Para: Curve parameters of eHOF models

Description

Derive common shape parameters from the different model types. Calculate a set of parameters (see values below) from eHOF models.

Usage

## S3 method for class 'HOF':
Para(resp, model, newdata = NULL, ...)
## S3 method for class 'HOF.list':
Para(resp, ...)

Arguments

Value

speciesName or abbreviat of the species.abund sumAbundance sum, i.e. sum of all response values divided by M.rangeRange of x values.modelModel type, if not specified the result of pick.model.paraModel parameters (a to d).MMaximum response value, specified in the HOF function call.miniLocation of the minimum, i.e. the gradient value, where the response is lowest, for model VI and VII the lowest response between the two optima.pessLowest estimated response value.topHighest estimated response value(s).optLocation of the optimum, i.e. the gradient value, where the species response is highest. NA for model I and an optimum interval for model type III.expectExpectancy value, i.e. average x value under the model curve).max slopeHighest slope, i.e. maximum of the first derivation of the curve.centralBorderFollowing Heegard, the central borders are calculated as the gradient values, where the response reaches "exp(-1/2)" of the top.outerBorderFollowing Heegard, the outer borders of the species niche are calculated as the gradient values, where the response reaches exp(-2) of the top.raw meanAverage of measured x values.

encoding

UTF-8

Details

If you want to obtain the model slope, you can use the undocumented function Para.deriv(resp, p=modelparameter, newdata=x, type='slope').

For models VI and VII Para will give you two expectancy values for the ranges left and right of the pessimum between the model optima. If you want to have the overall expectancy value, calculate something like: gradient <- seq(min(Para(resp)$range), max(Para(resp)$range), length.out=10000) weighted.mean(gradient, predict(resp, newdata=gradient))

References

Heegard, E. (2002) The outer border and central border for species-environmental relationships estimated by non-parametric generalised additive models. Ecological Modelling, 157, 131-139. Damgaard, C. (2006) Modelling ecological presence-absence data along an environmental gradient: threshold levels of the environment. Environ Ecol Stat 13:229-236.