intensity.RdThis function plots the empirical and theoretical intensity functions with respect to a covariate of interest.
intensity(data,UD,RSF,R=list(),variable=NULL,empirical=FALSE,level=0.95,ticks=TRUE,
smooth=TRUE,interpolate=TRUE,...)A telemetry object.
A UD object generated by akde from the same telemetry object as data. If weights were optimized in akde, then they will be adopted by intensity.
An iRSF model-fit object from rsf.fit or rsf.select.
A named list of rasters or time-varying raster stacks [NOT TESTED] to fit Poisson regression coefficients to (under a log link).
Variable of interest from names(R).
Plot an empirical estimate of \(\log\lambda\) [IN DEVELOPMENT].
Confidence level for intensity function estimates.
Demark used resource values atop the plot.
Apply location-error smoothing to the tracking data before regression.
Whether or not to interpolate raster values during extraction.
Arguments passed to plot.
With resepct to the Poisson point process likelihood \(L(\lambda)=\frac{\lambda(x,y)}{\iint \lambda(x',y') \, dx' dy'}\), the formula object of a ctmm iRSF model corresponds to the covariate dependence of \(\log(\lambda)\), which is typically of the form \(\boldsymbol{\beta} \cdot \mathbf{R}\). intensity plots both empirical (black) and theoretical (red) estimates of the log-intensity (or log-selection) function \(\log(\lambda)\) as a function of the covariate variable, which provides a visualization of what the true formula looks like and how the fitted model compares. The empirical estimate is semi-parametric, in that it assumes that RSF is correct for all variables other than variable.
Only relative differences in \(\log(\lambda)\) are meaningful.