sponge.grid: A dataset of predictors for generating sponge species...
In spm: Spatial Predictive Modeling
sponge.grid R Documentation
A dataset of predictors for generating sponge species richness in a selected region in the Timor Sea region, northern Australia marine margin
Description
This dataset contains 95530 rows of 7 predictive variables including longitude (easting), latitude (northing), 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.grid")
Format
A data frame with 95530 rows on the following 7 variables.
eastinga numeric vector, m
northinga numeric vector, m
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 used to produce the figure of predictions in the paper listed in the source.
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.grid | R Documentation |
A dataset of predictors for generating sponge species richness in a selected region in the Timor Sea region, northern Australia marine margin
Description
This dataset contains 95530 rows of 7 predictive variables including longitude (easting), latitude (northing), 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.grid")
Format
A data frame with 95530 rows on the following 7 variables.
eastinga numeric vector, m
northinga numeric vector, m
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 used to produce the figure of predictions in the paper listed in the source.
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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