linearFit: Linear regression of a segmented time series

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Linear regression of a segmented time series

Description

Segments the time series in approximately linear regions as defined by the linearSegmentation function and subsequently regressed the specified segment using a selected regression scheme.

Usage

linearFit(x, y, fit=lmsreg, method="widest",
    n.fit=5, angle.tolerance=5, aspect=TRUE)

Arguments

x	the independent variable of the time series.
y	the dependent variable of the time series.
angle.tolerance	the maximum angle in degrees that the running average of the slopes in the current set of points can change relative to the slope of the data calculated in the most current (rightmost) window before a change-point is recorded. Default: 5.
aspect	a logical value. If TRUE, the aspect ratio of the data (defined by range(y) / range(x)) is used to adjust the angle.tolerance. Specifically, the new angle tolerance becomes angle.tolerance / aspect.ratio. Using the aspect ratio to dilate angle.tolerance allows the user to specify the degree of variability they wish to impose on the data vis-a-vis a standard plot of the data, i.e. what you would see if you issued plot(xdata, ydata). The idea is that when looking at such plots, one might decide (for example) that a 5 degree variability on the plot would be acceptable. But if that range of y is vastly different from that of x, then the true change in angle from one section to the other will be much different than 5 degrees. Thus, aspect can be used to compensate for aspect ratios far away from unity. Default: TRUE.
fit	a function representing the linear regression scheme to use in fitting the resulting statistics (on a log-log scale). Supported functions are: lm, lmsreg, and ltsreg. See the on-line help documentation for each of these for more information. Default: lmsreg.
method	a character string used to define the criterion for selecting one of the segments returned by the linearSegmentation function. Choices are "first" The first (leftmost) segment. "last" The last (rightmost) segment. "widest" The segment containing the most number of points. "all" A union of all segments. Default: "widest".
n.fit	an integer denoting the window size, not to exceed the number of samples in the time series. Default: 5.

Value

the regression model.

See Also

logScale.

Examples

## obtain some data with approximately
## piecewise-linear trends
x <- seq(0,2*pi,length=100)
y <- sin(x)

## perform linear segmentation with aspect ratio
## dilation using a 5 degree tolerance and 5
## point windows. regress the widest of these
## segments with the lm() function.
linearFit(x, y, n.fit=5, angle.tolerance=5, aspect=TRUE,
    method="widest", fit=lm)

ifultools documentation built on July 14, 2022, 5:07 p.m.