# Update movement distributions Update tentative step length or turning angle distribution from a fitted iSSF. ```r 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_", ...) ``` ## 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. ## Returns An `amt_distr` object, which consists of a list with the `name` of the distribution and its parameters (saved in `params`). ## Examples ```r # Fit an SSF, then update movement parameters. data(deer) mini_deer <- deer[1:100, ] sh_forest <- get_sh_forest() # Prepare data for SSF ssf_data <- mini_deer |> steps_by_burst() |> random_steps(n = 15) |> extract_covariates(sh_forest) |> mutate(forest = factor(forest, levels = 1:0, 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 <- mini_deer |> steps_by_burst() s2 <- random_steps(s2, sl_distr = fit_distr(s2$sl_, "exp")) m2 <- s2 |> fit_clogit(case_ ~ sl_ + strata(step_id_)) update_sl_distr(m2) # half normal step-length distribution s3 <- mini_deer |> steps_by_burst() s3 <- random_steps(s3, sl_distr = fit_distr(s3$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 <- mini_deer |> steps_by_burst() s4 <- random_steps(s4, sl_distr = fit_distr(s4$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) ``` ## References Rdpack::insert_ref(key="fieberg2020guide",package="amt") ## See Also Wrapper to fit a distribution to data `fit_distr()` ## Author(s) Brian J. Smith and Johannes Signer

update_distr function