Package: QFASA 1.2.1

Connie Stewart

QFASA: Quantitative Fatty Acid Signature Analysis

Accurate estimates of the diets of predators are required in many areas of ecology, but for many species current methods are imprecise, limited to the last meal, and often biased. The diversity of fatty acids and their patterns in organisms, coupled with the narrow limitations on their biosynthesis, properties of digestion in monogastric animals, and the prevalence of large storage reservoirs of lipid in many predators, led to the development of quantitative fatty acid signature analysis (QFASA) to study predator diets.

Authors:Connie Stewart [cre, aut, cph], Sara Iverson [aut, cph], Chris Field [aut], Don Bowen [aut], Wade Blanchard [aut], Shelley Lang [aut], Justin Kamerman [aut], Hongchang Bao [ctb], Holly Steeves [aut], Jennifer McNichol [aut], Tyler Rideout [aut]

QFASA_1.2.1.tar.gz
QFASA_1.2.1.zip(r-4.5)QFASA_1.2.1.zip(r-4.4)QFASA_1.2.1.zip(r-4.3)
QFASA_1.2.1.tgz(r-4.4-x86_64)QFASA_1.2.1.tgz(r-4.4-arm64)QFASA_1.2.1.tgz(r-4.3-x86_64)QFASA_1.2.1.tgz(r-4.3-arm64)
QFASA_1.2.1.tar.gz(r-4.5-noble)QFASA_1.2.1.tar.gz(r-4.4-noble)
QFASA.pdf |QFASA.html✨
QFASA/json (API)
NEWS

# Install 'QFASA' in R:
install.packages('QFASA', repos = c('https://cstewartgh.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/cstewartgh/qfasa/issues

Datasets:
  • CC - Fatty acid calibration coefficients.
  • FAset - List of fatty acids used in sample prey and predator data sets, 'preyFAs' and 'predatorFAs' respectively.
  • bal.diet.data - Sample example of balanced repeatability diet estimates data with only two repeated measurements per predator.
  • predatorFAs - Predator fatty acid signatures. Each predator signature is a row with fatty acid proportions in columns.
  • preyFAs - Prey fatty acid signatures. Each prey signature is a row with fatty acid proportions in columns.
  • unbal.diet.data - Sample example of unbalanced repeatability diet estimates data with a max of two repeated measurements per predator.

On CRAN:

6.28 score 1 stars 17 scripts 7.0k downloads 10 mentions 30 exports 99 dependencies

Last updated 3 months agofrom:fe5a5bbccf. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 26 2024
R-4.5-win-x86_64OKOct 26 2024
R-4.5-linux-x86_64OKOct 26 2024
R-4.4-win-x86_64OKOct 26 2024
R-4.4-mac-x86_64OKOct 26 2024
R-4.4-mac-aarch64OKOct 26 2024
R-4.3-win-x86_64OKOct 26 2024
R-4.3-mac-x86_64OKOct 26 2024
R-4.3-mac-aarch64OKOct 26 2024

Exports:AIT.distAIT.moreAIT.objbackward.eliminationchisq.CAchisq.distcomp.repconf.methcreate.d.matCS.moreCS.objforward.selectionKL.distKL.moreKL.objMEANmethmultiplicativeReplacementp.MLEp.MUFASAp.QFASAp.sim.QFASAp.SMUFASAPOOLVARmethprey.clusterprey.on.preypseudo.predpseudo.pred.normtestfordiff.ind.boottestfordiff.ind.boot.funtestfordiff.ind.pval

Dependencies:bayesmBHbigassertrbigparallelrbigstatsrbitbootbootstrapclasscliclustercodetoolscolorspaceCompositionalcompositionscowplotDEoptimRdoParalleldplyremplikenergyfansifarverffflockforeachformatRfutile.loggerfutile.optionsgamlssgamlss.datagamlss.distgenericsggplot2glmnetgluegslgtableisobanditeratorslabelinglambda.rlatticelifecyclemagrittrMASSMatrixMatrixModelsmdamgcvminpack.lmmixturemnormtmunsellmvhtestsmvtnormnlmennetnumDerivparallellypermutepillarpkgconfigpsquadprogquantregR6RColorBrewerRcppRcppArmadilloRcppEigenRcppGSLRcppParallelRcppZigguratregdaRfastRfast2RhpcBLASctlrlangrmioRnanoflannrobustbaseRsolnpRSpectrascalesshapesnSparseMsurvivaltensorAtibbletidyselectTMBtruncnormutf8vctrsveganviridisLitewithr

MUFASA Workflow Example

Holly Steeves

Rendered fromMUFASA_Workflow_Example.Rmdusingknitr::rmarkdownon Oct 26 2024.

