susie_trendfilter | R Documentation |
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")
.
susie_trendfilter(y, order = 0, standardize = FALSE, use_mad = TRUE, ...)
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, |
standardize |
Logical indicating whether to standardize the X
variables ("basis functions"); |
use_mad |
Logical indicating whether to use the "median
absolute deviation" (MAD) method to the estimate residual
variance. If |
... |
Other arguments passed to |
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")
.
A "susie" fit; see susie
for details.
R. J. Tibshirani (2014). Adaptive piecewise polynomial estimation via trend filtering. Annals of Statistics 42, 285-323.
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)
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