calibrate {VGAM} | R Documentation |
calibrate
is a generic function used to produce calibrations
from various model fitting functions. The function invokes
particular `methods' which depend on the `class' of the first
argument.
calibrate(object, ...)
object |
An object for which a calibration is desired. |
... |
Additional arguments affecting the calibration produced.
Usually the most important argument in ... is
newdata which, for calibrate , contains new
response data, Y, say. |
Given a regression model with explanatory variables X and
response Y,
calibration involves estimating X from Y using the
regression model.
It can be loosely thought of as the opposite of predict
(which takes an X and returns a Y.)
In general, given a new response Y, the explanatory
variables X are returned.
However, for constrained ordination models such as CQO and CAO models,
it is usually not possible to return X, so the latent
variables are returned instead (they are
linear combinations of the X).
See the specific calibrate
methods functions to see
what they return.
This function was not called predictx
because of the
inability of constrained ordination models to return X;
they can only return the latent variable values (site scores) instead.
T. W. Yee
data(hspider) hspider[,1:6] = scale(hspider[,1:6]) # Standardized environmental vars set.seed(123) p1 = cao(cbind(Pardlugu, Pardmont, Pardnigr, Pardpull, Zoraspin) ~ WaterCon + BareSand + FallTwig + CoveMoss + CoveHerb + ReflLux, family = poissonff, data = hspider, Rank = 1, df1.nl = c(Zoraspin=2, 1.9), Bestof = 3, Crow1positive = TRUE) siteNos = 1:2 # Calibrate these sites cp1 = calibrate(p1, new=data.frame(p1@y[siteNos,]), trace=TRUE) ## Not run: # Graphically compare the actual site scores with their calibrated values persp(p1, main="Solid=actual, dashed=calibrated site scores", label=TRUE, col="blue", las=1) abline(v=lv(p1)[siteNos], lty=1, col=1:length(siteNos)) # actual site scores abline(v=cp1, lty=2, col=1:length(siteNos)) # calibrated values ## End(Not run)