fitSpline {imageData}

Produce the fits from a natural cubic smoothing spline applied to a response in a data.frame

Description

Uses smooth.spline to fit a spline to all the values of response stored in data.

The amount of smoothing can be controlled by df. 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 Relative Growth Rate (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.

Usage

fitSpline(data, response, x, df=NULL, smoothing.scale = "identity", 
          correctBoundaries = FALSE, 
          deriv=NULL, suffices.deriv=NULL, RGR=NULL, AGR=NULL, 
          na.x.action="exclude", na.y.action = "exclude", ...)

Arguments

data

A data.frame containing the column to be smoothed.

response

A character giving the name of the column in data that is to be smoothed.

x

A character giving the name of the column in data that contains the values of the predictor variable.

df

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.

smoothing.scale

A character giving the scale on which smoothing is to be performed. The two possibilites are "identity", for directly smoothing the observed response, and "logarithmic", for scaling the log-transformed response.

correctBoundaries

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.

deriv

A numeric specifying one or more orders of derivatives that are required.

suffices.deriv

A character giving the characters to be appended to the names of the derivatives.

RGR

A character giving the character to be appended to the smoothed response to create the RGR name, but only when smoothing.scale is identity. When smoothing.scale is identity: (i) if RGR is not NULL deriv must include 1 so that the first derivative is available for calculating the RGR; (ii) if RGR is NULL, the RGR is not calculated from the AGR. When smoothing.scale 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.

AGR

A character giving the character to be appended to the smoothed response to create the AGR name, but only when smoothing.scale is logarithmic. When smoothing.scale is logarithmic: (i) if AGR is not NULL, deriv must include 1 so that the first derivative is available for calculating the AGR; (ii) If AGR is NULL, the AGR is not calculated from the RGR. When smoothing.scale is identity, the AGR is the first derivative and so, to obtain it, merely include 1 in deriv and any suffix for it in suffices.deriv.

na.x.action

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.

na.y.action

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.

Value

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.

Author(s)

Chris Brien

References

Huang, C. (2001). Boundary corrected cubic smoothing splines. Journal of Statistical Computation and Simulation, 70, 107-121.

See Also

splitSplines, smooth.spline, predict.smooth.spline, splitContGRdiff

Examples

data(exampleData)
fit <- fitSpline(longi.dat, response="Area", , x="xDays", df = 4,
                 deriv=c(1,2), suffices.deriv=c("AGRdv","Acc"))