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. (submitted). 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).
|Author||Lu Bai, Scott Bartell, Robin Bliss, and Veronica Vieira|
|Date of publication||2016-09-03 16:39:06|
|Maintainer||Scott Bartell <email@example.com>|
beertweets: Geocoded Tweets with Beer Indicator
CAdata: Deidentified survival data for California
CAgrid: A grid on state of California
CAmap: Map of California
colormap: Maps Predicted Values and Clusters on a two-dimentional map
dls: Calculating derivatives of partial likelihood for Cox...
gamcox: Fit a Cox Additive Model with a Two-Dimensional Smooth
MAdata: Synthetic Case-Control Data for Massachusetts
MAmap: Map of Massachusetts
MapGAM-package: Mapping Smoothed Effect Estimates from Individual-Level...
MapGAM.version: Revision dates for MapGAM functions
modgam: Fit a Generalized Additive Model (GAM) with a Two-Dimensional...
mypredict.gam: Prediction method for GAM fits
optspan: Determine the Optimal Span Size for modgam
plot.modgam: Maps Predicted Values and Clusters for modgam Objects
predgrid: Create a Grid and Clip it to a Map and Data Bounds
predict.gamcox: Prediction method for GAMCOX fits
print.gamcox: Print gamcox Objects
print.modgam: Print modgam Objects
sampcont: Unmatched Control Sampling
toformula: Build a formula based on data for 'modgam' function
trimdata: Trim a Data Set To Map Boundaries