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Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Related Experiment Video

Updated: May 28, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Distributed Lag Linear and Non-Linear Models in R: The Package dlnm.

Antonio Gasparrini1

  • 1Department of Social and Environmental Health Research, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London WC1H 9SH, United Kingdom.

Journal of Statistical Software
|October 18, 2011
PubMed
Summary

This study introduces Distributed Lag Non-linear Models (DLNMs) for analyzing time series data with complex effects. The R package dlnm facilitates flexible modeling and interpretation of these non-linear and delayed relationships.

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Last Updated: May 28, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Statistics
  • Environmental Epidemiology
  • Time Series Analysis

Background:

  • Time series data often exhibit complex associations with delayed and non-linear effects.
  • Traditional statistical models may not adequately capture these intricate relationships.
  • Flexible modeling frameworks are needed to accurately represent temporal dependencies.

Purpose of the Study:

  • To provide an overview of the Distributed Lag Non-linear Models (DLNMs) framework.
  • To describe the R package dlnm for implementing and interpreting DLNMs.
  • To demonstrate the application of DLNMs using real-world time series data.

Main Methods:

  • DLNMs utilize a cross-basis, a two-dimensional functional space defined by basis functions.
  • These functions model the relationships between a predictor and its lagged effects.
  • The dlnm R package offers tools for model specification, fitting, and graphical interpretation.

Main Results:

  • The dlnm package enables the flexible modeling of non-linear and delayed associations in time series.
  • Graphical representations aid in the interpretation of complex exposure-lag-response relationships.
  • The framework allows for robust analysis of various time series data types.

Conclusions:

  • DLNMs provide a powerful and flexible approach for analyzing time series data with complex dependencies.
  • The dlnm R package simplifies the implementation and interpretation of these advanced statistical models.
  • This methodology enhances the understanding of delayed and non-linear effects in scientific research.