Resource Selection (Probability) Functions for useavailability wildlife data as described in Lele and Keim (2006) and Lele (2009).
1 2 3 4 5 6 7 8  rsf(formula, data, m, B = 99, inits, method = "NelderMead",
control, model = TRUE, x = FALSE, ...)
rspf(formula, data, m, B = 99, link = "logit", inits,
method = "NelderMead", control, model = TRUE, x = FALSE, ...)
rsf.fit(X, Y, m, link = "logit", B = 99,
inits, method = "NelderMead", control, ...)

formula 
two sided model formula of the form 
m 
argument describing the matching of use and available points.
All available points are used for each use points if 
data 
data. 
B 
number of bootstrap iterations to make. 
link 
character, type of link function to be used. 
inits 
initial values, optional. 
method 
method to be used in 
control 
control options for 
model 
a logical value indicating whether model frame should be included as a component of the returned value 
x 
logical values indicating whether the model matrix used in the fitting process should be returned as components of the returned value. 
Y 
vector of observations. 
X 
covariate matrix. 
... 
other arguments passed to the functions. 
The rsf
function fits the Exponential Resource Selection Function
(RSF) model to presence only data.
The rspf
function fits the Resource Selection Probability Function
(RSPF) model to presence only data Link function "logit"
,
"cloglog"
, and "probit"
can be specified via the
link
argument.
The rsf.fit
is the workhorse behind the two functions.
link="log"
leads to Exponential RSF.
LHS of the formula
data must be binary, ones indicating used locations,
while zeros indicating available location.
For model description and estimation details, see Lele and Keim (2006) and Lele (2009).
A list with class "rsf"
or "rspf"
containing the following components:
call 
the matched call. 
y 
vector from LHS of the formula. 
coefficients 
a named vector of coefficients. 
std.error 
a named vector of standard errors for the coefficients 
loglik 
the maximized loglikelihood 
results 

link 
character, value of the link function used. 
control 
control parameters for 
inits 
initial values used in optimization. 
m 
value of the 
np 
number of active parameters. 
fitted.values 
vector of fitted values. These are relative selection values for RSF models, and probability of selection for RSPF models. 
nobs 
number of used locations. 
bootstrap 
component to store bootstrap results if 
converged 
logical, indicating convergence of the optimization. 
formula 
the formula supplied. 
terms 
the 
levels 
a record of the levels of the factors used in fitting. 
contrasts 
the contrasts used. 
model 
if requested, the model frame. 
x 
if requested, the model matrix. 
Subhash R. Lele, Jonah L. Keim, Peter Solymos
Lele, S.R. (2009) A new method for estimation of resource selection probability function. Journal of Wildlife Management 73, 122–127.
Lele, S. R. & Keim, J. L. (2006) Weighted distributions and estimation of resource selection probability functions. Ecology 87, 3021–3028.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ## settings
n.used < 1000
m < 10
n < n.used * m
set.seed(1234)
x < data.frame(x1=rnorm(n), x2=runif(n))
cfs < c(1.5,1,0.5)
## fitting Exponential RSF model
dat1 < simulateUsedAvail(x, cfs, n.used, m, link="log")
m1 < rsf(status ~ .status, dat1, m=0, B=0)
summary(m1)
## fitting Logistic RSPF model
dat2 < simulateUsedAvail(x, cfs, n.used, m, link="logit")
m2 < rspf(status ~ .status, dat2, m=0, B=0)
summary(m2)

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
Please suggest features or report bugs with the GitHub issue tracker.
All documentation is copyright its authors; we didn't write any of that.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.