dispersal.kernel: Dispersal kernels for dispersal data

In apferreira/dispfit: Fit distributions to species dispersal data

Dispersal kernels for dispersal data

Description

Fits several pre-defined distributions to dispersal or movement data, computing several estimators: AIC, AICc, BIC, Chi-squared, and Kolgomorov-Smirnov. It also estimates the parameter(s) value(s) and CI of each distribution, as well as its Mean, Variance, Skewness, and Kurtosis.

Usage

dispersal.kernel(
  data,
  distribution = "all",
  order.by = "AICc",
  confidence.level = 0.95
)

Arguments

data	A numeric vector of distances.
distribution	A character string naming the distributions to fit. By default “all” distributions are selected, but they may be selected manually by name. See Details.
order.by	A character string giving the estimator by which the distributions are to be ordered in the output table. The default is “AICc”, but one can also choose “AIC” or “BIC”.
confidence.level	A numeric value varying from 0 to 1, defining the confidence interval to be used when calculating confidence envelopes.

Details

This function fits one or more dispersal kernels with 1-2 parameters, by estimating the distribution of kernel parameters (θ∈R) maximizing the likelihood function.

Value

Returns a list with all maximum likelihood calculations for each selected distribution. Furthermore, two summary tables may be accessed.

distribution.selection	A table listing the fitted distributions.
distribution.parameters	A table with the estimated parameters and moments for each fitted distribution.

See Also

plot.dispfit

Examples

## simulate data from lognormal distribution
set.seed(1111)
simulated.data <- rlnorm(200, meanlog = 5, sdlog = 1)

## run dispersal.kernel function
fit <- dispersal.kernel(simulated.data)

## display table
fit$distribution.selection

apferreira/dispfit documentation built on April 16, 2023, 4:18 a.m.