CRAN
qfasar 1.2.0
Quantitative Fatty Acid Signature Analysis in R
Released Jan 10, 2017 by Jeffrey F. Bromaghin
Dependencies
An implementation of Quantitative Fatty Acid Signature Analysis (QFASA) in R. QFASA is a method of estimating the diet composition of predators. The fundamental unit of information in QFASA is a fatty acid signature (signature), which is a vector of proportions describing the composition of fatty acids within lipids. Signature data from at least one predator and from samples of all potential prey types are required. Calibration coefficients, which adjust for the differential metabolism of individual fatty acids by predators, are also required. Given those data inputs, a predator signature is modeled as a mixture of prey signatures and its diet estimate is obtained as the mixture that minimizes a measure of distance between the observed and modeled signatures. A variety of estimation options and simulation capabilities are implemented. Please refer to the vignette for additional details and references.
Installation
Maven
This package can be included as a dependency from a Java or Scala project by including
the following your project's pom.xml
file.
Read more
about embedding Renjin in JVM-based projects.
<dependencies> <dependency> <groupId>org.renjin.cran</groupId> <artifactId>qfasar</artifactId> <version>1.2.0-b16</version> </dependency> </dependencies> <repositories> <repository> <id>bedatadriven</id> <name>bedatadriven public repo</name> <url>https://nexus.bedatadriven.com/content/groups/public/</url> </repository> </repositories>
Renjin CLI
If you're using Renjin from the command line, you load this library by invoking:
library('org.renjin.cran:qfasar')
Test Results
This package was last tested against Renjin 0.9.2644 on Jun 2, 2018.
- Test_CC_augmentation.CC_augmentation_is_correct_E1
- Test_CC_augmentation.CC_augmentation_is_correct_E2
- Test_CC_augmentation.CC_augmentation_is_correct_E3
- Test_CC_augmentation.CC_augmentation_is_correct_E4
- Test_CC_augmentation.CC_augmentation_is_correct_E5
- Test_diet_estimation.Diet_estimates_are_correct_E1
- Test_diet_estimation.Diet_estimates_are_correct_E2
- Test_diet_estimation.Diet_estimates_are_correct_E3
- Test_diet_estimation.Diet_estimates_are_correct_E4
- Test_diet_estimation.Diet_estimates_are_correct_E5
- Test_diet_estimation.Diet_estimates_are_correct_E6
- Test_dimac_algorithm.Distance_measure_1
- Test_dimac_algorithm.Distance_measure_2
- Test_dimac_algorithm.Distance_measure_3,_default_gamma
- Test_dimac_algorithm.Distance_measure_3,_gamma_0_5
- Test_estimation_of_chi-square_gamma_parameter.Gamma_found
- Test_estimation_of_chi-square_gamma_parameter.Gamma_not_found
- Test_fat_conversion.Fat_conversion_with_variance_is_correct
- Test_fat_conversion.Fat_conversion_without_variance_is_correct
- Test_pooling_of_diet_estimates.First_diet_is_correct
- Test_pooling_of_diet_estimates.Second_diet_is_correct
- add_cc_err-examples
- adj_diet_fat-examples
- cc_aug-examples
- comp_chi_gamma-examples
- diet_pool-examples
- dimac-examples
- est_diet-examples
- find_boot_ss-examples
- gof-examples
- lopo-examples
- lopo_pool-examples
- make_diet_grid-examples
- make_diet_rand-examples
- make_ghost-examples
- make_pred_sigs-examples
- make_prey_part-examples
- pred_beyond_prey-examples
- prep_fa-examples
- prep_sig-examples
- testthat