fitNMDS-package: The fitNMDS Package for Evaluating Nonmetric Multidimensional...
In phytomosaic/fitNMDS: Fitting Resampled and Reciprocal NMDS Models
Description
Measures of agreement for bootstrapped and reciprocal
NMDS.
Details
Combining two different datasets into one nonmetric
multidimensional scaling (NMDS) model can be risky if they each
cover different attribute spaces (e.g., different species pools
in ecology). Therefore, comparing two datasets requires
estimating internal agreement (sampling variability) relative
to external agreement (exchangeability).
Resampled NMDS with resamp_nmds estimates internal
agreement (sampling variability) of one candidate dataset.
The premise of this is to resample dissimilarities (either
through jackknife or bootstrap resampling), perform NMDS
ordination on each resampled replicate, then determine
collective agreement across all NMDS solutions. Other uses of
resampled NMDS may include estimating confidence regions for
ordination site scores, or testing the stability of species
positions along ordination axes.
Reciprocal NMDS with recip_nmds estimates external
agreement (exchangeability) of two candidate datasets. The
premise of reciprocal NMDS is to calibrate each of two models
with each respective dataset, mutually exchanging datasets
among the alternative ordination models, then determining
measures of how well each dataset fit the alternative model.
This is really just a special case of 2-fold cross-validation
as applied to NMDS. The primary use of reciprocal NMDS is to
test the null hypothesis that two multivariate datasets are
exchangeable.
phytomosaic/fitNMDS documentation built on May 17, 2019, 8:19 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description
Measures of agreement for bootstrapped and reciprocal NMDS.
Details
Combining two different datasets into one nonmetric multidimensional scaling (NMDS) model can be risky if they each cover different attribute spaces (e.g., different species pools in ecology). Therefore, comparing two datasets requires estimating internal agreement (sampling variability) relative to external agreement (exchangeability).
Resampled NMDS with resamp_nmds estimates internal
agreement (sampling variability) of one candidate dataset.
The premise of this is to resample dissimilarities (either
through jackknife or bootstrap resampling), perform NMDS
ordination on each resampled replicate, then determine
collective agreement across all NMDS solutions. Other uses of
resampled NMDS may include estimating confidence regions for
ordination site scores, or testing the stability of species
positions along ordination axes.
Reciprocal NMDS with recip_nmds estimates external
agreement (exchangeability) of two candidate datasets. The
premise of reciprocal NMDS is to calibrate each of two models
with each respective dataset, mutually exchanging datasets
among the alternative ordination models, then determining
measures of how well each dataset fit the alternative model.
This is really just a special case of 2-fold cross-validation
as applied to NMDS. The primary use of reciprocal NMDS is to
test the null hypothesis that two multivariate datasets are
exchangeable.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.