Errata and Notes for “Software for Data Analysis: Programming with R”

Chambers, John M.

doi:10.1007/978-0-387-75936-4_14

Cited by 145 publications

(176 citation statements)

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“…Fragments smaller than 200 bp and larger than 1,200 bp were excluded from the profiles. Profiles of ribotype abundances (based on peak heights) were created by using the program RiboSort (55) within the statistical package R, version 2.6.0 (13). Fragment sizes that differed by less than 0.5 bp were considered to be identical ribotypes.…”

Section: Methodsmentioning

confidence: 99%

Soil Microbial Community Successional Patterns during Forest Ecosystem Restoration

Banning

Gleeson

Grigg

et al. 2011

Appl Environ Microbiol

221

147

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Soil microbial community characterization is increasingly being used to determine the responses of soils to stress and disturbances and to assess ecosystem sustainability. However, there is little experimental evidence to indicate that predictable patterns in microbial community structure or composition occur during secondary succession or ecosystem restoration. This study utilized a chronosequence of developing jarrah (Eucalyptus marginata) forest ecosystems, rehabilitated after bauxite mining (up to 18 years old), to examine changes in soil bacterial and fungal community structures (by automated ribosomal intergenic spacer analysis [ARISA]) and changes in specific soil bacterial phyla by 16S rRNA gene microarray analysis. This study demonstrated that mining in these ecosystems significantly altered soil bacterial and fungal community structures. The hypothesis that the soil microbial community structures would become more similar to those of the surrounding nonmined forest with rehabilitation age was broadly supported by shifts in the bacterial but not the fungal community. Microarray analysis enabled the identification of clear successional trends in the bacterial community at the phylum level and supported the finding of an increase in similarity to nonmined forest soil with rehabilitation age. Changes in soil microbial community structure were significantly related to the size of the microbial biomass as well as numerous edaphic variables (including pH and C, N, and P nutrient concentrations). These findings suggest that soil bacterial community dynamics follow a pattern in developing ecosystems that may be predictable and can be conceptualized as providing an integrated assessment of numerous edaphic variables.

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Section: Methodsmentioning

confidence: 99%

Soil Microbial Community Successional Patterns during Forest Ecosystem Restoration

Banning

Gleeson

Grigg

et al. 2011

Appl Environ Microbiol

221

147

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“…Fourth, based on the RMSDs values, the structures were classified into several groups by performing cluster analysis with the nearest neighboring method using the "hclust" function of R software. 26,27) Finally, amino acid mutation, space group of the protein crystal, category of substrate or inhibitor, and crystallization condition were surveyed for every structure of the respective clusters. The nearest neighboring method produces a dendrogram of crystal structures in a hierarchical manner.…”

Section: Methodsmentioning

confidence: 99%

A Cluster Analysis on the Structural Diversity of Protein Crystals, Exemplified by Human Immunodeficiency Virus Type 1 Protease

Fudo

Neya

et al. 2014

CHEMICAL & PHARMACEUTICAL BULLETIN

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Information on many protein crystal structures has recently become available due to developments in crystallographic techniques. Even for a single kind of protein, several and sometimes many crystal structures are available. Human immunodeficiency virus type 1 (HIV-1) protease is one of the most extensively studied viral proteins, and about six hundred crystal structures have been determined. In this work, we examined the structural diversity of HIV-1 protease, classifying crystal structures into several groups from the viewpoint of similarity in atom geometry. Using 499 crystal structures downloaded from the Protein Data Bank (PDB), cluster analysis was applied to the whole body of HIV-1 protease and also to a limited number of residues at the binding pocket. As a consequence of clustering with regard to the whole body, 499 crystal structures were separated into 6 groups. It was found that a major factor for this separation is the space group of the crystals and that the space group strongly depends on the agents used in the protein crystallization. Amino acid mutation is a minor factor for separation in clustering. In cluster analysis for a limited number of residues at the binding pocket, crystal structures were not distinctly separated, and no clear factor linked to the separation was clarified. The results suggest that amino acid mutations have little effect on the coordinates of the main-chain atoms of HIV-1 protease. Hence, the changes in drug efficacy or substrate fitness caused by mutations are mainly due to the physicochemical features of amino acid side chains.Key words clustering analysis; crystal structure; amino acid mutation; drug resistance; space group Due to the progress in techniques for protein crystallization and in software for model building, crystal structures of many proteins have been elucidated. The reliability of solved protein structures has also increased.1) The development of a high-energy X-ray source has also been important for advancing crystallographic studies.2) Due to the progress in crystallographic technology, the availability of crystal structures in good quality has enabled us to examine structural differences in proteins in detail. Even for a single kind of protein, an amino acid mutation will cause the difference in its activity. Mutagenesis is one of the best approaches for clarifying the relationship between the protein activity and the mutated amino acid residue. Since protein activity has a close relation with its structure, 3,4) it is of great interest in molecular biology that the influence of amino acid mutation induces the change in protein structure.Apart from artificial mutagenesis, amino acid mutation ceaselessly occurs in the process of evolution. There is a common consensus that the accumulation of amino acid mutations generates a genetic diversity and that the diversity is observed as a difference in protein function in phenotype. 5,6) In general, the mutation rate of viral proteins is much higher than that of protein in eukaryotes. Variants of a virus som...

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“…The package kequate can use 'glm' objects created using the R function glm() (from package stats; R Core Team 2013) as input arguments and estimate the equating function and associated standard errors directly from the information contained therein. The S4 system of classes and methods (Chambers 2008), a more formal and rigorous way of handling objects in R, is used in package kequate, providing methods for the generic functions plot() and summary() for a number of newly defined classes. The main function of the package is kequate(), which enables the equating of two parallel tests using the previously defined equating designs.…”

Section: Kequate For Rmentioning

confidence: 99%

Performing the Kernel Method of Test Equating with the Packagekequate

Andersson¹,

Bränberg²,

Wiberg³

2013

J. Stat. Soft.

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In standardized testing it is important to equate tests in order to ensure that the test takers, regardless of the test version given, obtain a fair test. Recently, the kernel method of test equating, which is a conjoint framework of test equating, has gained popularity. The kernel method of test equating includes five steps: (1) pre-smoothing, (2) estimation of the score probabilities, (3) continuization, (4) equating, and (5) computing the standard error of equating and the standard error of equating difference. Here, an implementation has been made for six different equating designs: equivalent groups, single group, counter balanced, non-equivalent groups with anchor test using either chain equating or poststratification equating, and non-equivalent groups using covariates. An R package for the kernel method of test equating called kequate is presented. Included in the package are also diagnostic tools aiding in the search for a proper log-linear model in the pre-smoothing step for use in conjunction with the R function glm.

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Errata and Notes for “Software for Data Analysis: Programming with R”

Cited by 145 publications

References 0 publications

Soil Microbial Community Successional Patterns during Forest Ecosystem Restoration

Soil Microbial Community Successional Patterns during Forest Ecosystem Restoration

A Cluster Analysis on the Structural Diversity of Protein Crystals, Exemplified by Human Immunodeficiency Virus Type 1 Protease

Performing the Kernel Method of Test Equating with the Packagekequate

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