View source: R/EKF_interp_joint.r

EKF_interp_joint | R Documentation |

Extended Kalman Filter (EKF) for joint shark movement with interpolation

```
EKF_interp_joint(area_map=NULL, d, npart=100, sigma_pars, tau_pars,
mu0_pars=list(alpha=c(-4.5 ,-2), beta=c(0,0)),
V0_pars=list(alpha=c(0.25, 0.25), beta=c(0.25, 0.25)),
Errvar0=rep(list(diag(2)), 2),
Errvar_df=c(20, 20), Particle_errvar0, Particle_err_df=20,
dirichlet_init=c(9,2,2,7), logvelocity_truncate=c(-10, 15),
maxStep=NULL, delaysample=1, state_favor=c(1,1),
nstates=2,centroids=matrix(c(0,0), ncol=2),
truncate_to_map=TRUE, enforce_full_line_in_map=TRUE,
do_trunc_adjust=TRUE, lowvarsample=TRUE,
time_radius=60*30, spat_radius=300, min_num_neibs=10,
interact=TRUE, interact_pars=list(mu0=0, precision0=2,
known_precision=2), neff_sample=1, time_dep_trans=FALSE,
time_dep_trans_init=dirichlet_init, smoothing=FALSE,
fix_smoothed_behaviors=TRUE, smooth_parameters=TRUE,
reg_dt=120, max_int_wo_obs=NULL, resamp_full_hist=TRUE,
compare_with_known=FALSE, known_trans_prob=NULL,
known_foraging_prob=NULL, known_regular_step_ds=NULL,
update_eachstep=FALSE, update_params_for_obs_only=FALSE,
output_plot=TRUE, loc_pred_plot_conf=0.5,
output_dir=getwd(), pdf_prefix="EKF_2D", verbose=3)
```

`area_map` |
Shapefile within which the observations are located (optional). Should be the output of applying |

`d` |
Dataset of observations, with required variable columns: tag, X, Y, logvelocity, speed, turn.angle.rad, region (optional), date_as_sec, time_to_next, state.guess2, prev.guess2. |

`npart` |
Number of particles to be used in simulation. |

`sigma_pars` |
Vector of inverse-gamma parameters for sigma^2 (logV variance). Two elements for each state. The inverse gamma parameters are specified in pairs. |

`tau_pars` |
Vector of inverse-gamma parameters for tau^2 (turn angle variance). |

`mu0_pars` |
List of initial values of mean logV (alpha) and turn (beta) for one or two behavioral states. |

`V0_pars` |
List of initial values of degrees of freedom of inverse-gamma sigma and tau (variances of alpha and beta) for one or two behavioral state. |

`Errvar0` |
List of prior 2x2 covariance matrices for predicting y from x, one for each behavioral state. |

`Errvar_df` |
Vector of degrees of freedom of |

`Particle_errvar0` |
Prior 2x2 covariance matrix for predicting x_t from x_t-1. |

`Particle_err_df` |
Degree of freedom of |

`dirichlet_init` |
List of 4-element vectors specifying Dirichlet parameters for transition matrices for each region. Will be replicated to equal number of regions. |

`logvelocity_truncate` |
When simulating log-velocity, a vector of the allowable range (values outside will be truncated to fall in this range). Log-velocity is simulated by a normal distribution (which is symmetric but can be positive or negative), so that speed (=exp(log_velocity)) will be positive. However, the transformation has asymmetric impact in that, say, a fixed error in underestimating log-velocity results in a smaller displacement (when translated to speed and thus distance) than the same error over-estimated. The variance of log-velocity takes into account low and high values equally. This restriction prevents the variance from growing too large from low (e.g. very negative) values of log-velocity, which will then cause large over-estimates of speed and distance traveled. The difference between, say, log-velocity of -2 and -50 is very small in practical terms of distance, but the effect on the variance will be much larger for the -50. |

`maxStep` |
Maximum number of regular steps to simulate. Default is NULL, meaning that the number of regular steps simulated will be the minimum number required to cover the range of observed data. If not NULL, maxStep will be the minimum of the submitted value or the the above. |

`delaysample` |
Number of regular steps at which resampling will begin. The default =1 means resampling will begin immediately. |

`state_favor` |
Vector of weights to favor states when resampling (but not propagating). For instance c(1,3) will favor state 2 weight 3 times as much as state 1 weights for particles. By default, they are equally weighted. |

`nstates` |
Number of behavioral states. For now restricted to a maximum of 2. |

`centroids` |
Matrix with two columns specifying the centroids of regions. |

`truncate_to_map` |
Logical. If TRUE, make sure that coordinate predictions are inside the boundary |

`enforce_full_line_in_map` |
Logical. If TRUE, when conducting truncated sampling ( |

