PRIMER-e v7 生態學多變數統計軟體-資安軟體/研究分析軟體/心理學軟體/新永資訊有限公司

PRIMER-e v7 生態學多變數統計軟體

PRIMER-e v7 生態學多變數統計軟體

  • PRIMER-e v7 生態學多變數統計軟體
  • 編號
  • 類別
    統計分析軟體
  • 介紹
    PRIMER has become the global standard in software tools for nonparametric​ multivariate analysis. Classical techniques falter when there are too many​ variables, or when assumptions of multivariate normality do not hold.​
  • 價格

PRIMER version 7

PRIMER v7 provides a wide range of univariate, graphical and multivariate routines for analysing arrays of species-by-samples data from community ecology.​

Easy manipulation of data and results-The PRIMER Workspace (*.pwk)
  • • Work on multiple data-sets, graphics and output files simultaneously within a single interactive & intuitive graphical Windows environment.​
  • • Keep track of all your work easily using the tree-like navigation pane.​
  • • Input/output data from Excel, .csv, .txt or 3-column format.​
  • • View and change the colours, fonts, look & feel of your plots and graphics on-screen.​​
  • • Copy/print/export results to .jpg, .emf, .tif, .gif, .png, .bmp or .rtf files.
  • • Power and flexibility in specifying analyses.
  • • Identify important subsets of samples or variables.​​
  • • Define group structures for tests and displays.​​
  • • Handle relatively large data sets (subject only to available Windows memory).​
  • • Merge or split data on specified criteria.​​
  • • Unleash the time-saving power of in-built Wizards for matrix displays and core analyses.

Multivariate analysis is more than a stack of univariate parts.
Achieve a holistic view of your high-dimensional data by exploring and revealing its salient structures.


Ordination
  • • Ordination techniques for visualising data, including principal components analysis (PCA), plus non-metric, metric or threshold-metric multi-dimensional scaling (nMDS, mMDS, tmMDS).​
  • • Visualise high-dimensional data in 2d or 3d. Customise text, colours and symbols. Add essential information with overlays, such as clusters, trajectories, bubbles, images, vectors, or minimum spanning trees.​
  • • Use bootstrapping to show confidence regions on MDS plots. Spin, expand, rotate, animate, save and share your insightful images.​

Clustering
  • • Perform hierarchical clustering into sample (or variable/species) groups, with single, complete, group-average or flexible-beta linkage options.​​
  • • Create cophenetic distance matrices and plot dendrograms. Customise, rotate, collapse, zoom in, or print them out over multiple pages to capture all of the finer details.​​
  • • Similarity profiles (SIMPROF) and associated permutation tests identify the coherence of groups of samples or species.​​
  • • Divisive clustering methods can be unconstrained (UNCTREE), or constrained by environmental (or other) variables (LINKTREE), while krCLUSTER does a non-parametric version of k-means clustering.​

Shade plots and Heat maps​
  • • Display actual values in 2d or 3d with grey-scale or colour spectra of choice.​
  • • Salient patterns are uncovered with ease.​
  • • Samples and variables can be sorted according to various criteria, including gradients, clustering constraints, or groupings.​​​
  • • Labels and symbols are easily modified and customised.​

Non-parametric tests​
  • • Permutation-based hypothesis testing using analysis of similarities (ANOSIM), which tests for differences between groups of multivariate samples from different times, locations, experimental treatments, etc. PRIMER’s algorithms now support most three-way designs for these fully non-parametric tests.​
  • • Identify species primarily providing discrimination between two groups of samples (SIMPER).​ ​
  • • Test for seriation and other Mantel-type tests for sequential data in time or space.​​​
  • • Relate matrices to one another or identify subsets of variables which together produce a ‘BEST’ Mantel-type match to a given resemblance matrix. ​
  • • Permutation tests include variable-selection steps to ensure rigorous statistical inference.​​​​

Biodiversity analyses​
  • • Use taxonomic, functional and/or phylogenetic information to inform biodiversity assessments. Test against master lists using sub-sampling algorithms.​
  • • Calculate a multitude of classic and novel diversity indices or summary statistics of individual variables, with aggregation tools for analysing data at higher taxonomic levels.​​
  • • Access a wide choice of flexible plotting functions for individual variables: line, histogram, scatter, bar, box, means, surface or Draftsman plots.​​​​
  • • Ecology-specific tools are at your fingertips, such as dominance plots, geometric class plots, or abundance biomass curves (ABC).​​

PRIMER-e 生態學多變數統計軟體

Thousands of researchers globally trust PRIMER for ingenuity and clarity in multivariate analysis.Analyse ecological data, or any other data, that would benefit from a safe non-parametric approach​.

Data are typically of abundance, biomass, % area (or line) cover, presence/absence etc. and arise in biological monitoring of environmental impacts and more​ fundamental ecological studies. Also catered for are matrices of physical values and chemical concentrations, which are analysed in their own right or in​ parallel with biological assemblage data, e.g., to examine co-variation of biotic changes with physico-chemical conditions.​

Though the analysis requirements for biological assemblage data are a principle focus, the package is equally applicable (and increasingly being applied)​ to other data structures which are multivariate or can be treated as such.​​

The unifying feature is that all datasets can be reduced to an appropriate triangular matrix representing the resemblance of every pair of samples in terms of​ their assemblages, suites of biomarkers, particle size distributions, shape of growth curves, etc. Clustering and ordination techniques are then able to​ display the relationships among samples, and permutation tests are used to test hypotheses.​
 

PRIMER-e v7 生態學多變數統計軟體

PRIMER has become the global standard in software tools for nonparametric​ multivariate analysis. Classical techniques falter when there are too many​ variables, or when assumptions of multivariate normality do not hold.​

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