plot.SiZer | R Documentation |
SiZer
object that was created using SiZer()
Plot a SiZer map
Plot a SiZer
object that was created using SiZer()
## S3 method for class 'SiZer' plot( x, ylab = expression(log[10](h)), colorlist = c("red", "purple", "blue", "grey"), ggplot2 = FALSE, ... )
x |
An object created using |
ylab |
What the y-axis should be labled. |
colorlist |
What colors should be used. This is a vector that corresponds to 'decreasing', 'possibley zero', 'increasing', and 'insufficient data'. |
ggplot2 |
Should the graphing be done using 'ggplot2'? Defaults to FALSE for backwards compatibility. |
... |
Any other parameters to be passed to the function |
The white lines in the SiZer map give a graphical representation of the bandwidth. The horizontal distance between the lines is 2h.
Derek Sonderegger
Chaudhuri, P., and J. S. Marron. 1999. SiZer for exploration of structures in curves. Journal of the American Statistical Association 94:807-823.
Hannig, J., and J. S. Marron. 2006. Advanced distribution theory for SiZer. Journal of the American Statistical Association 101:484-499.
Sonderegger, D.L., Wang, H., Clements, W.H., and Noon, B.R. 2009. Using SiZer to detect thresholds in ecological data. Frontiers in Ecology and the Environment 7:190-195.
plot.SiZer
, locally.weighted.polynomial
data('Arkansas') x <- Arkansas$year y <- Arkansas$sqrt.mayflies plot(x,y) # Calculate the SiZer map for the first derivative SiZer.1 <- SiZer(x, y, h=c(.5,10), degree=1, derv=1, grid.length=21) plot(SiZer.1) plot(SiZer.1, ggplot2=TRUE) # Calculate the SiZer map for the second derivative SiZer.2 <- SiZer(x, y, h=c(.5,10), degree=2, derv=2, grid.length=21); plot(SiZer.2) # By setting the grid.length larger, we get a more detailed SiZer # map but it takes longer to compute. # # SiZer.3 <- SiZer(x, y, h=c(.5,10), grid.length=100, degree=1, derv=1) # plot(SiZer.3)
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