pdk: Plotting Probability Dispersal Kernel (pdk) of Fish Movement

In fishmove: Prediction of Fish Movement Parameters

Description

Plotting probability dispersal Kernel (pdk) of fish movement based on multiple regression

Usage

	pdk(fishmove, p = 0.67,...)

Arguments

fishmove	Output from fishmove, containing the movement parameters sigma_stat and sigma_mob.
p	Share of stationary component on the population (0-1). The default value for p is 0.67.
...	do not use.

Details

pdk provides graphs (based on ggplot2) displaying probability density kernels (pdk) for leptokurtic fish dispersal. For each plot the fitted mean as well as the upper and the lower bound (based on confidence or prediction interval, see predict.lm) are displayed.

p is the share of the stationary component in the population resp. 1-p is the share of the mobile component. An average value for p is 0.66 (66% stationary) (Radinger and Wolter, 2013).

The underlying leptokurtic density function is:

F(x) = p * (1/(2*pi*sigma_stat^2)^(1/2))*e^(-(x-mu)^2/(2*pi*sigma_stat^2)) + (1-p) * (1/(2*pi*sigma_mob^2)^(1/2))*e^(-(x-mu)^2/(2*pi*sigma_mob^2))

Author(s)

Johannes Radinger

References

Radinger, J. and Wolter C. (2014) Patterns and predictors of fish dispersal in rivers. Fish and Fisheries. 15:456-473. DOI: http://dx.doi.org/10.1111/faf.12028.

See Also

fishmove, lm, predict.lm, ggplot

Examples

	# Plotting dispersal kernel for selected fish species with time=365 days
	pdk(fishmove(species="Salmo trutta fario",T=365))

Example output

Loading required package: ggplot2
Loading required package: plyr
Loading required package: MASS
Loading required package: boot

fishmove documentation built on May 1, 2019, 9:04 p.m.