View source: R/correlation.regression.functions.R
regressEns | R Documentation |
This is the primary function for ensemble regression. It will take ensemble values in time and/or values in the predictor (X), and regress them on ensemble values in time and/or values in Y (the predictand). The function will then apply the ensemble linear model to the full length of X to create a modeled Y. Will also optionally create plots.
regressEns(
time.x,
values.x,
time.y,
values.y,
bin.vec = NA,
bin.step = NA,
bin.fun = mean,
max.ens = NA,
percentiles = c(0.025, 0.25, 0.5, 0.75, 0.975),
recon.bin.vec = NA,
min.obs = 10,
gaussianize = TRUE
)
time.x |
matrix of age/time ensembles, or single column |
values.x |
matrix of values ensembles, or single column |
time.y |
matrix of age/time ensembles, or single column |
values.y |
matrix of values ensembles, or single column |
bin.vec |
vector of bin edges for binning step |
bin.step |
spacing of bins, used to build bin step |
bin.fun |
function to use during binning (mean, sd, and sum all work) |
max.ens |
maximum number of ensemble members to regress |
percentiles |
quantiles to calculate for regression parameters |
recon.bin.vec |
bin vector to use for the modeled regression. |
min.obs |
minimum number of points required to calculate regression |
gaussianize |
Boolean flag indicating whether the values should be mapped to a standard Gaussian prior to analysis. |
list of ensemble output
View a full-fledged example of how to use this function.
Nick McKay
Other regress:
plotRegressEns()
,
plotScatterEns()
,
plotTrendLinesEns()
,
regress()
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