Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Help
Clear All

Query Builder

Query box

Help
Clear All

For additional assistance using the Custom Query please check out our Help Page

i

On Extrapolating Past the Range of Observed Data When Making Statistical Predictions in Ecology

Filetype[PDF-2.30 MB]


Details Supporting Files Related Documents You May Also Like

Details:

  • Journal Title:
    PLOS ONE
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    Ecologists are increasingly using statistical models to predict animal abundance and occurrence in unsampled locations. The reliability of such predictions depends on a number of factors, including sample size, how far prediction locations are from the observed data, and similarity of predictive covariates in locations where data are gathered to locations where predictions are desired. In this paper, we propose extending Cook's notion of an independent variable hull (IVH), developed originally for application with linear regression-models, to generalized regression models as a way to help assess the potential reliability of predictions in unsampled areas. Predictions occurring inside the generalized independent variable hull (gIVH) can be regarded as interpolations, while predictions occurring outside the gIVH can be regarded as extrapolations worthy of additional investigation or skepticism. We conduct a simulation study to demonstrate the usefulness of this metric for limiting the scope of spatial inference when conducting model-based abundance estimation from survey counts. In this case, limiting inference to the gIVH substantially reduces bias, especially when survey designs are spatially imbalanced. We also demonstrate the utility of the gIVH in diagnosing problematic extrapolations when estimating the relative abundance of ribbon seals in the Bering Sea as a function of predictive covariates. We suggest that ecologists routinely use diagnostics such as the gIVH to help gauge the reliability of predictions from statistical models (such as generalized linear, generalized additive, and spatio-temporal regression models).
  • Keywords:
  • Source:
    PLOS ONE. 2015; 10(10): e0141416.
  • DOI:
  • Pubmed Central ID:
    PMC4619888
  • Document Type:
    Journal Article
  • Place as Subject:
    United States
  • Rights Information:
    CC BY
  • Compliance:
    PMC
  • Main Document Checksum:
  • Download URL:
  • File Type:

Supporting Files

More +

Related Documents

You May Also Like

Checkout today's featured content at repository.library.noaa.gov

Version 3.26