Fits a functional movement model to telemetry data following Buderman et al., 2015.
mcmc.fmove(xy,t,fdabasis,tpred=t,QQ="CAR2",a=1,b=1,r=1,q=1,
n.mcmc=100,num.paths.save=10,sigma.fixed=NA)
Numeric vector of the values of sigma^2 at each mcmc iteration
Numeric vector of the values of tau^2 at each mcmc iteration
A list of length num.paths.save, with each item itself being a list with two entries:
xy = a matrix with rows corresponding to x,y locations of the quasi-continuous path imputation
t = a vector with entries corresponding to the times at which the quasi-continuous path was imputed
A two-column matrix with each row corresponding to the x,y locations of a telemetry location.
A numeric vector of length = nrow(xy), with the i-th entry corresponding to the time of the i-th telemetry location in xy.
A "basisfd" object, typically resulting from a call to "create.bspline.basis" in the fda package. Other basis functions can be used.
Numeric vector of times to impute the quasi-continuous path.
The precision matrix of the fda basis coefficients. This can either be a string, taking on values of "CAR1" or "CAR2", or can be a user specified matrix (or sparse matrix using the Matrix package) of dimension equal to the number of basis functions in fdabasis. Defaults to "CAR2". "CAR1" will result in less-smooth paths.
The shape parameter of the inverse gamma prior on the observation variance.
The scale parameter of the inverse gamma prior on the observation variance.
The shape parameter of the inverse gamma prior on the partial sill parameter of the spline basis coefficients.
The scale parameter of the inverse gamma prior on the partial sill parameter of the spline basis coefficients.
Number of mcmc iterations to run.
Number of quasi-continuous path realizations to save. Defaults to 10.
Numeric value (or the default NA). If NA, then the observation variance sigma^2 is estimated using MCMC. If a numeric value, this is the fixed standard deviation of the observation error.
Ephraim M. Hanks
Fits the functional movement model of Buderman et al., 2015, and outputs quasi-continuous paths that stochastically interpolate between telemetry locations. The model fit is as follows (written out for 1-D):
y_t = observed location at time t
z_t = Sum_k beta_k*phi_k(t) = true location at time t, expressed using a linear combination of spline basis functions phi_k(t).
y_t ~ N( z_t , sigma^2 )
beta ~ N( 0 , tau^2 * QQ^(-1) )
sigma^2 ~ IG(a,b)
tau^2 ~ IG(r,q)
Buderman, F.E.; Hooten, M. B.; Ivan, J. S. and Shenk, T. M. A functional model for characterizing long-distance movement behavior. Methods in Ecology and Evolution, 2016, 7, 264-273.
## For example code, do
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## > help(ctmcMove)
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