy), disttance to coast (dist), seabe relief (relief), seabed slope (slope).
Usage
data("petrel")
Format
A data frame with 237 observations on the following 9 variables.
longa numeric vector, decimal degree
lata numeric vector, decimal degree
muda numeric vector, percentage
sanda numeric vector, percentage
gravela numeric vector, percentage
bathya numeric vector, meter bellow sea level
dista numeric vector, degree
reliefa numeric vector, meter
slopea numeric vector, no unit
Details
For details, please check the reference.
Source
Li, J., 2013. Predicting the spatial distribution of seabed gravel content using random forest, spatial interpolation methods and their hybrid methods, The International Congress on Modelling and Simulation (MODSIM) 2013: Adelaide, pp. 394-400.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.