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PRC - Principal Response Curves (PRC) For Displaying Multivariate Main Effects And Interactions

PRC is a small \code{vegan}- and \code{LMMsolver}-based package to create and display Principal Response Curves (PRC) and smooth Principal Response Curves (PRC). PRC is a special case of Redundancy Analysis (rda) for multivariate responses in designed experiments and repeated measurement designs. PRC expresses ordination scores as deviation from a reference, usually to visualize multivariate A-dependent effects (e.g time-dependent effects) of a treatment B in (un)balanced factorial experiments. The main user functions are \code{\link{doPRC}}, \code{\link{plotPRC}}, \code{\link{plotPRC2d}} and \code{\link{smoothPRC}}. The easiest example is the code demo \code{PRC_pyrifos}.

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3.32 score 21 scripts

douconca - Double Constrained Correspondence Analysis for Trait-Environment Analysis in Ecology

Double constrained correspondence analysis (dc-CA) analyzes (multi-)trait (multi-)environment ecological data by using library vegan and native R code. Throughout the two step algorithm of ter Braak et al. (2018) is used. This algorithm combines and extends community- (sample-) and species-level analyses, i.e. the usual community weighted means (CWM)-based regression analysis and the species-level analysis of species-niche centroids (SNC)-based regression analysis. The two steps use canonical correspondence analysis to regress the abundance data on to the traits and (weighted) redundancy analysis to regress the CWM of the orthonormalized traits on to the environmental predictors. The function dc_CA has an option to divide the abundance data of a site by the site total, giving equal site weights. This division has the advantage that the multivariate analysis corresponds with an unweighted (multi-trait) community-level analysis, instead of being weighted. The first step of the algorithm uses vegan::cca. The second step uses douconca::wrda but vegan::rda if the site weights are equal. This version has a predict function. For details see ter Braak et al. 2018 <doi:10.1007/s10651-017-0395-x>.

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1.70 score 1 stars 7 scripts 209 downloads

Gendis2unmix - Calculates a generalized discriminant function to unmix two classes, typically sexes of birds

The goal of Gendis2unmix is to sex birds from a population on the basis of several measurements. The key feature is that the birds from different populations may differ in size but that within populations females are smaller than males (or reversely). The predict function for a set of unsexed birds from a new population therefore estimates a new cutoff value which thus depends on the sizes of the birds in the new population. In the training phase, a generalized discriminant function (GDF) is calculated from a birds of known sex of different populations, in which the algorithm uses a common within-covariance matrix across populations and sexes. In the prediction phase Gendis2unmix then applies the GDF to measurements of individuals of unknow sex or class. The cutoff value is determined by unmixing the distribution in terms of two normal distributions with unequal means and variances using an EM algorithm. The parametric approach taken in Gendis2unmix make it suitable for small number of samples in both the training and prediction phase (say 20-100 per sex/population).

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1.70 score

TraitEnvMLMWA - Weighted Averaging (WA) and Multi-Level Methods (MLM) for Trait-Environment Association in Functional and Community Ecology

This package grew out of the paper New robust weighted averaging- and model-based methods for assessing trait-environment relationships (Methods in Ecology and Evolution). The model-based methods are GLMMs, here called multi-level models (MLM), of which the novel MLM3 outperforms all existing methods. MLM3 has up to 30% more power, but the WA-methods are 500 times faster than MLM3. The main function for WA-methods is CWMSNC_regressions. The main functions for MLM3 are make_obj_for_traitenv, expand4glmm and MLM3_p_max.

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1.70 score

demc2 - demc2 implements adaptive MCMC on real parameter spaces via Differential Evolution Markov Chain

The package implements both the origal Markovian demc version of ter Braak (2006) and the egodic demc-zc version of ter Braak & Vrugt (2008) using learning from the past and the snooker update. Convergence can be checked with coda. The demc package allows restarts.

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1.70 score 1 stars