Computer software for identifying compositional subpopulations in marine sediment geochemical data using threshold value analysis

PeterA., Knoop; RobertM., Owen

doi:10.1080/10641199409388250

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…These statistical treatments are optimized for use in sedimentary geochemistry but can also be used by other geochemical communities. As detailed below, these approaches have been used successfully for the past 20–30 years [e.g., Leinen and Pisias , ; Leinen , ; Knoop and Owen , ; McMurtry et al ., ; Zhou and Kyte , ; Kyte et al ., ], including a relatively recent series of papers used by members of our research group for sediments in a variety of locations, including the equatorial Pacific Ocean, Cariaco Basin, Arctic Ocean, and the northwest Pacific Ocean [ Ziegler and Murray , ; Ziegler et al ., ; Martinez et al ., ; Scudder et al ., ]. While other multivariate techniques are also useful for other approaches [e.g., mineralogy, Andrews and Eberl , ; general similarity analysis, Borchardt , ; principal components analysis, Vermeesch , ], for specific identification of geochemical sources and their respective contributions these Q‐mode factor analysis, constrained least squares multiple linear regression, and total inversion techniques have proven robust over the years.…”

Section: Factor Analysis Multiple Linear Regression and Total Inversionmentioning

confidence: 99%

Multivariate statistical analysis and partitioning of sedimentary geochemical data sets: General principles and specific MATLAB scripts

Pisias

Murray

Scudder

2013

Geochem Geophys Geosyst

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[1] Multivariate statistical treatments of large data sets in sedimentary geochemical and other fields are rapidly becoming more popular as analytical and computational capabilities expand. Because geochemical data sets present a unique set of conditions (e.g., the closed array), application of generic off-the-shelf applications is not straightforward and can yield misleading results. We present here annotated MATLAB scripts (and specific guidelines for their use) for Q-mode factor analysis, a constrained least squares multiple linear regression technique, and a total inversion protocol, that are based on the well-known approaches taken by Dymond (1981), Leinen and Pisias (1984), Kyte et al. (1993), and their predecessors. Although these techniques have been used by investigators for the past decades, their application has been neither consistent nor transparent, as their code has remained in-house or in formats not commonly used by many of today's researchers (e.g., FORTRAN). In addition to providing the annotated scripts and instructions for use, we discuss general principles to be considered when performing multivariate statistical treatments of large geochemical data sets, provide a brief contextual history of each approach, explain their similarities and differences, and include a sample data set for the user to test their own manipulation of the scripts.Components: 4,422 words.

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Section: Factor Analysis Multiple Linear Regression and Total Inversionmentioning

confidence: 99%

Multivariate statistical analysis and partitioning of sedimentary geochemical data sets: General principles and specific MATLAB scripts

Pisias

Murray

Scudder

2013

Geochem Geophys Geosyst

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“…A method for resolving component modes of multimodal distributions similar to that described here was developed and used in the analysis of ore minerals (Sinclair 1976) and in the geochemical analysis of marine sediments (Knoop & Owen 1994). Although their method of resolving the component modes was slightly different than what we use here, Sinclair (1976) and Knoop & Owen (1994) provided pertinent additional information and perspective into component mode resolution by probability plotting. Thus, we are not developing a new statistical method here.…”

Section: Institutional Abbreviationsmentioning

confidence: 99%

The utility of probability plotting in palaeobiology

Rinehart¹,

Heckert

Lucas

2022

Palaeontology

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We introduce probability plotting for general use in the analysis of biological and palaeontological morphometric data. When applied to morphometric data, this analytical tool can yield palaeoecological, ontogenetic and demographic information. We have adapted and expanded a statistical tool used in reliability engineering, mechanical or electrical lifetime analysis, geochemistry and ore mineral analysis to analyse fossil populations. The technique is explained stepwise in a 'user's manual' format. Its power is to characterize statistical distribution types, to determine whether these distributions are skewed or truncated, and to resolve and quantify the components of mixed distributions, which in palaeontological data potentially represent morphological groupings such as cohorts or sexual dimorphs. The ease of application and simplicity are surprising. A probability plot of growth series data (e.g. skull lengths, femoral lengths, snout-to-vent lengths or other morphometrics) typically shows complex non-normal distribution of the data because of multimodal composition. We show that these component modes, which are generally, but not always normal (i.e. Gaussian), can be resolved, and that they correspond to yearly age groups (cohorts), sexual dimorphs, pre-and post-metamorphic forms, other ontogenetic stages or possibly mixed taxa. When these component size distributions are associated with an age group or sex, several important palaeoecological calculations become possible. These include growth curves, age and sex distributions, sexual dimorphism indices, etc. We demonstrate the biological and palaeontological utility of probability plots with several examples including a Permian wingless insect, an extant unionid bivalve, a Triassic metoposaurid amphibian, an extant lizard, and a Triassic theropod dinosaur.

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“…Quantitative techniques such as Q-mode factor analysis and linear programming (Leinen & Pisias, 1984;Knoop & Owen, 1991, 1994 have been employed successfully in the open marine setting to interpret geochemical information for marine mineral exploration and paleoceanographic reconstructions. The purpose of this investigation is to examine the efficacy Downloaded by [University of California Santa Cruz] at 08:37 25 November 2014 of these techniques in determining trace metal-host phase relationships in fluival sediment (see below) with a history of significant chemical contamination.…”

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confidence: 99%

Geoenvironmental application of statistical techniques used in marine mineral exploration

Owen

Paulsen

Noakes

1998

Marine Georesources & Geotechnology

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Computer software for identifying compositional subpopulations in marine sediment geochemical data using threshold value analysis

Cited by 3 publications

References 12 publications

Multivariate statistical analysis and partitioning of sedimentary geochemical data sets: General principles and specific MATLAB scripts

Multivariate statistical analysis and partitioning of sedimentary geochemical data sets: General principles and specific MATLAB scripts

The utility of probability plotting in palaeobiology

Geoenvironmental application of statistical techniques used in marine mineral exploration

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