smashr | R Documentation |
This package performs nonparametric regression on
univariate Poisson or Gaussian data using multi-scale methods. For
the Poisson case, the data x
is a vector, with x_j \sim
Poi(\mu_j)
where the mean vector \mu
is to be estimated.
For the Gaussian case, the data x
are a vector with x_j
\sim N(\mu_j, \sigma^2_j)
. Where the mean vector \mu
and
variance vector \sigma^2
are to be estimated. The primary
assumption is that \mu
is spatially structured, so \mu_j
- \mu_{j+1}
will often be small (that is, roughly, \mu
is
smooth). Also \sigma
is spatially structured in the Gaussian
case (or, optionally, \sigma
is constant, not depending on
j
).
The function smash
provides a minimal
interface to perform simple smoothing. It is actually a wrapper to
smash.gaus
and smash.poiss
which
provide more options for advanced use. The only required input is
a vector of length 2^J for some integer J. Other options include
the possibility of returning the posterior variances, specifying a
wavelet basis (default is Haar, which performs well in general due
to the fact that smash uses the translation-invariant transform)
Matthew Stephens and Zhengrong Xing
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