calibrate.qrrvglm {VGAM} | R Documentation |
Performs maximum likelihood calibration for constrained and unconstrained quadratic and additive ordination models (CQO and CAO models are better known as QRR-VGLMs and RR-VGAMs respectively).
calibrate.qrrvglm(object, newdata = NULL, type=c("lv","predictors","response","vcov","all3or4"), initial.vals = NULL, ...)
object |
The fitted CQO/CAO model. |
newdata |
A data frame with new response data (usually new species data). The default is to use the original data used to fit the model; however, the calibration may take a long time to compute because the computations are expensive. |
type |
What type of result is to be returned.
The first are the calibrated latent variables or site scores.
This must be computed always.
The "predictors" are the linear/quadratic or additive
predictors evaluated at the calibrated latent variables or site
scores.
The "response" are the fitted means evaluated at the
calibrated latent variables or site scores.
The "vcov" are the estimated variance-covariance
matrices of the
calibrated latent variables or site scores.
The "all3or4" is for all of them, i.e., all type s.
For CAO models,
"vcov" is unavailable, so all 3 are returned.
For CQO models,
"vcov" is available, so all 4 are returned.
|
initial.vals |
Initial values for the search.
For rank-1 models, this should be a vector of length
nrow(newdata) , and for rank 2 models
this should be a two column matrix with the number of rows equalling
the number of rows in newdata .
The default is a grid defined by arguments in
calibrate.qrrvglm.control .
|
... |
Arguments that are fed into
calibrate.qrrvglm.control . |
Given a fitted regression CQO/CAO model, maximum likelihood calibration is theoretically easy and elegant. However, the method assumes that all species are independent, which is not really true in practice. More details and references are given in Yee (2005).
The function optim
is used to search for
the maximum likelihood solution. Good initial values are
needed, and calibrate.qrrvglm.control
allows the user some control over the choice of these.
The argument type
determines what is returned.
If type="all3or4"
then all the type
values are returned
in a list, with the following components.
Each component has length nrow(newdata)
.
lv |
Calibrated latent variables or site scores. |
predictors |
linear/quadratic or additive predictors. For example, for Poisson families, this will be on a log scale, and for binomial families, this will be on a logit scale. |
response |
Fitted values of the response, evaluated at the calibrated latent variables or site scores. |
vcov |
Estimated variance-covariance matrix of the
calibrated latent variables or site scores.
Actually, these are stored in an array whose last dimension
is nrow(newdata) .
|
This function is computationally expensive.
Setting trace=TRUE
to get a running log is a good idea.
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.
ter Braak, C. J. F. 1995. Calibration. In: Data Analysis in Community and Landscape Ecology by Jongman, R. H. G., ter Braak, C. J. F. and van Tongeren, O. F. R. (Eds.) Cambridge University Press, Cambridge.
calibrate.qrrvglm.control
,
calibrate
,
cqo
,
uqo
,
cao
.
data(hspider) hspider[,1:6] = scale(hspider[,1:6]) # Standardize the environmental variables set.seed(123) p1 = cqo(cbind(Pardlugu, Pardmont, Pardnigr, Pardpull, Zoraspin) ~ WaterCon + BareSand + FallTwig + CoveMoss + CoveHerb + ReflLux, family = poissonff, data = hspider, Rank = 1, IToler = TRUE, 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="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)