R Code for multiple linear regression analysis for ETNP primary nitrite maximum data
Abstract/Contents
- Abstract
- Multiple linear regression (MLR) models were built to assess the environmental variables that influence the depth and magnitude of the PNM feature in the ETNP. The first set 45 of MLR models (“full” models) used semi-continuous measurements (temperature, density, oxygen, chlorophyll fluorescence, PAR, nitrate, nitrite and ammonium) from CTD/PPS casts collected at 16 stations on the 2016 cruise to predict nitrite concentration.
Description
| Type of resource | software, multimedia, text |
|---|---|
| Publication date | December 24, 2022; 2022 |
Creators/Contributors
| Author | Travis, Nicole |
|---|---|
| Author | Kelly, Colette |
| Author | Mulholland, Margaret |
| Author | Casciotti, Karen |
Subjects
| Subject | Regression analysis |
|---|---|
| Genre | Software/code |
| Genre | Code |
| Genre | Computer program |
Bibliographic information
| Related item |
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| DOI | https://doi.org/10.25740/bf589mh2984 |
| Location | https://purl.stanford.edu/bf589mh2984 |
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under an Open Data Commons Attribution License v1.0.
Preferred citation
- Preferred citation
- Travis, N., Kelly, C., Mulholland, M., and Casciotti, K. (2022). R Code for multiple linear regression analysis for ETNP primary nitrite maximum data. Stanford Digital Repository. Available at https://purl.stanford.edu/bf589mh2984. https://doi.org/10.25740/bf589mh2984.
Collection
Marine Biogeochemistry Data
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