Overview
badlm
is a small R package for inferring the temporally delayed
dependence between a predictor and a response variable. See the paper
- Rushworth, A. Bayesian adaptive distributed lag models. (2018). Arxiv link.
Installation
To install the development version of the package, use
devtools::install_github("alastairrushworth/badlm")
# load the package
library(badlm)
badlm
example
Generate a distributed lag function
# a nice distributed lag function - hump
x <- 0:50
dlfunction <- -0.1 + (0.01*exp(-0.2*x) + exp(dnorm(x, sd = 4, mean = 10))) / 10
plot(x, dlfunction, type = "l")
Generate predictor and response under the distributed lag function
# response is an AR(1) process
expose <- arima.sim(model = list(ar = 0.5), n = 500, sd = 0.1)
expose <- (expose - mean(expose)) / sd(expose)
lag_mat <- lag_matrix(expose, p = 50)
deaths.sig <- lag_mat %*% dlfunction
deaths <- deaths.sig + rnorm(450, sd = 0.01)
Try to recover the distribution lag function
dlm_est <- badlm(x = expose, y = deaths,
nlag = 50, k = 30, samples = 10000)
Plot the resulting lag curve
plot_lagcurve(dlm_est)