data.frame
,
and growth rates can be computed using derivativesUses smooth.spline
to fit a spline to all the values of
response
stored in data
.
The amount of smoothing can be controlled by df
and the
smoothing.method
provides for direct
amd
logarithmic
smoothing. If df = NULL
, the amount of
smoothing is controlled by the default arguments and those you supply
for smooth.spline
. The method of Huang (2001) for correcting the
fitted spline for estimation bias at the end-points will be applied if
correctBoundaries
is TRUE
.
The derivatives of the fitted spline can also be obtained, and the
Absolute and Relative Growth Rates ( AGR and RGR) computed using them, provided
correctBoundaries
is FALSE
. Otherwise, growth rates can be
obtained by difference using splitContGRdiff
.
By default, smooth.spline
will issue an error if there are not
at least four distinct x-values. On the other hand, fitSplines
issues a warning and sets all smoothed values and derivatives to
NA
. The handling of missing values in the observations is
controlled via na.x.action
and na.y.action
.
fitSpline(data, response, x, df=NULL, smoothing.method = "direct",
correctBoundaries = FALSE,
deriv=NULL, suffices.deriv=NULL, RGR=NULL, AGR=NULL,
na.x.action="exclude", na.y.action = "exclude", ...)
A data.frame
containing the column to be smoothed.
A character
giving the name of the column in
data
that is to be smoothed.
A character
giving the name of the column in
data
that contains the values of the predictor variable.
A numeric
specifying the desired equivalent number of degrees
of freedom of the smooth (trace of the smoother matrix). Lower values
result in more smoothing. If df = NULL
, the amount of smoothing
is controlled by the default arguments for and those that you supply to
smooth.spline
.
A character
giving the smoothing method
to use. The two possibilites are (i) "direct"
, for directly
smoothing the observed response
, and (ii) "logarithmic"
, for
smoothing the log
-transformed response
and then
back-transforming by taking the exponentional of the fitted values.
A logical
indicating whether the fitted spline
values are to have the method of Huang (2001) applied
to them to correct for estimation bias at the end-points. Note that
deriv
must be NULL
for correctBoundaries
to be
set to TRUE
.
A numeric
specifying one or more orders of derivatives
that are required.
A character
giving the characters to be
appended to the names of the derivatives. If NULL
and the derivative is to be retained then smooth.dv
is appended.
A character
giving the character to be appended
to the smoothed response
to create the RGR name,
but only when smoothing.method
is direct
and the
RGR is required. When smoothing.method
is direct
and the RGR is required RGR
must not be NULL
and
deriv
must include one so that the first derivative is
available for calculating it.
When smoothing.method
is logarithmic
, the RGR is
the backtransformed first derivative and so, to obtain it, merely
include 1
in deriv
and any suffix for it in
suffices.deriv
. Leave RGR
set to NULL
.
A character
giving the character to be appended
to the smoothed response
to create the AGR name,
but only when smoothing.method
is logarithmic
and the
AGR is required. When smoothing.method
is logarithmic
and the AGR is required AGR
must not be NULL
and
deriv
must include one so that the first derivative is
available for calculating it.
When smoothing.method
is direct
, the AGR is the
backtransformed first derivative and so, to obtain it, merely
include 1
in deriv
and any suffix for it in
suffices.deriv
. Leave AGR
set to NULL
.
A character
string that specifies the action to
be taken when values of x
are NA
. The possible
values are fail
, exclude
or omit
.
For exclude
and omit
, predictions and derivatives
will only be obtained for nonmissing values of x
.
The difference between these two codes is that for exclude
the returned data.frame
will have as many rows as
data
, the missing values have been incorporated.
A character
string that specifies the action to
be taken when values of y
, or the response
, are
NA
. The possible values are fail
, exclude
,
omit
, allx
, trimx
, ltrimx
or
rtrimx
. For all options, except fail
, missing
values in y
will be removed before smoothing.
For exclude
and omit
, predictions
and derivatives will be obtained only for nonmissing values of
x
that do not have missing y
values. Again, the
difference between these two is that, only for exclude
will the missing values be incorporated into the
returned data.frame
. For allx
, predictions and
derivatives will be obtained for all nonmissing x
.
For trimx
, they will be obtained for all nonmissing
x
between the first and last nonmissing y
values
that have been ordered for x
; for ltrimx
and
utrimx
either the lower or upper missing y
values, respectively, are trimmed.
allows for arguments to be passed to smooth.spline
.
A data.frame
containing x
and the fitted smooth. The names
of the columns will be the value of x
and the value of response
with .smooth
appended. The number of rows in the data.frame
will be equal to the number of pairs that have neither a missing x
or
response
and it will have the same order of codex as data
.
If deriv
is not NULL
, columns
containing the values of the derivative(s) will be added to the
data.frame
; the name each of these columns will be the value of
response
with .smooth.dvf
appended, where
f
is the order of the derivative, or the value of response
with .smooth.
and the corresponding element of
suffices.deriv
appended. If RGR
is not NULL
, the RGR
is calculated as the ratio of value of the first derivative of the fitted
spline and the fitted value for the spline.
Huang, C. (2001). Boundary corrected cubic smoothing splines. Journal of Statistical Computation and Simulation, 70, 107-121.
splitSplines
, smooth.spline
,
predict.smooth.spline
, splitContGRdiff
# NOT RUN {
data(exampleData)
fit <- fitSpline(longi.dat, response="Area", , x="xDays", df = 4,
deriv=c(1,2), suffices.deriv=c("AGRdv","Acc"))
# }
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