Description Usage Arguments Details Value See Also Examples
uhcdatasimulator
simulates the data used in Fieberg et al. (In Review)
1 | uhcdatasimulator(nused, navail, betas, corx, ntemp, example)
|
nused |
The number of used locations in training/test data set |
navail |
The number of background locations in training/test data set |
betas |
The vector of length 2 for *true* probability of use |
corx |
The correlation between elevation and precipitation in training/test dataset. For missing predictor example only. |
ntemp |
A large number of available points. |
example |
The name of the example. Options include "missing predictor" or "non-linear". |
This is a function that creates a dataframe based on the example
chosen from the manuscript (Fieberg et al. 2018). In the first example,
("missing predictor") the distribution of a species is related to elevation
(x_1) and precipitation (x_2), where x_1 and x_2
are normally distributed with mean 0 and variance 4. We considered 3
different data-generating scenarios in which we varied the correlation of
x_1,x_2 (corx
) in the training and test data sets.
In the second example ("non-linear"), the distribution of
a species is non-linearly related to temperature (x_3 \sim N(0,4)).
A dataframe of simulated data
Full archive of the data and code necessary to replicate the manuscript at http://doi.org/10.13020/D6T590.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | # Simulate training or test data for the non-linear example
nonlinear.data <- uhcdatasimulator(nused = 100,
navail = 10000,
betas = c(2,-1),
ntemp = 1000000,
example = "non-linear")
# Simulate training or test data for the missing predictor example
# Where corr(x1,x2) = 0
missingpredictor.0.data <- uhcdatasimulator(nused = 100,
navail = 10000,
betas = c(0.5,-1),
corx = 0,
ntemp = 1000000,
example = "missing predictor")
# Where corr(x1,x2) = -0.3
missingpredictor.N.data <- uhcdatasimulator(nused = 100,
navail = 10000,
betas = c(0.5,-1),
corx = -0.3,
ntemp = 1000000,
example = "missing predictor")
# Where corr(x1,x2) = 0.3
missingpredictor.P.data <- uhcdatasimulator(nused = 100,
navail = 10000,
betas = c(0.5,-1),
corx = 0.3,
ntemp = 1000000,
example = "missing predictor")
|
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