README.Rmd

---
output: github_document
---

```{r, echo = FALSE}
knitr::opts_chunk$set(
  fig.path = "man/figures/README-"
)
```

Overview 
---

`badlm` is a small R package for inferring the temporally delayed dependence between a predictor and a response variable.  


Installation 
---

To install the development version of the package, use
```{r, eval = FALSE, warning=FALSE, message=F}
devtools::install_github("alastairrushworth/badlm")

# load the package
library(badlm)
```

```{r, echo = FALSE, warning=FALSE, message=F}
library(badlm)
```


`badlm` example
---


Generate a distributed lag function
```{r, cache = TRUE}
# 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
```{r, cache = TRUE}
# 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
```{r, cache = TRUE, results='hide'}
dlm_est <- badlm(x = expose, y = deaths, 
           nlag = 50, k = 30, samples = 10000)
```

Plot the resulting lag curve

```{r}
plot_lagcurve(dlm_est)
```