Paper · 1997 · Environmental Entomology

Projection Pursuit Nonparametric Regression Used for Predicting Insect Densities from Visual Abundance Classes

Laurent Lapchin; Roger Boll; Jacques Rochat; et al.

4
claims
4
verbatim quotes
12
citations
2
research areas
—
Not open access

Abstract

Two visual abundance methods were used to estimate the density of healthy or mummified aphids, Aphis gossypii Glover, on leaves and whole cucumber plants in greenhouses. Additional explanatory variables (e.g., proportion of infested leaves per plant or number of leaves per plant) were collected at the same time. Precise counts were also made on the same spatial units and fit to visual class data along with the explanatory variables using multiple regression with Poisson errors, logistic regression, and projection pursuit regression. Smaller subsets of data were used to test the robustness of the regression models. Multiple regression with Poisson errors gave the least precise fits in all cases, and the projection pursuit regression gave slightly better predictions than logistic regression. The accuracy of the logistic regression was similar for both the reference and the validation data sets. Visual abundance estimates and the associated explanatory variables are easy to gather, and when substituted into the projection pursuit regression model, they can be used to estimate aphid densities. Such models are useful in metapopulation studies of highly abundant organisms where extensive census data are required.

Abstract from the paper’s OpenAlex record, shown for indexing and research. Copyright remains with the authors or the publisher. Read at the source ↗

The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.

Claims 4

Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

Poisson multiple regression gave the least precise fits for aphid density estimation in all cases.

Outcome
least precise fitse0
Direction
least precisee0
Comparator
Multiple regression with Poisson errorse0

Verbatim

e0

“Multiple regression with Poisson errors gave the least precise fits in all cases”

✓ located verbatim in the source · abstract, characters 622–702 · show in text ↑
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Projection pursuit regression predicted aphid density slightly better than logistic regression.

Outcome
predictionse0
Direction
slightly bettere0
Comparator
logistic regressione0

Verbatim

e0

“the projection pursuit regression gave slightly better predictions than logistic regression”

✓ located verbatim in the source · abstract, characters 708–799 · show in text ↑
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Logistic regression accuracy was similar for reference and validation data.

Outcome
accuracye0
Direction
similare0
Comparator
reference and the validation data setse0

Verbatim

e0

“The accuracy of the logistic regression was similar for both the reference and the validation data sets”

✓ located verbatim in the source · abstract, characters 801–904 · show in text ↑
Method / resourceauthor interpretationoriginal resultpositivemeasured

Claim · model-written summary

Visual abundance estimates and explanatory variables in the projection pursuit model can estimate aphid densities.

Outcome
estimate aphid densitiese0
Qualifier
easy to gathere0

Not reportedComparator

Verbatim

e0

“Visual abundance estimates and the associated explanatory variables are easy to gather, and when substituted into the projection pursuit regression model, they can be used to estimate aphid densities”

✓ located verbatim in the source · abstract, characters 906–1105 · show in text ↑