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Fit activity model to time-of-day data

mm_fit_activity.Rd

Fits kernel density to radian time-of-day data and estimates activity level from this distribution. Optionally: 1. bootstraps the distribution, in which case SEs and confidence limits are also stored for activity level and PDF; 2. weights the distribution; 3. truncates the distribution at given times.

Usage

mm_fit_activity(
  time_of_day,
  weights = NULL,
  n_boostrap = 1000,
  bandwidth = NULL,
  adjustment = 1,
  sample = c("none", "data", "model"),
  bounds = NULL,
  show = TRUE
)

Arguments

time_of_day

A numeric vector of radian time-of-day data

weights

A numeric vector of weights for each dat value.

n_boostrap

Number of bootstrap iterations to perform. Ignored if sample=="none"

bandwidth

Numeric value for kernel bandwidth. If NULL, calculated internally.

adjustment

Numeric bandwidth adjustment multiplier.

sample

Character string defining sampling method for bootstrapping errors (see details).

bounds

A two-element vector defining radian bounds at which to truncate.

show

Logical whether or not to show a progress bar while bootstrapping.

Value

A list

Details

When no bounds are given (default), a circular kernel distribution is fitted using dvmkern. Otherwise, a normal kernel distribution is used, truncated at the values of bounds, using density2.

The bandwidth adjustment multiplier adj is provided to allow exploration of the effect of adjusting the internally calculated bandwidth on accuracy of activity level estimates.

The alternative bootstrapping methods defined by sample are:

  • "none": no bootstrapping

  • "data": sample from the data

  • "model": sample from the fitted probability density distribution

It's generally better to sample from the data, but sampling from the fitted distribution can sometimes provide more sensible confidence intervals when the number of observations is very small.

Examples

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


fit_act <- mm_fit_activity(time_of_day = observations$time_radian,
                           sample = "model", n_boostrap = 100)

# Access activity level estimation
fit_act$activity
#> # A tibble: 1 × 4
#>     act     se lower_ci upper_ci
#>   <dbl>  <dbl>    <dbl>    <dbl>
#> 1 0.243 0.0856    0.156    0.481