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Fit Random Encounter Model (REM)

mm_fit_rem.Rd

Fits a random encounter model using observed data and trap rate information. Automatically estimates detection radius, detection angle, animal speed, and activity pattern models if not provided.

Usage

mm_fit_rem(
  data,
  traprate_data,
  radius_model = NULL,
  angle_model = NULL,
  speed_model = NULL,
  activity_model = NULL,
  strata = NULL,
  time_of_day,
  n_bootstrap = 1000
)

Arguments

data

A data frame of observations, including distance, angle, speed, and time-of-day (in radians).

traprate_data

A data frame created by mm_traprate_data().

radius_model

Optional. A detection function model for radius (distance) fitted using mm_fit_detmodel().

angle_model

Optional. A detection function model for angle fitted using mm_fit_detmodel().

speed_model

Optional. A model for movement speed fitted using mm_fit_speedmodel().

activity_model

Optional. An activity model fitted with activity::fitact().

strata

Optional. A data frame of stratification information with columns stratumID and area.

time_of_day

The column name (unquoted or as a string) representing time-of-day in radians.

n_bootstrap

Number of bootstrap replicates for uncertainty estimation. Default is 1000.

Value

A data frame with columns:

  • parameters: Model parameter name

  • estimate: Estimated value

  • se: Standard error

  • cv: Coefficient of variation

  • lower_ci: Lower bound of 95% confidence interval

  • upper_ci: Upper bound of 95% confidence interval

See also

mm_fit_speedmodel(), mm_fit_detmodel(), mm_fit_activity()

Examples

data("camtrapdp")
deployments <- camtrapdp$data$deployments
observations <- camtrapdp$data$observations %>%
  dplyr::filter(scientificName == "Vulpes vulpes") %>%
  # Add time of day
  dplyr::mutate(time_of_day = mm_to_radian(times = timestamp))

# Prepare trap rate data
trap_rate <- mm_traprate_data(observation_data = observations,
                              deployment_data = deployments,
                              deployment_column = deploymentID,
                              datetime_column = timestamp,
                              start = start, end = 'end'
)


# Fit REM
mm_fit_rem(data = observations,
           traprate_data = trap_rate,
           time_of_day = time_of_day)
#> ℹ Fitting radius model
#> ✔ Fitting radius model ... done
#> 
#> ℹ Fitting angle model
#> ✔ Fitting angle model ... done
#> 
#> ℹ Fitting speed model
#> ✔ Fitting speed model ... done
#> 
#> ℹ Fitting activity model
#> ✔ Fitting activity model ... done
#> 
#> ℹ Calculating density
#> ✔ Calculating density ... done
#> 
#> 
#> # A tibble: 7 × 8
#>   parameters     estimate     se    cv lower_ci upper_ci     n unit   
#>   <chr>             <dbl>  <dbl> <dbl>    <dbl>    <dbl> <int> <chr>  
#> 1 radius            4.18   0.886 0.212    2.44     5.91      4 m      
#> 2 angle            44.4   11.3   0.254    0.39     1.16      5 degree 
#> 3 active_speed      3.08   0.765 0.248    0.439    1.27      4 km/hour
#> 4 activity_level    0.243  0.076 0.311    0.095    0.391    15 none   
#> 5 overall_speed    17.9    7.15  0.398    0.046    0.37     NA km/day 
#> 6 trap_rate         0.441  0.117 0.266    0.239    0.635     3 n/day  
#> 7 density           6.66   3.52  0.529    2.52    17.6      NA n/km2