DTDA: An R Package to Analyze Randomly Truncated Data

In this paper, the R package DTDA for analyzing truncated data is described. This package contains tools for performing three different but related algorithms to compute the nonparametric maximum likelihood estimator of the survival function in the presence of random truncation. More precisely, the package implements the algorithms proposed by Efron and Petrosian (1999) and Shen (2008), for analyzing randomly one-sided and two-sided (i.e., doubly) truncated data. These algorithms and some recent extensions are briefly reviewed. Two real data sets are used to show how DTDA package works in practice.

Does functional soil microbial diversity contribute to explain within-site plant beta-diversity in an alpine grassland and a <i>dehesa</i> meadow in Spain?

Questions: Once that the effects of hydrological and chemical soil properties have been accounted for, does soil microbial diversity contribute to explain change in plant community structure (i.e. within-site beta-diversity)? If so, at which spatial scale does microbial diversity operate? Location: La Mina in Moscosa Farm, Salamanca, western Spain (dehesa community) and Laguna Larga in the Urbión Peaks, Soria, central-northern Spain (alpine grassland). Methods: The abundance of vascular plant species, soil gram-negative microbial functional types and soil chemical properties (pH, available phosphorus, and extractable cations) were sampled at both sites, for which hydrological models were available. Redundancy analysis (RDA) was used to partition variation in plant community structure into hydrological, chemical and microbial components. Spatial filters, arranged in scalograms, were used to test for the spatial scales at which plant community structure change. Results: In the case of the dehesa the diversity of soil gram-negative microbes, weakly driven by soil pH, contributed to a small extent (adj-R2 = 2%) and at a relative medium spatial scale to explain change in plant community structure. The abundance of a few dehesa species, both annual (Trifolium dubium, Vulpia bromoides) and perennial (Poa bulbosa, Festuca ampla), was associated with either increasing or decreasing soil microbial diversity. In the alpine meadow the contribution was negligible. Conclusions: Microbial diversity can drive community structure, though in the hierarchy of environmental factors structuring communities it appears to rank lower than other soil factors. Still, microbial diversity appears to promote or restrain individual plant species. This paper aims to encourage future studies to use more comprehensive and insightful techniques to assess microbial diversity and to combine this with statistical approaches such as the one used here

NPCirc: An R Package for Nonparametric Circular Methods

Nonparametric density and regression estimation methods for circular data are included in the R package NPCirc. Specifically, a circular kernel density estimation procedure is provided, jointly with different alternatives for choosing the smoothing parameter. In the regression setting, nonparametric estimation for circular-linear, circular-circular and linear-circular data is also possible via the adaptation of the classical Nadaraya-Watson and local linear estimators. In order to assess the significance of the features observed in the smooth curves, both for density and regression with a circular covariate and a linear response, a SiZer technique is developed for circular data, namely CircSiZer. Some data examples are also included in the package, jointly with a routine that allows generating mixtures of different circular distributions

A general framework for circular local likelihood regression

This paper presents a general framework for the estimation of regression models with circular covariates, where the conditional distribution of the response given the covariate can be specified through a parametric model. The estimation of a conditional characteristic is carried out nonparametrically, by maximizing the circular local likelihood, and the estimator is shown to be asymptotically normal. The problem of selecting the smoothing parameter is also addressed, as well as bias and variance computation. The performance of the estimation method in practice is studied through an extensive simulation study, where we cover the cases of Gaussian, Bernoulli, Poisson and Gamma distributed responses. The generality of our approach is illustrated with several real-data examples from different fields.Comment: 47 pages, 19 figure

multimode: An R Package for Mode Assessment

In several applied fields, multimodality assessment is a crucial task as a previous exploratory tool or for determining the suitability of certain distributions. The goal of this paper is to present the utilities of the R package multimode, which collects different exploratory and testing non-parametric approaches for determining the number of modes and their estimated location. Specifically, some graphical tools (SiZer map, mode tree or mode forest) are provided, allowing for the identification of mode patterns, based on the kernel density estimation. Several formal testing procedures for determining the number of modes are described in this paper and implemented in the multimode package, including methods based on the ideas of the critical bandwidth, the excess mass or using a combination of both. This package also includes a function for estimating the modes locations and different classical data examples that have been considered in mode testing literature