Last update: 2022-10-18
Started: 2021-06-02

Parallel Execution for Confidence Intervals

Justin Kamerman

Rendered fromParallel_Execution_for_Confidence_Intervals.Rmdusingknitr::rmarkdownon Oct 26 2024.

Last update: 2022-10-18
Started: 2021-06-02

QFASA Workflow Example

Shelley Lang

Rendered fromQFASA_Workflow_Example.Rmdusingknitr::rmarkdownon Oct 26 2024.

Last update: 2021-07-20
Started: 2021-06-02

SMUFASA Workflow Example

Jennifer McNichol

Rendered fromSMUFASA_Workflow_Example.Rmdusingknitr::rmarkdownon Oct 26 2024.

Last update: 2022-10-18
Started: 2021-07-20

Readme and manuals

Help Manual

Help pageTopics
Returns the distance between two compositional vectors using Aitchison's distance measure.AIT.dist
Used to provide additional information on various model components evaluated at the optimal solution, i.e., using the QFASA diet estimates and Aitchison distance measure.AIT.more
Used in 'solnp()' as the objective function to be minimized when Aitchison distance measure is chosen.AIT.obj
Returns diet estimates corresponding to a sample of predators based on a backward elimination algorithm that chooses the prey species to be included in the modelling.backward.elimination
Sample example of balanced repeatability diet estimates data with only two repeated measurements per predator.bal.diet.data
Fatty acid calibration coefficients.CC
Called by 'create.d.mat()' to compute the chi-square distance.chisq.CA
Returns the distance between two compositional vectors using the chi-square distance.chisq.dist
Repeatability in Diet Estimatescomp.rep
Confidence Intervals for Diet Proportionsconf.meth
Called by 'testfordiff.ind.boot.fun()' to create a matrix of distances.create.d.mat
Used to provide additional information on various model components evaluated at the optimal solution, i.e., using the QFASA diet estimates and chi-square distance measure.CS.more
Used in 'solnp()' as the objective function to be minimized when chi-square distance measure is chosen. Unlike 'AIT.obj()' and 'KL.obj()', does not require modifying zeros.CS.obj
List of fatty acids used in sample prey and predator data sets, 'preyFAs' and 'predatorFAs' respectively.FAset
Returns diet estimates corresponding to a sample of predators based on a forward selection algorithm that chooses the prey species to be included in the modelling.forward.selection
Returns the distance between two compositional vectors using Kullback-Leibler distance measure.KL.dist
Used to provide additional information on various model components evaluated at the optimal solution, i.e., using the QFASA diet estimates and Kullback-Leibler distance measure.KL.more
Used in 'solnp()' as the objective function to be minimized when Kullback-Leibler distance measure is chosen.KL.obj
Returns the geometric mean of a compositional vectormean_geometric
Returns the multivariate mean FA signature of each prey group entered into the QFASA model. Result can be passed to prey.mat in 'p.QFASA()'.MEANmeth
Multiplicative replacement of zeroesmultiplicativeReplacement
Returns simplified MLE diet estimates corresponding to a sample of predators.p.MLE
Returns MUFASA diet estimates corresponding to a sample of predators.p.MUFASA
Returns QFASA diet estimates corresponding to a sample of predators.p.QFASA
Simultaneous estimation of diet composition and calibration coefficientsp.sim.QFASA
Simultaneous maximum unified fatty acid signature analysisp.SMUFASA
Computes within species variance-covariance matrices on transformed scaled, along with a pooled estimate.POOLVARmeth
Predator fatty acid signatures. Each predator signature is a row with fatty acid proportions in columns.predatorFAs
Produces a dendrogram using distances between the mean FA signatures of the prey types.prey.cluster
Each prey fatty acid signature is systematically removed from the supplied prey database and its QFASA diet estimate is obtained by treating the individual as a predator.prey.on.prey
Prey fatty acid signatures. Each prey signature is a row with fatty acid proportions in columns.preyFAs
Generate a pseudo predator by sampling with replacement from prey database.pseudo.pred
Generate a pseudo predator parametrically from multivariate normal distributions.pseudo.pred.norm
QFASA: A package for Quantitative Fatty Acid Signature AnalysisQFASA-package QFASA
Returns 'sum(alpha)' and used in 'solnp()'.QFASA.const.eqn
Splits prey database into a simulation set (1/3) and a modelling set (2/3). Returns a list:split_prey
Called by 'testfordiff.ind.pval()'.testfordiff.ind.boot
Called by 'testfordiff.ind.boot()'.testfordiff.ind.boot.fun
Test for a difference between two independent samples of compositional data. Zeros of any type are allowed.testfordiff.ind.pval
Sample example of unbalanced repeatability diet estimates data with a max of two repeated measurements per predator.unbal.diet.data