Mapping Smoothed Effect Estimates from Individual-Level Data
AIC of the modgam Object
Coefficents of the gamcox Object
Coefficients of the modgam Object
Maps Predicted Values and Clusters on a Two-Dimentional Map
Calculating Derivatives of Partial Likelihood for Cox Proportional Haz...
Formula of the gamcox Object
Formula of the modgam Object
Fit a Cox Additive Model with a Two-Dimensional Smooth
Mapping Smoothed Effect Estimates from Individual-Level Spatial Data
Revision Dates for MapGAM Functions
Fit a Generalized Additive Model (GAM) with a Two-Dimensional Smooth a...
Prediction for GAM Fits
Determine the Optimal Span Size for modgam
Maps Predicted Values and Clusters for modgam Objects
Create a Grid and Clip It to a Map and Data Bounds
Prediction Method for gamcox Fits
Print the gamcox Object
Print the modgam Object
Residuals of the gamcox Object
Residuals of the modgam Object
Unmatched Control Sampling
Summarize the gamcox Object
Summarize the modgam Object
Build a Formula Based on Data for modgam Function
Trim a Data Set To Map Boundaries
Contains functions for mapping odds ratios, hazard ratios, or other effect estimates using individual-level data such as case-control study data, using generalized additive models (GAMs) or Cox models for smoothing with a two-dimensional predictor (e.g., geolocation or exposure to chemical mixtures) while adjusting linearly for confounding variables, using methods described by Kelsall and Diggle (1998), Webster at al. (2006), and Bai et al. (2020). Includes convenient functions for mapping point estimates and confidence intervals, efficient control sampling, and permutation tests for the null hypothesis that the two-dimensional predictor is not associated with the outcome variable (adjusting for confounders).