amt (version 0.1.7)

update_sl_distr: Update movement distributions

Description

Update tentative step length or turning angle distribution from a fitted iSSF.

Usage

update_sl_distr(
  object,
  beta_sl = "sl_",
  beta_log_sl = "log_sl_",
  beta_sl_sq = "sl_sq_",
  beta_log_sl_sq = "log_sl_sq_",
  ...
)
update_ta_distr(object, beta_cos_ta = "cos_ta_", ...)

Value

An amt_distr object, which consists of a list with the name of the distribution and its parameters (saved in params).

Arguments

object

[fit_clogit]
A fitted iSSF model.

beta_sl

[character]
The name of the coefficient of the step length.

beta_log_sl

[character]
The name of the coefficient of the log of the step length.

beta_sl_sq

[character]
The name of the coefficient of the square of the step length.

beta_log_sl_sq

[character]
The name of the coefficient of the square of log of the step length.

...

Further arguments, none implemented.

beta_cos_ta

[character]
The name of the coefficient of cosine of the turning angle.

Author

Brian J. Smith and Johannes Signer

References

fieberg2020guideamt

See Also

Wrapper to fit a distribution to data fit_distr()

Examples

# Fit an SSF, then update movement parameters.

# Prepare data for SSF
ssf_data <- deer %>%
  steps_by_burst() %>%
  random_steps(n = 15) %>%
  extract_covariates(sh_forest) %>%
  mutate(forest = factor(sh.forest, levels = 1:2,
                    labels = c("forest", "non-forest")),
  cos_ta_ = cos(ta_),
  log_sl_ = log(sl_))

# Check tentative distributions
# Step length
sl_distr_params(ssf_data)
attr(ssf_data, "sl_")
#    Turning angle
ta_distr_params(ssf_data)

# Fit an iSSF
m1 <- ssf_data %>%
  fit_issf(case_ ~ forest +
               sl_ + log_sl_ + cos_ta_ +
               strata(step_id_))

# Update step length distribution
new_gamma <- update_sl_distr(m1)

# Update turning angle distribution
new_vm <- update_ta_distr(m1)

# It is also possible to use different step length distributions

# exponential step-length distribution
s2 <- deer %>% steps_by_burst() %>%
  random_steps(sl_distr = fit_distr(.$sl_, "exp"))
m2 <- s2 %>%
  fit_clogit(case_ ~ sl_ + strata(step_id_))
update_sl_distr(m2)

# half normal step-length distribution
s3 <- deer %>% steps_by_burst() %>%
  random_steps(sl_distr = fit_distr(.$sl_, "hnorm"))
m3 <- s3 %>%
  mutate(sl_sq_ = sl_^2) %>%
  fit_clogit(case_ ~ sl_sq_ + strata(step_id_))
update_sl_distr(m3)

# log normal step-length distribution
s4 <- deer %>% steps_by_burst() %>%
  random_steps(sl_distr = fit_distr(.$sl_, "lnorm"))
m4 <- s4 %>%
  mutate(log_sl_ = log(sl_), log_sl_sq_ = log(sl_)^2) %>%
  fit_clogit(case_ ~ log_sl_ + log_sl_sq_ + strata(step_id_))
update_sl_distr(m4)