Description Usage Arguments Details Value References Examples

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")`

.

1 | ```
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.

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)
``` |

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