pdlm: Polynomial Distributed Lag Models

In wjunger/ares: Environmental air pollution epidemiology: a library for time series analysis

Description

Fit a constrained polynomial distributed lag model

Usage

pdlm(model, var, lags = 5, degrees = 2, ...)

Arguments

model	a model fitted by fit_core
var	a quoted string with the name with the exposure variable
lags	an integer indicating the number of lags to estimate the effects. Default is 5
degrees	an integer indicating the number of degrees for the constrained polynomial. Default is 2 for a parabolic shape
...	arguments passed on to other methods. See family

Details

This function updates model with the unconstrained distributed lag models using pdl. Then, the unconstrained coefficients and their standard errors are extracted using get.beta.

This model is thoroughly discussed in Schwartz (2000).

Value

The class pdlm is added to the model inheritance and the following list is returned

cmodel	the fitted constrained model
variate	the vector with exposure variate data
var.name	the name of exposure variate
beta	the unconstrained coefficients
lags	an integer indicating the lags used for the distributed lag structure
degrees	an integer indicating the degrees used for the polynomial in the distributed lag structure
call	function call

Author(s)

Washington Junger [e-mail] and Antonio Ponce de Leon [e-mail]

References

Schwartz, J. (2000) The distributed lag between air pollution and daily deaths. Epidemiology 11(3), 320–326.

See Also

gam,glm,fit_core

Examples

data(admrio)
setup(admrio,"date")
f <- resp5~s(time)+weekdays+s(tmpmax)+s(humid)
m <- fit_core(f)
dlm <- pdlm(m,"pm10",lags=5,degrees=2)

wjunger/ares documentation built on Dec. 23, 2021, 5:17 p.m.