skills.rest
ntluong95
ntluong95COMMUNITY
Explore all skills by this author →
WebsiteGitHub

DLNM Model Specification

Specify and fit Distributed Lag Non-Linear Models for time series regression.

Updated Mar 7, 2026
Search Tags:#time series#glm#confounding#dlnm#quasi-poisson#splines
One-click install
npx skills add https://github.com/ntluong95/agent-skills-statistics --skill dlnm-model-specification
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: DLNM Model Specification
Source: https://github.com/ntluong95/agent-skills-statistics/tree/main/skills/dlnm/model-specification
Command: npx skills add https://github.com/ntluong95/agent-skills-statistics --skill dlnm-model-specification

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies the process of specifying and fitting complex Distributed Lag Non-Linear Models (DLNMs) for time series data, particularly in environmental epidemiology.

Core Features & Use Cases

  • Canonical Model Structure: Provides a template for fitting GLMs with cross-basis terms, time trends, and confounders.
  • Outcome & Family Guidance: Recommends appropriate families (quasi-Poisson, Poisson, negative binomial) based on outcome type and over-dispersion.
  • Confounding Control: Details strategies for adjusting for time trends, seasonality, meteorological factors, and other temporal confounders.
  • Use Case: Fit a quasi-Poisson GLM to daily mortality data, including a DLNM for temperature, natural splines for long-term trends and day-of-week, and a separate spline for daily temperature.

Quick Start

Fit a DLNM model for daily deaths using cross-basis for temperature, natural splines for time and temperature, and day of week.

Frequently Asked Questions about DLNM Model Specification

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I specify a DLNM time series model for environmental epidemiology data?▼

To specify a DLNM time series model, use a GLM with cross-basis terms for exposure-lag-response relationships, natural cubic splines for temporal trends, and quasi-Poisson families for over-dispersed count outcomes like daily mortality data.

When should I use quasi-Poisson instead of Poisson in a time series regression?▼

Use quasi-Poisson in time series regression when modeling over-dispersed count outcomes, such as daily deaths or hospital admissions. It adjusts the standard errors appropriately, whereas standard Poisson assumes the mean and variance are strictly equal.

How do I control for confounding in a distributed lag non-linear model?▼

Control confounding in a DLNM by adding natural cubic splines for long-term trends and seasonality, incorporating day-of-week effects as categorical variables, and including separate splines for meteorological factors like daily temperature.

Can I fit a DLNM with negative binomial regression instead of quasi-Poisson?▼

Yes, you can fit a DLNM using negative binomial regression instead of quasi-Poisson for over-dispersed count data. The model supports Poisson, quasi-Poisson, and negative binomial families depending on your outcome type and variance structure.

What is the best way to model temperature's lagged effects on daily mortality?▼

Model temperature's lagged effects on daily mortality by fitting a quasi-Poisson GLM with a cross-basis function for temperature, natural splines for long-term time trends, and indicator variables for day-of-week to adjust for temporal confounding.

Related Skills

Explore More Education & Research Skills→
ntluong95ntluong95

Time Series in R

Manage time series structures and autocorrelation diagnostics in R.

Community
Intermediate
ntluong95ntluong95

Cross-Basis Construction

Construct cross-basis matrices for Distributed Lag Non-Linear Models in R.

Community
Intermediate
ntluong95ntluong95

Confounding Adjustment

Control time-varying confounders in environmental time series analyses using R.

Community
Intermediate
ntluong95ntluong95

Generalized Additive Models

Fits and interprets generalized additive models in R using the mgcv package.

Community
Intermediate