Description Usage Arguments Details Value See Also Examples
uhcsim
samples, randomly, locations from non-stratified
test data.
1 | uhcsim(nsims, nused_test, xmat, fit_rsf, z)
|
nsims |
The number of simulations (M) used to create the UHC plot. |
nused_test |
The number of used locations in the test data set. |
xmat |
A matrix of predictor variables in the test data. |
fit_rsf |
The fitted logistic regression model object |
z |
A vector or matrix of (used & available) environmental characteristics in the test data set. |
This function samples, randomly, locations from non-stratified test data and returns an array of dimension nsims x nused_test x p (where p is the number of predictors to be validated)
An array of dimensions nsims x nused_test x p.
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 32 33 34 35 36 37 | # Simulate training data for the non-linear example
nonlinear.train <- uhcdatasimulator(nused = 100,
navail = 10000,
betas = c(2,-1),
ntemp = 1000000,
example = "non-linear")
# Simulate test data for the non-linear example
nonlinear.test <- uhcdatasimulator(nused = 100,
navail = 10000,
betas = c(2,-1),
ntemp = 1000000,
example = "non-linear")
# Fit GLM with quadratic relationship
train.correct <- glm(y~temp + I(temp^2),
family = binomial,
data = nonlinear.train)
# Fit GLM with linear (misspecified) relationship
train.misspec <- glm(y~temp,
family = binomial,
data = nonlinear.train)
# Simulate data for quadratic model
xhat.correct <- uhcsim(nsims = 1000,
nused_test = 100,
xmat = model.matrix(y~temp + I(temp^2), data = nonlinear.test)[,-1],
fit_rsf = train.correct,
z = as.matrix(nonlinear.test[,"temp"]))
# Simulate data for linear (misspecified) model
xhat.misspec <- uhcsim(nsims = 1000,
nused_test = 100,
xmat = as.matrix(model.matrix(y~temp, data = nonlinear.test)[,2]),
fit_rsf = train.misspec,
z = as.matrix(nonlinear.test[,"temp"]))
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