getconditionals.RdThis will obtain responses to each predictor variable across its range of observed values, conditional on all other predictor variables being set to 0 (their mean value, if data are scaled properly). If automatic scaling is used, predictors and responses are backtransformed to their original scale. If the model includes multiple populations, responses are obtained for each population.
getconditionals(fit, xrange = "default", extrap = 0.01, nvals = 25, plot = T)Output from fitGP.
The range of scaled predictor values over which responses
should be evaluated. Options are "local" (the range observed within
each population), or "global" (the range across all populations).
Irrelevant if there is only one population. The default ("default")
will be "local" if scaling="local" was used, and
"global" if scaling="global" or scaling="none" was
used. This option can useful for either preventing or allowing for
extrapolation beyond the range of the data in a given population.
Percentage to extrapolate beyond the predictor range for each
population (i.e. predictor values will range from (min(X)-range(X)*extrap
to (max(X)+range(X)*extrap Defaults to 0.01.
The number of values to evaluate across the predictor range for each population. Defaults to 25, which should be fine for most applications.
Produce a plot, or not (logical, defaults to TRUE).
Returns (invisibly) a data frame containing the predictor values and conditional responses (means and standard deviations).