dlnm-package: Distributed Lag Non-linear Models (DLNM)
Description
The package dlnm contains functions to specify basis and cross-basis matrices in order to run distributed lag models (DLM) and their non-linear extension (DLNM), then to predict and plot the results for a fitted model.Details
Distributed lag non-linear models (DLNM) represent a modelling framework to describe simultaneously non-linear and delayed dependencies in time-series data. This methodology is based on the definition of a cross-basis, a bi-dimensional space of functions specifying the dependency along the space of the predictor and along lags. The cross-basis functions are built combining the basis functions for the two dimensions, chosen among a set of possible bases. This framework includes simple distributed lag models (DLM) as a special case.
Given a series of observations ordered and equally spaced in time, crossbasis creates a matrix object of class "crossbasis" containing the transformed variables to be included in the model formula. The estimation is obtained by default model commands, usually glm. After the model fitting, crosspred predicts the results for a set of suitable values of the original predictor and stores them in a "crosspred" object. Finally, crossplot offers a set of choices to plot the results.
Use citation("dlnm") to cite this package.
A list of changes included in the current and previous versions can be found typing file.show(system.file("dlnmChangeLog", package = "dlnm")).
For further information on DLNM, see the references below. For a detailed description of the capabilities of the package, refer to:
vignette("dlnmOverview", package = "dlnm")References
Armstrong, B. Models for the relationship between ambient temperature and daily mortality. Epidemiology. 2006, 17(6):624-31.See Also
crossbasis to create the basis and cross-basis matrices. crosspred to predict the effects after model fitting. crossplot to plot several type of graphs.