Description Usage Arguments Details Value Note References See Also Examples

Functions to answer the question "what radius is expected to include
proportion p of points from a circular bivariate distribution
corresponding to a given detection function", and the reverse. These
functions may be used to relate the scale parameter(s) of a detection
function (e.g., *σ*) to home-range area (specifically, the area
within an activity contour for the corresponding simple home-range
model) (see Note).

WARNING: the default behaviour of these functions changed in version
2.6.0. Integration is now performed on the cumulative hazard (exposure)
scale for all functions unless `hazard = FALSE`

. Results will
differ.

1 2 3 4 5 |

`p` |
vector of probability levels for which radius is required |

`r` |
vector of radii for which probability level is required |

`detectfn` |
integer code or character string for shape of detection function 0 = halfnormal, 2 = exponential etc. – see detectfn for other codes |

`sigma` |
spatial scale parameter of detection function |

`detectpar` |
named list of detection function parameters |

`hazard` |
logical; if TRUE the transformation |

`...` |
other arguments passed to |

`circular.r`

is the quantile function of the specified circular
bivariate distribution (analogous to `qnorm`

, for example). The
quantity calculated by `circular.r`

is sometimes called 'circular
error probable' (see Note).

For detection functions with two parameters (intercept and scale) it is
enough to provide `sigma`

. Otherwise, `detectpar`

should be a
named list including parameter values for the requested detection
function (g0 may be omitted, and order does not matter).

Detection functions in secr are expressed in terms of the decline
in probability of detection with distance *g(d)*, and both
`circular.r`

and `circular.p`

integrate this function by
default. Rather than integrating *g(d)* itself, it may be more
appropriate to integrate *g(d)* transformed to a hazard i.e. *1
- log(-g(d))*. This is selected with `hazard = TRUE`

.

Integration may fail with the message "maximum number of subdivisions reached". See Examples for how to increase the number of subdivisions.

Vector of values for the required radii or probabilities.

The term ‘circular error probable’ has a military origin. It is
commonly used for GPS accuracy with the default probability level set to
0.5 (i.e. half of locations are further than CEP from the true
location). A circular bivariate normal distriubution is commonly assumed
for the circular error probable; this is equivalent to setting
`detectfn = "halfnormal"`

.

Closed-form expressions are used for the normal and uniform cases; in
the circular bivariate normal case, the relationship is *r = (-2*log(1-p))^0.5 * sigma*. Otherwise,
the probability is computed numerically by integrating the radial
distribution. Numerical integration is not foolproof, so check
suspicious or extreme values.

When `circular.r`

is used with the default `sigma = 1`

, the result
may be interpreted as the factor by which sigma needs to be inflated to
include the desired proportion of activity (e.g., 2.45 sigma for 95%
of points from a circular bivariate normal distribution fitted on the hazard
scale (detectfn = 14) OR 2.24 sigma on the probability scale (detectfn = 0)).

Calhoun, J. B. and Casby, J. U. (1958) Calculation of home range and density of small mammals. Public Health Monograph No. 55. United States Government Printing Office.

Johnson, R. A. and Wichern, D. W. (1982) Applied multivariate statistical analysis. Prentice-Hall, Englewood Cliffs, New Jersey, USA.

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 | ```
## Calhoun and Casby (1958) p 3.
## give p = 0.3940, 0.8645, 0.9888
circular.p(1:3)
## halfnormal, hazard-rate and exponential
circular.r ()
circular.r (detectfn = "HR", detectpar = list(sigma = 1, z = 4))
circular.r (detectfn = "EX")
circular.r (detectfn = "HHN")
circular.r (detectfn = "HHR", detectpar = list(sigma = 1, z = 4))
circular.r (detectfn = "HEX")
plot(seq(0, 5, 0.01), circular.p(r = seq(0, 5, 0.01)),
type = "l", xlab = "Radius (multiples of sigma)", ylab = "Probability")
lines(seq(0, 5, 0.01), circular.p(r = seq(0, 5, 0.01), detectfn = 2),
type = "l", col = "red")
lines(seq(0, 5, 0.01), circular.p(r = seq(0, 5, 0.01), detectfn = 1,
detectpar = list(sigma = 1,z = 4)), type = "l", col = "blue")
abline (h = 0.95, lty = 2)
legend (2.8, 0.3, legend = c("halfnormal","hazard-rate, z = 4", "exponential"),
col = c("black","blue","red"), lty = rep(1,3))
## in this example, a more interesting comparison would use
## sigma = 0.58 for the exponential curve.
``` |

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