Literature DB >> 2759968

Fitting piecewise linear regression functions to biological responses.

E Vieth1.   

Abstract

An iterative approach was achieved for fitting piecewise linear functions to nonrectilinear responses of biological variables. This algorithm is used to estimate the parameters of the two (or more) regression functions and the separation point(s) (thresholds, sensitivities) by statistical approximation. Although it is often unknown whether the response of a biological variable is adequately described by one rectilinear regression function or by piecewise linear regression function(s) with separation point(s), an F test is proposed to determine whether one regression line is the optimal fitted function. A FORTRAN-77 program has been developed for estimating the optimal parameters and the coordinates of the separation point(s). A few sets of data illustrating this kind of problem in the analysis of thermoregulation, osmoregulation, and the neuronal responses are discussed.

Mesh:

Year:  1989        PMID: 2759968     DOI: 10.1152/jappl.1989.67.1.390

Source DB:  PubMed          Journal:  J Appl Physiol (1985)        ISSN: 0161-7567


  32 in total

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4.  Temperature sensitivity of neurones in slices of the rat spinal cord.

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5.  A new approach to the assessment of anaerobic metabolism: measurement of lactate in saliva.

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7.  Seasonal variations in circadian rhythms coincide with a phase of sensitivity to short photoperiods in the European hamster.

Authors:  Stefanie Monecke; Franziska Wollnik
Journal:  J Comp Physiol B       Date:  2005-02-22       Impact factor: 2.200

8.  The effect of CPAP in normalizing daytime sleepiness, quality of life, and neurocognitive function in patients with moderate to severe OSA.

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9.  Ambulatory glucose profile analysis of the juvenile diabetes research foundation continuous glucose monitoring dataset-Applications to the pediatric diabetes population.

Authors:  Gregory P Forlenza; Laura L Pyle; David M Maahs; Timothy C Dunn
Journal:  Pediatr Diabetes       Date:  2016-11-23       Impact factor: 4.866

10.  A model selection approach to discover age-dependent gene expression patterns using quantile regression models.

Authors:  Joshua W K Ho; Maurizio Stefani; Cristobal G dos Remedios; Michael A Charleston
Journal:  BMC Genomics       Date:  2009-12-03       Impact factor: 3.969

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