sponge: A dataset of sponge species richness in the Timor Sea region,...
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sponge R Documentation
A dataset of sponge species richness in the Timor Sea region, northern Australia marine margin
Description
This dataset contains 77 samples of 8 variables including longitude (easting), latitude (northing), sponge, topographic position index (tpi3), variance of backscatter (var7), entropy (entro7), backscatter at incidence angle 11 degree (bs11), and backscatter at incidence angle 34 degree (bs34).
Usage
data("sponge")
Format
A data frame with 77 observations on the following 8 variables.
eastinga numeric vector, m
northinga numeric vector, m
spongea numeric vector, no unit
tpi3a numeric vector, no unit
var7a numeric vector, dB^2
entro7a numeric vector, no unit
bs11a numeric vector, dB
bs34a numeric vector, dB
Details
For details, please see the source. This dataset was published as an appendix of the paper listed in the source. Where the long and lat were replaced with easting and northing for prediction purpose.
Source
Li, J., B. Alvarez, J. Siwabessy, M. Tran, Z. Huang, R. Przeslawski, L. Radke, F. Howard, and S. Nichol. 2017. Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness. Environmental Modelling & Software, 97: 112-129
spm documentation built on May 6, 2022, 9:06 a.m.
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| sponge | R Documentation |
A dataset of sponge species richness in the Timor Sea region, northern Australia marine margin
Description
This dataset contains 77 samples of 8 variables including longitude (easting), latitude (northing), sponge, topographic position index (tpi3), variance of backscatter (var7), entropy (entro7), backscatter at incidence angle 11 degree (bs11), and backscatter at incidence angle 34 degree (bs34).
Usage
data("sponge")
Format
A data frame with 77 observations on the following 8 variables.
eastinga numeric vector, m
northinga numeric vector, m
spongea numeric vector, no unit
tpi3a numeric vector, no unit
var7a numeric vector, dB^2
entro7a numeric vector, no unit
bs11a numeric vector, dB
bs34a numeric vector, dB
Details
For details, please see the source. This dataset was published as an appendix of the paper listed in the source. Where the long and lat were replaced with easting and northing for prediction purpose.
Source
Li, J., B. Alvarez, J. Siwabessy, M. Tran, Z. Huang, R. Przeslawski, L. Radke, F. Howard, and S. Nichol. 2017. Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness. Environmental Modelling & Software, 97: 112-129
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.
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