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Fit animal detection

mm_fit_detmodel.Rd

Fits a detection function (either point or line transect) to model detection radius or angle.

Usage

mm_fit_detmodel(
  formula,
  data,
  newdata = NULL,
  unit = c("m", "km", "cm", "degree", "radian"),
  ...
)

Arguments

formula

A formula specifying the response (e.g., radius ~ 1 or angle ~ covariate).

data

A data frame containing detection observations.

newdata

Optional new data frame with covariate values for prediction.

unit

Unit of the detection variable. One of "m", "km", "cm" for distance, or "degree", "radian" for angle.

...

Additional arguments passed to Distance::ds().

Value

a list with elements:

  • ddf a detection function model object.

  • dht abundance/density information (if survey region data was supplied, else NULL)

See also

mm_fit_rem(), mm_fit_speedmodel(), mm_fit_activity()

Examples

data("camtrapdp")
observations <- camtrapdp$data$observations %>%
  dplyr::filter(scientificName == "Vulpes vulpes")

mm_fit_detmodel(radius ~ 1, data = observations)
#> Error in -lt$value : invalid argument to unary operator
#> 
#> Distance sampling analysis object
#> 
#> Summary for ds object
#> Number of observations :  5 
#> Distance range         :  0  -  7.226863 
#> AIC                    :  22.73211 
#> Optimisation           :  mrds (nlminb) 
#> 
#> Detection function:
#>  Half-normal key function 
#> 
#> Detection function parameters 
#> Scale coefficient(s): 
#>             estimate        se
#> (Intercept) 1.722192 0.6432342
#> 
#>                      Estimate        SE        CV
#> Average p           0.6783846 0.3138098 0.4625839
#> N in covered region 7.3704500 3.8882665 0.5275481
#> EDR                 5.9523416 1.3767287 0.2312920

# For angle
mm_fit_detmodel(angle ~ 1, data = observations)
#> Error in -lt$value : invalid argument to unary operator
#> 
#> Distance sampling analysis object
#> 
#> Summary for ds object
#> Number of observations :  5 
#> Distance range         :  0  -  0.5841587 
#> AIC                    :  0.3802033 
#> Optimisation           :  mrds (nlminb) 
#> 
#> Detection function:
#>  Half-normal key function 
#> 
#> Detection function parameters 
#> Scale coefficient(s): 
#>              estimate        se
#> (Intercept) -1.606023 0.1353736
#> 
#>                      Estimate          SE        CV
#> Average p            0.232632  0.05906989 0.2539199
#> N in covered region 21.493176 10.03408954 0.4668500
#> EDR                  0.281751  0.03577110 0.1269600