# rsf: Resource Selection (Probability) Functions for... In ResourceSelection: 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

 ```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, ...) ```

## Arguments

 `formula` two sided model formula of the form `y ~ x`, where `y` is a vector of observations, `x` is the set of covariates. `m` argument describing the matching of use and available points. All available points are used for each use points if `m=0` (global availability). If `m` is a single value, e.g. `m=5`, it is assumed that available data points are grouped in batches of 5, e.g. with IDs `c(1,2)` for used point locations and `c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2)` for available locations (local availability, matched use-available design). Similarly, a vector of matching IDs can also be provided, e.g. `c(1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2)` by combining the above two. This potentially could allow for unbalanced matching and for easier subsetting of the data, but comes with an increased computing time. `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 `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.

## 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).

## 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` value of the `m` argument with possibly matched use-available design. `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.

## Author(s)

Subhash R. Lele, Jonah L. Keim, Peter Solymos

## 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

 ``` 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) ```

### Example output

```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

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

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
```

ResourceSelection documentation built on May 31, 2017, 4:51 a.m.