w2n: Scaling function: working to natural parameters In TheoMichelot/moveHMM: Animal Movement Modelling using Hidden Markov Models

Description

Scales each parameter from the set of real numbers, back to its natural interval. Used during the optimization of the log-likelihood.

Usage

 `1` ```w2n(wpar, bounds, parSize, nbStates, nbCovs, estAngleMean, stationary) ```

Arguments

 `wpar` Vector of state-dependent distributions unconstrained parameters. `bounds` Matrix with 2 columns and as many rows as there are elements in `wpar`. Each row contains the lower and upper bound for the correponding parameter. `parSize` Vector of two values: number of parameters of the step length distribution, number of parameters of the turning angle distribution. `nbStates` The number of states of the HMM. `nbCovs` The number of covariates. `estAngleMean` `TRUE` if the angle mean is estimated, `FALSE` otherwise. `stationary` `FALSE` if there are covariates. If TRUE, the initial distribution is considered equal to the stationary distribution. Default: `FALSE`.

Value

A list of:

 `stepPar` Matrix of natural parameters of the step length distribution `anglePar` Matrix of natural parameters of the turning angle distribution `beta` Matrix of regression coefficients of the transition probabilities `delta` Initial distribution

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```## Not run: nbStates <- 3 nbCovs <- 2 par <- c(0.001,0.999,0.5,0.001,1500.3,7.1) parSize <- c(1,1) bounds <- matrix(c(0,1,0,1,0,1, 0,Inf,0,Inf,0,Inf), byrow=TRUE,ncol=2) beta <- matrix(rnorm(18),ncol=6,nrow=3) delta <- c(0.6,0.3,0.1) wpar <- n2w(par,bounds,beta,delta,nbStates,FALSE) print(w2n(wpar,bounds,parSize,nbStates,nbCovs,estAngleMean=FALSE,stationary=FALSE)) ## End(Not run) ```

TheoMichelot/moveHMM documentation built on Sept. 28, 2018, 11:06 a.m.