Description Usage Arguments Details Value Author(s) References Examples
Resource Selection (Probability) Functions for use-availability 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 = "Nelder-Mead",
control, model = TRUE, x = FALSE, ...)
rspf(formula, data, m, B = 99, link = "logit", inits,
method = "Nelder-Mead", control, model = TRUE, x = FALSE, ...)
rsf.fit(X, Y, m, link = "logit", B = 99,
inits, method = "Nelder-Mead", 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 log-likelihood |
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)
|
ResourceSelection 0.3-2 2017-02-28
Call:
rsf(formula = status ~ . - status, data = dat1, m = 0, B = 0)
Resource Selection Function (Exponential RSF) model
Non-matched Used-Available design
Maximum Likelihood estimates
Fitted values:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.03488 0.69045 1.36192 2.39663 2.77403 40.65580
Coefficients (log link):
Estimate Std. Error z value Pr(>|z|)
x1 -1.0032 NA NA NA
x2 0.4711 NA NA NA
Log-likelihood: -8714
BIC = 1.744e+04
Hosmer and Lemeshow goodness of fit (GOF) test:
X-squared = 3.673, df = 8, p-value 0.8854
Call:
rspf(formula = status ~ . - status, data = dat2, m = 0, B = 0)
Resource Selection Probability Function (Logistic RSPF) model
Non-matched Used-Available design
Maximum Likelihood estimates
Fitted probabilities:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.1572 0.7945 0.8837 0.8474 0.9384 0.9962
Coefficients (logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.6406 0.7050 2.327 0.0200 *
x1 -1.0063 0.3983 -2.527 0.0115 *
x2 0.7426 0.8322 0.892 0.3722
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Log-likelihood: -9198
BIC = 1.842e+04
Hosmer and Lemeshow goodness of fit (GOF) test:
X-squared = 4.434, df = 8, p-value 0.816
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