| Title |
chngpt: threshold regression model estimation and inference
|
|---|---|
| Published in |
BMC Bioinformatics, October 2017
|
| DOI | 10.1186/s12859-017-1863-x |
| Pubmed ID | |
| Authors |
Youyi Fong, Ying Huang, Peter B. Gilbert, Sallie R. Permar |
| Abstract |
Threshold regression models are a diverse set of non-regular regression models that all depend on change points or thresholds. They provide a simple but elegant and interpretable way to model certain kinds of nonlinear relationships between the outcome and a predictor. The R package chngpt provides both estimation and hypothesis testing functionalities for four common variants of threshold regression models. All allow for adjustment of additional covariates not subjected to thresholding. We demonstrate the consistency of the estimating procedures and the type 1 error rates of the testing procedures by Monte Carlo studies, and illustrate their practical uses using an example from the study of immune response biomarkers in the context of Mother-To-Child-Transmission of HIV-1 viruses. chngpt makes several unique contributions to the software for threshold regression models and will make these models more accessible to practitioners interested in modeling threshold effects. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 25% |
| Unknown | 3 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 75% |
| Members of the public | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 134 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 28 | 21% |
| Researcher | 15 | 11% |
| Student > Doctoral Student | 13 | 10% |
| Student > Master | 7 | 5% |
| Professor | 7 | 5% |
| Other | 23 | 17% |
| Unknown | 41 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 21 | 16% |
| Economics, Econometrics and Finance | 17 | 13% |
| Environmental Science | 11 | 8% |
| Biochemistry, Genetics and Molecular Biology | 10 | 7% |
| Medicine and Dentistry | 10 | 7% |
| Other | 18 | 13% |
| Unknown | 47 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 08 March 2022.
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#13,666,776
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Outputs from BMC Bioinformatics
#4,266
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Outputs of similar age
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Outputs of similar age from BMC Bioinformatics
#57
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