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Last data update: 2014.03.03

R: Fit a piecewise linear model

piecewise.linear

R Documentation

Fit a piecewise linear model

Description

Fit a degree 1 spline with 1 knot point where the location of the knot point is unknown.

Usage

piecewise.linear(x, y, middle = 1, CI = FALSE, 
	bootstrap.samples = 1000, sig.level = 0.05)

Arguments

`x`	Vector of data for the x-axis.
`y`	Vector of data for the y-axis
`middle`	A scalar in [0,1]. This represents the range that the change-point can occur in. 0 means the change-point must occur at the middle of the range of x-values. 1 means that the change-point can occur anywhere along the range of the x-values.
`CI`	Whether or not a bootstrap confidence interval should be calculated.
`bootstrap.samples`	The number of bootstrap samples to take.
`sig.level`	What significance level to use for the confidence intervals.

Details

The bootstrap samples are taken by resampling the raw data points. Often a more appropriate bootstrap sample would be to calculate the residuals and then add a randomly selected residual to each y-value.

Value

A list of 5 elements is returned:

`change.point`	The estimate of α.
`model`	The resulting `lm` object once α is known.
`x`	The x-values used.
`y`	The y-values used.
`CI`	Whether or not the confidence interval was calculated.
`intervals`	If the CIs where calculated, this is a matrix of the upper and lower intervals.

...

Author(s)

Derek Sonderegger

References

Chiu, G. S., R. Lockhart, and R. Routledge. 2006. Bent-cable regression theory and applications. Journal of the American Statistical Association 101:542-553.

Toms, J. D., and M. L. Lesperance. 2003. Piecewise regression: a tool for identifying ecological thresholds. Ecology 84:2034-2041.

Examples

data(Arkansas)
x <- Arkansas$year
y <- Arkansas$sqrt.mayflies

model <- piecewise.linear(x,y, CI=FALSE)
plot(model)
print(model)
predict(model, 2001)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(SiZer)
Loading required package: splines
Loading required package: boot
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/SiZer/piecewise.linear.Rd_%03d_medium.png", width=480, height=480)
> ### Name: piecewise.linear
> ### Title: Fit a piecewise linear model
> ### Aliases: piecewise.linear piecewise.linear.simple
> ###   logLik.PiecewiseLinear predict.PiecewiseLinear plot.PiecewiseLinear
> ###   print.PiecewiseLinear piecewise.linear.likelihood
> ### Keywords: regression
> 
> ### ** Examples
> 
> data(Arkansas)
> x <- Arkansas$year
> y <- Arkansas$sqrt.mayflies
> 
> model <- piecewise.linear(x,y, CI=FALSE)
> plot(model)
> print(model)
[1] "Threshold alpha: 1996.72364605122"
[1] ""
[1] "Model coefficients: Beta[0], Beta[1], Beta[2]"
 (Intercept)            x            w 
-2301.334060     1.158305    -1.325665 
> predict(model, 2001)
(Intercept) 
   10.76505 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>