PubMed · 495787
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
D R Barker, L M Diana. 1979. Measurement errors and curve fitting.. https://doi.org/10.1152/ajpregu.1979.237.5.r366
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
MOTIVATION: Compositional data are frequently encountered in many disciplines, such as in next-generation sequencing experiments widely used in biomedical studies. Regression analysis with compositional data as either responses or predictors has been well studied. However, when both responses and predictors are compositional, the inventory of analysis tools is surprisingly limited, especially in the high-dimensional setting. Among the few existing methods, most of them rely on a log-ratio transformation to move compositional data from the simplex to real numbers. Yet, a serious weakness of these methods is their failure to handle the substantial fraction of zeroes observed in data collected from next-generation sequencing experiments. RESULTS: To investigate associations between two high-dimensional multi-omics compositions, we propose a composition-on-composition (COC) regression analysis method which does not require log-ratio transformations and hence can handle zeroes in the data. To account for high dimensionality, we estimate regression coefficients using a penalized estimation equation approach. Finally, inference procedures for COC regression are also proposed. Superior performance of COC is demonstrated through both comprehensive numerical simulations and case studies. AVAILABILITY AND IMPLEMENTATION: Source R codes to implement COC method is available at https://github.com/nrios4/COC.
In the present paper the prediction method using the logistic regression is explained, and the range of problems for its use is deliminated. The example is presented of mentioned method's application on how to predict the dog survival in a radiolobiological experiment. The obtained results are compared with the prediction of outcome using the linear discriminant function. Both models are identic in a proportion of erroneously classified subjects. This method may be diagnostically supportive in ranging individuals to one of two groups delimitated previously.
Explore the source record for details and available documents.