SiZer | R Documentation |

Calculates the SiZer map from a given set of X and Y variables.

SiZer( x, y, h = NA, x.grid = NA, degree = NA, derv = 1, grid.length = 41, quiet = TRUE )

`x` |
data vector for the independent axis |

`y` |
data vector for the dependent axis |

`h` |
An integer representing how many bandwidths should be considered, or vector of length 2 representing the upper and lower limits h should take, or a vector of length greater than two indicating which bandwidths to examine. |

`x.grid` |
An integer representing how many bins to use along the x-axis, or a vector of length 2 representing the upper and lower limits the x-axis should take, or a vector of length greater than two indicating which x-values the derivative should be evaluated at |

`degree` |
The degree of the local weighted polynomial used to smooth the data.
This must be greater than or equal to |

`derv` |
The order of derivative for which to make the SiZer map. |

`grid.length` |
The default length of the |

`quiet` |
Should diagnostic messages be suppressed? Defaults to TRUE. |

SiZer stands for the Significant Zero crossings of the derivative. There are two dominate approaches in smoothing bivariate data: locally weighted regression or penalized splines. Both approaches require the use of a 'bandwidth' parameter that controls how much smoothing should be done. Unfortunately there is no uniformly best bandwidth selection procedure. SiZer (Chaudhuri and Marron, 1999) is a procedure that looks across a range of bandwidths and classifies the p-th derivative of the smoother into one of three states: significantly increasing (blue), possibly zero (purple), or significantly negative (red).

Returns list object of type SiZer which has the following components:

- x.grid
Vector of x-values at which the derivative was evaluated.

- h.grid
Vector of bandwidth values for which a smoothing function was calculated.

- slopes
Matrix of what category a particular x-value and bandwidth falls into (Increasing=1, Possibly Zero=0, Decreasing=-1, Not Enough Data=2).

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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