`do_trunc_adjust` |
Logical. If TRUE, adjust particle posterior weights by the fraction of their predictions that are within the truncation boundary. |

`lowvarsample` |
Logical. If TRUE, use low-variance sampling when resampling particles to ensure particles are resampled proportionately to weight. Otherwise there is some sampling variance when drawing random samples. The setting applies to smoothing as well. |

`time_radius` |
Time in seconds to consider for spatial neighbors. |

`spat_radius` |
Radius in meters of (circular) spatial neighborhood. |

`min_num_neibs` |
Minimum number of time and spatial radius observations that need to exist to constitute a neighborhood. |

`interact` |
Logical. If TRUE, simulate interaction parameters of neighborhood. If |

`interact_pars` |
List of interaction priors: |

`neff_sample` |
Number between 0 and 1. If effective sample size < |

`time_dep_trans` |
Logical. If TRUE, state transition matrices are time-dependent meaning that probability depends on the number of steps a shark has remained in the current state. |

`time_dep_trans_init` |
4-element numeric vector of Dirichlet parameters for |

`smoothing` |
Logical. If TRUE, perform smoothing at the end. |

`fix_smoothed_behaviors` |
Logical. If TRUE, when performing smoothing, keep behavior modes fixed for each particle history from what was originally predicted during filtering,
before smoothing. This means the particles will be smoothed backwards with each particle weight at each time point being conditioned on the
behavior predicted in filtering. Thus, the behavioral agreement with, say, the observed or true behaviors is the same for smoothing as for
filtering, since behaviors are not allowed to change. If |

`smooth_parameters` |
Logical. If TRUE, when performing smoothing, resample the parameters theta as well. |

`reg_dt` |
Length in seconds of each regular interval. |

`max_int_wo_obs` |
When simulating, the maximum number of intervals of length |

`resamp_full_hist` |
Logical. If TRUE, resample the full particle history, not just all particle times since the last observation, each time resampling occurs. |

`compare_with_known` |
Logical. If TRUE, provide a known regular-step dataset from which |

`known_trans_prob` |
If |

`known_foraging_prob` |
If |

`known_regular_step_ds` |
If |

`update_eachstep` |
Logical. If TRUE, for regular steps without observations, update the movement parameters based on the simulated movements. If FALSE, parameters are only updated based on the simulated movements when a new observation occurs; this means the simulated movements are drawn using the parameter values learned since the last observation. |

`update_params_for_obs_only` |
Logical. If TRUE, the particle movement parameters are updated based on simulated movement only at intervals with observed locations.
If FALSE, particle movement in intermediate steps that are simulated will be used to update as well.
If TRUE, then |

`output_plot` |
Logical. If TRUE, a set of diagnostic plots will be printed to a file in |

`loc_pred_plot_conf` |
Numeric. Confidence level of ellipse for location prediction error to plot in step-wise diagnostics. |

`pdf_prefix` |
String prefix for output PDF filename, if |

`output_dir` |
Directory for output PDF of diagnostic plots. |

`verbose` |
Integer, one of 0,1,2,3. Control of verbosity of printouts during simulation. 3 means show both printouts and plots; 2 means show plots only; 1 means show printouts only; 0 means show no plots or prinouts. Final plotting will be done regardless. |

Many of the returned values are the same as in `EKF_1d_interp_joint`

. The ones that differ are listed below.

`centroids` |
Input centroids of spatial regions. |

`nregions` |
Number of unique regions, as determined by |

.

`tau_pars` |
Posterior inverse gamma distribution parameters for the turn angle variance. |

`cov_err_hist` |
Overall history of location estimate error draws. |

`param_draws` |
Posterior sampled valued of mean of log-velocity and turn. |

`variance_draws` |
Posterior sampled valued of variance of log-velocity and turn. |

`trans_mean_byregion` |
Posterior estimates of mean behavior switching probabilities from |

`region_counts` |
Array of total number of simulated regular-step intervals that shark begin movement in each spatial region. A proxy for the total amount of time spent in each region. |

`euclidean_estimate_true_from_obs` |
Estimates of true locations by Euclidean and Bezier cubic spline interpolation from observations |

`error_euclidean_estimate_true_from_obs` |
Euclidean error from |

The following inputted parameters are returned:

`area_map` |

See `sim_trajectory_joint`

for a full example of usage.
Video explanation of EKF state-space model by author: https://youtu.be/SgyhRVUn77k

Samuel Ackerman

Ackerman, Samuel. "A Probabilistic Characterization of Shark Movement Using Location Tracking Data." Temple University doctoral thesis, 2018. https://digital.library.temple.edu/digital/collection/p245801coll10/id/499150

Carvalho, Carlos M., Johannes, Michael S., Lopes, Hedibert F., and Nicholas G. Polson. "Particle learning and smoothing." Statistical Science, 2010.

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