calibrate.qrrvglm.control {VGAM} | R Documentation |
Algorithmic constants and parameters for running
calibrate.qrrvglm
are set using this function.
calibrate.qrrvglm.control(object, trace = FALSE, Method.optim = "BFGS", gridSize = if (Rank == 1) 9 else 5, varlvI = FALSE, ...)
object |
The fitted CQO/UQO/CAO model. The user should ignore this argument.
|
trace |
Logical indicating if output should be produced for each iteration. It
is a good idea to set this argument to be TRUE since the
computations are expensive.
|
Method.optim |
Character. Fed into the method argument of
optim .
|
gridSize |
Numeric, recycled to length Rank . Controls the resolution
of the grid used for initial values. For each latent variable,
an equally spaced grid of length gridSize is cast from the
smallest site score to the largest site score. Then the likelihood
function is evaluated on the grid, and the best fit is chosen as the
initial value. Thus increasing the value of gridSize increases
the chance of obtaining the global solution, however, the computing
time increases proportionately.
|
varlvI |
Logical. For CQO objects only, this argument is fed into
Coef.qrrvglm .
|
... |
Avoids an error message for extraneous arguments.
|
Most CQO/CAO users will only need to make use of trace
and gridSize
. These arguments should be used inside their
call to calibrate.qrrvglm
, not this function
directly.
A list which with the following components.
trace |
Numeric (even though the input can be logical). |
gridSize |
Positive integer. |
varlvI |
Logical. |
Despite the name of this function, UQO and CAO models are handled as well.
T. W. Yee
Yee, T. W. (2005) On constrained and unconstrained quadratic ordination. Manuscript in preparation.
calibrate.qrrvglm
,
Coef.qrrvglm
.
data(hspider) hspider[,1:6] = scale(hspider[,1:6]) # Needed when ITol = TRUE set.seed(123) p1 = cqo(cbind(Alopacce, Alopcune, Pardlugu, Pardnigr, Pardpull, Trocterr, Zoraspin) ~ WaterCon + BareSand + FallTwig + CoveMoss + CoveHerb + ReflLux, family = poissonff, data = hspider, ITol = TRUE) sort(p1@misc$deviance.Bestof) # A history of all the iterations 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="Site scores: solid=actual, dashed=calibrated", 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)