sealPolys | R Documentation |
A polygon file for southeast Alaska, USA, of object type sf
. The data.frame contains 306 polygons with estimated trends of harbor seals for each polygon, and 157 polygons with missing data.
data(sealPolys)
A sf
object. The projection is in Alaska Alber, EPSG:3338. The data.frame has 463 records and 7 variables:
A factor variable with 1307 levels for all harbor seal polygons in Alaska. Only 463 of these occur in southeast Alaska. Each polygon has a unique polyid.
An integer from 8 to 12. There are 5 distinct genetic stocks for harbor seals in southeast Alaska, and each polygon is associated with one of the 5 stocks. Numbers 1 to 7, and 13, are stocks elsewhere in Alaska
A factor variablewith 13 levels, which are the names of the stocks.
The estimated trend of harbor seal abundance in each polygon, which was a linear estimate on the log scale using Poisson regression. We treat this as the response variable. 157 polygons have missing data on trends.
The estimated standard error of the estimated trend (on the log scale). We ignore this for demonstration purposes.
x-coordinate of the polygon centroid.
y-coordinate of the polygon centroid.
These data were collected by the Polar Ecosystem Program of the Marine Mammal Laboratory of the Alaska Fisheries Science Center of NOAA Fisheries. The data were used in the publication given below.
The data and code for the analyses were permanently archived as an R
package at link to: Zenodo. Here, we simplified the data into a single SpatialPolygonsDataFrame
. It was subsequently converted to sf
using st_as_sf
function.
Ver Hoef, J.M., Peterson, E. E., Hooten, M. B., Hanks, E. M., and Fortin, M.-J. 2018. Spatial Autoregressive Models for Statistical Inference from Ecological Data. Ecological Monographs, 88: 36-59. DOI: 10.1002/ecm.1283
Link to:link to: Spatial Autoregressive Models for Statistical Inference from Ecological Data..
library(ZVHdata) library(sf) data(sealPolys) names(sealPolys) summary(sealPolys) plot(sealPolys) boxplot(Estimate ~ as.factor(stockid), data = sealPolys) summary(lm(Estimate ~ as.factor(stockid), data = sealPolys))
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