predict.not {not} | R Documentation |
Estimate signal for a 'not' object.
Description
Estimates signal in object$x
with change-points at cpt
. The type of the signal depends on
on the value of contrast
that has been passed to not
(see details below).
Usage
## S3 method for class 'not'
predict(object, cpt, ...)
Arguments
object |
An object of class 'not', returned by |
cpt |
An integer vector with locations of the change-points.
If missing, the |
... |
Further parameters that can be passed to |
Details
The data points provided in object$x
are assumed to follow
Y_{t} = f_{t}+\sigma_{t}\varepsilon_{t},
for t=1,\ldots,n
, where n
is the number of observations in object$x
, the signal f_{t}
and the standard deviation \sigma_{t}
are non-stochastic with change-points at locations given in cpt
and \varepsilon_{t}
is a white-noise. Denote by \tau_{1}, \ldots, \tau_{q}
the elements in cpt
and set \tau_{0}=0
and \tau_{q+1}=T
. Depending on the value of contrast
that has been passed to not
to construct object
, the returned value is calculated as follows.
For
contrast="pcwsConstantMean"
andcontrast="pcwsConstantMeanHT"
, in each segment(\tau_{j}+1, \tau_{j+1})
,f_{t}
fort\in(\tau_{j}+1, \tau_{j+1})
is approximated by the mean ofY_{t}
calculated overt\in(\tau_{j}+1, \tau_{j+1})
.For
contrast="pcwsLinContMean"
,f_{t}
is approximated by the linear spline fit with knots at\tau_{1}, \ldots, \tau_{q}
minimising the l2 distance between the fit and the data.For
contrast="pcwsLinMean"
in each segment(\tau_{j}+1, \tau_{j+1})
, the signalf_{t}
fort\in(\tau_{j}+1, \tau_{j+1})
is approximated by the line\alpha_{j} + \beta_{j} t
, where the regression coefficients are found using the least squares method.For
contrast="pcwsQuad"
, the signalf_{t}
fort\in(\tau_{j}+1, \tau_{j+1})
is approximated by the curve\alpha_{j} + \beta_{j} t + \gamma_{j} t^2
, where the regression coefficients are found using the least squares method.For
contrast="pcwsConstMeanVar"
, in each segment(\tau_{j}+1, \tau_{j+1})
,f_{t}
and\sigma_{t}
fort\in(\tau_{j}+1, \tau_{j+1})
are approximated by, respectively, the mean and the standard deviation ofY_{t}
, both calculated overt\in(\tau_{j}+1, \tau_{j+1})
.
Value
A vector wit the estimated signal or a two-column matrix with the estimated estimated signal and standard deviation if contrast="pcwsConstMeanVar"
was used to construct object
.
See Also
Examples
# **** Piecewisce-constant mean with Gaussian noise.
x <- c(rep(0, 100), rep(1,100)) + rnorm(100)
# *** identify potential locations of the change-points
w <- not(x, contrast = "pcwsConstMean")
# *** when 'cpt' is omitted, 'features' function is used internally
# to choose change-points locations
signal.est <- predict(w)
# *** estimate the signal specifying the location of the change-point
signal.est.known.cpt <- predict(w, cpt=100)
# *** pass arguments of the 'features' function through 'predict'.
signal.est.aic <- predict(w, penalty.type="aic")
# **** Piecewisce-constant mean and variance with Gaussian noise.
x <- c(rep(0, 100), rep(1,100)) + c(rep(2, 100), rep(1,100)) * rnorm(100)
# *** identify potential locations of the change-points
w <- not(x, contrast = "pcwsConstMeanVar")
# *** here signal is two-dimensional
signal.est <- predict(w)