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An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach

Overview of attention for article published in BMC Pharmacology, June 2010
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)

Mentioned by

blogs
1 blog
twitter
12 X users
wikipedia
1 Wikipedia page

Citations

dimensions_citation
673 Dimensions

Readers on

mendeley
902 Mendeley
citeulike
3 CiteULike
Title
An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach
Published in
BMC Pharmacology, June 2010
DOI 10.1186/1471-2210-10-6
Pubmed ID
Authors

Andrej-Nikolai Spiess, Natalie Neumeyer

Abstract

It is long known within the mathematical literature that the coefficient of determination R(2) is an inadequate measure for the goodness of fit in nonlinear models. Nevertheless, it is still frequently used within pharmacological and biochemical literature for the analysis and interpretation of nonlinear fitting to data.

X Demographics

X Demographics

The data shown below were collected from the profiles of 12 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 902 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 <1%
Brazil 4 <1%
Germany 3 <1%
Australia 3 <1%
France 2 <1%
Switzerland 2 <1%
Sweden 2 <1%
Japan 2 <1%
United Kingdom 2 <1%
Other 10 1%
Unknown 867 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 218 24%
Researcher 140 16%
Student > Master 138 15%
Student > Bachelor 63 7%
Student > Doctoral Student 53 6%
Other 142 16%
Unknown 148 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 173 19%
Engineering 116 13%
Environmental Science 68 8%
Biochemistry, Genetics and Molecular Biology 50 6%
Computer Science 36 4%
Other 237 26%
Unknown 222 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 September 2023.
All research outputs
#1,875,832
of 24,829,155 outputs
Outputs from BMC Pharmacology
#3
of 65 outputs
Outputs of similar age
#6,384
of 101,889 outputs
Outputs of similar age from BMC Pharmacology
#2
of 2 outputs
Altmetric has tracked 24,829,155 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 65 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 101,889 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.