blockmaxxer | R Documentation |
Find the block maximum of a data set.
blockmaxxer(x, ...)
## S3 method for class 'data.frame'
blockmaxxer(x, ..., which = 1, blocks = NULL,
blen = NULL, span = NULL)
## S3 method for class 'fevd'
blockmaxxer(x, ...)
## S3 method for class 'matrix'
blockmaxxer(x, ..., which = 1, blocks = NULL,
blen = NULL, span = NULL)
## S3 method for class 'vector'
blockmaxxer(x, ..., blocks = NULL, blen = NULL,
span = NULL)
x |
An object of class “fevd” where the fit is for a PP model, a numeric vector, matrix or data frame. |
... |
optional arguments to |
which |
number or name of a column indicating for which column to take the block maxima. Note, this does not take componentwise maxima (as in the multivariate setting). Instead, it takes the maxima for a single column and returns a vector, data frame or matrix of the block maxima for that column along with the entire row where that maxima occurred. |
blocks |
numeric (integer or factor) vector indicating the blocks over which to take the maxima. Must be non-NULL if |
blen |
(optional) may be used instead of the |
span |
(optional) must be specified if |
vector of length equal to the number of blocks (vector method) or a matrix or data frame with number of rows equal to the number of blocks (matrix and data frame methods).
The fevd
method is for finding the block maxima of the data passed to a PP model fit and the blocks are determined by the npy
and span
components of the fitted object. If the fevd
object is not a PP model, the function will error out. This is useful for utilizing the PP model in the GEV with approximate annual maxima. Any covariate values that occur contiguous with the maxima are returned as well.
The aggregate
function is used with max
in order to take the maxima from each block.
Eric Gilleland
fevd
, max
, aggregate
data(Fort)
bmFort <- blockmaxxer(Fort, blocks = Fort$year, which="Prec")
plot(Fort$year, Fort$Prec, xlab = "Year", ylab = "Precipitation (inches)",
cex = 1.25, cex.lab = 1.25,
col = "darkblue", bg = "lightblue", pch = 21)
points(bmFort$year, bmFort$Prec, col="darkred", cex=1.5)
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