eHOF (version 1.0)

HOF: Fits hierarchical logistic regression models

Description

Fits six hierarchical logistic regression models and selects the most appropriate model by information criteria and a bootstrap approach to guarantee model stability. The first five shapes are known as Huisman-Olff-Fresco (HOF) models in ecology (Huisman et al. 1993). The package provides an additional bimodal shape.

Usage

## S3 method for class 'default':
HOF(occ, grad, M = max(occ), y.name, family = binomial, lim = 100, bootstrap = 100, test = c('AICc', 'BIC', 'AIC','Dev'), ...)
## S3 method for class 'data.frame':
HOF(veg, grad, M, freq.limit = 10, ...)
## S3 method for class 'HOF':
pick.model(object, level = 0.95, test = c("AICc", "BIC", "AIC", "Dev"), modeltypes, penal = 'df', gam = FALSE, selectMethod = c('bootselect', 'IC.weight', 'raw'), quiet = FALSE, ...)
## S3 method for class 'HOF':
fitted(object, model, ...)
## S3 method for class 'HOF':
predict(object, model, newdata, ...)

Value

Function HOF.fit returns an object of class "HOF" which contains the parameters of the call, the fitting results for every model type and a vector of chosen model types from bootstrapping.

encoding

UTF-8

Details

The minimization function changed compared to package gravy (Oksanen 2002) from nlm to nlminb to be able to set a limit for estimated parameters (default= -100 to 100). The old models III and V have been often too sharp. lim=Inf will produce results similar to gravy.

Function pick.model finds the most adequate modeltype according to the chosen Information Criterion (AICc is default).

Function fitted returns the fitted values for the used gradient, and predict for any values in newdata. To improve and check model stability a bootstrapping mechanism is implemented in function HOF. If the initially chosen model type is different from the most frequent one, the latter will be chosen by default.

References

Jansen, F. & Oksanen, J. (in prep) What shape are species responses along ecological gradients? - Huisman-Olf-Fresco models revisited

Oksanen, J. & Minchin, P.R. (2002). Continum theory revisited: what shape are species responses along ecological gradients? Ecological Modelling 157, 119-129.

Huisman, J., Olff, H. & Fresco, L.F.M. (1993). A hierarchical set of models for species response analysis. Journal of Vegetation Science 4, 37-46.

See Also

plot.HOF provides advanced plotting possibilities for HOF models. Para derives model parameters like optimum, niche (width), slope etc.

Examples

data(acre)
sel <- c('MATRREC', 'RUMEACT', 'SILENOC', 'APHAARV', 'MYOSARV', 'DESUSOP', 'ARTE#VU')
mo <- HOF(acre[,match(sel, names(acre))], acre.env$PH_KCL, M=1, bootstrap=NULL)
mo