# susie_trendfilter: Apply susie to trend filtering (especially changepoint... In susieR: Sum of Single Effects Linear Regression

## Description

Fits the non-parametric Gaussian regression model y = mu + e, where the mean mu is modelled as mu = Xb, X is a matrix with columns containing an appropriate basis, and b is vector with a (sparse) SuSiE prior. In particular, when `order = 0`, the jth column of X is a vector with the first j elements equal to zero, and the remaining elements equal to 1, so that b_j corresponds to the change in the mean of y between indices j and j+1. For background on trend filtering, see Tibshirani (2014). See also the "Trend filtering" vignette, `vignette("trend_filtering")`.

## Usage

 `1` ```susie_trendfilter(y, order = 0, standardize = FALSE, use_mad = TRUE, ...) ```

## Arguments

 `y` An n-vector of observations ordered in time or space (assumed to be equally spaced). `order` An integer specifying the order of trend filtering. The default, `order = 0`, corresponds to "changepoint" problems (i.e., piecewise constant mu). Although `order > 0` is implemented, we do not recommend its use; in practice, we have found problems with convergence of the algorithm to poor local optima, producing unreliable inferences. `standardize` Logical indicating whether to standardize the X variables ("basis functions"); `standardize = FALSE` is recommended as these basis functions already have a natural scale. `use_mad` Logical indicating whether to use the "median absolute deviation" (MAD) method to the estimate residual variance. If `use_mad = TRUE`, susie is run twice, first by fixing the residual variance to the MAD value, then a second time, initialized to the first fit, but with residual variance estimated the usual way (by maximizing the ELBO). We have found this strategy typically improves reliability of the results by reducing a tendency to converge to poor local optima of the ELBO. `...` Other arguments passed to `susie`.

## Details

This implementation exploits the special structure of X, which means that the matrix-vector product X^Ty is fast to compute; in particular, the computation time is O(n) rather than O(n^2) if `X` were formed explicitly. For implementation details, see the "Implementation of SuSiE trend filtering" vignette by running `vignette("trendfiltering_derivations")`.

## Value

A "susie" fit; see `susie` for details.

## References

R. J. Tibshirani (2014). Adaptive piecewise polynomial estimation via trend filtering. Annals of Statistics 42, 285-323.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```set.seed(1) mu = c(rep(0,50),rep(1,50),rep(3,50),rep(-2,50),rep(0,200)) y = mu + rnorm(400) s = susie_trendfilter(y) plot(y) lines(mu,col = 1,lwd = 3) lines(predict(s),col = 2,lwd = 2) # Calculate credible sets (indices of y that occur just before # changepoints). susie_get_cs(s) # Plot with credible sets for changepoints. susie_plot_changepoint(s,y) ```

susieR documentation built on June 14, 2021, 5:07 p.m.