ResourceSelection (version 0.1-3)

rsf: Resource Selection (Probability) Functions for Use-Availability Data

Description

Resource Selection (Probability) Functions for use-availability wildlife data as described in Lele and Keim (2006) and Lele (2009).

Usage

rsf(formula, data, B = 99, inits, method = "Nelder-Mead", 
control, model = TRUE, x = FALSE, ...)

rspf(formula, data, B = 99, link = "logit", inits, 
method = "Nelder-Mead", control, model = TRUE, x = FALSE, ...)

rsf.fit(X, Y, link = "logit", B = 99, 
inits, method = "Nelder-Mead", control, ...)

Arguments

formula

two sided model formula of the form y ~ x, where y is a vector of observations, x is the set of covariates.

data

data.

B

number of bootstrap iterations to make.

link

character, lype of link function to be used.

inits

initial values, optional.

method

method to be used in optim for numerical optimization.

control

control options for optim.

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.

Value

• 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 log-likelihood
• results
  optim results.
• link
  character, value of the link function used.
• control
  control parameters for optim.
• inits
  initial values used in optimization.
• m
  component for future developmen, currently it takes value 0.
• 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 B>0.
• converged
  logical, indicating convergence of the optimization.
• formula
  the formula supplied.
• terms
  the terms object used.
• 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.

Details

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).

References

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.

Examples

fun <- function(n.used, parms, data, m, link="logit") {
    X <- model.matrix(~., data)
    n.sites <- nrow(X)
    n.avail <- n.used * m
    linkinvfun <- binomial(link=make.link(link))$linkinv
    p <- drop(linkinvfun(X %*% parms))
    id1 <- sample.int(n.sites, n.used, replace = TRUE, prob = p)
    id2 = sample(n.sites, n.avail, replace = TRUE)
    data.frame(status=c(rep(1, n.used), rep(0, n.avail)), data[c(id1, id2),])
}
## 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 <- fun(n.used, cfs, x, m=m, link="log")
m1 <- rsf(status ~ .-status, dat1, B=0)
summary(m1)
## fitting Logistic RSPF model
dat2 <- fun(n.used, cfs, x, m=m, link="logit")
m2 <- rspf(status ~ .-status, dat2, B=0)
summary(m2)