This methods function returns important matrices etc. of a QO object.
Coef.qrrvglm(object, varI.latvar = FALSE, refResponse = NULL, ...)
A CQO object.
The former has class "qrrvglm".
Logical indicating whether to scale the site scores (latent variables)
to have variance-covariance matrix equal to the rank-TRUE.
See below for further details.
Integer or character. Specifies the reference response or reference species. By default, the reference species is found by searching sequentially starting from the first species until a positive-definite tolerance matrix is found. Then this tolerance matrix is transformed to the identity matrix. Then the sites scores (latent variables) are made uncorrelated. See below for further details.
Currently unused.
The A, B1, C, T, D matrices/arrays
are returned, along with other slots.
The returned object has class "Coef.qrrvglm"
(see Coef.qrrvglm-class).
If I.tolerances=TRUE or eq.tolerances=TRUE (and its
estimated tolerance matrix is positive-definite) then all species'
tolerances are unity by transformation or by definition, and the spread
of the site scores can be compared to them. Vice versa, if one wishes
to compare the tolerances with the sites score variability then setting
varI.latvar=TRUE is more appropriate.
For rank-2 QRR-VGLMs, one of the species can be chosen so that the
angle of its major axis and minor axis is zero, i.e., parallel to
the ordination axes. This means the effect on the latent vars is
independent on that species, and that its tolerance matrix is diagonal.
The argument refResponse allows one to choose which is the reference
species, which must have a positive-definite tolerance matrix, i.e.,
is bell-shaped. If refResponse is not specified, then the code will
try to choose some reference species starting from the first species.
Although the refResponse argument could possibly be offered as
an option when fitting the model, it is currently available after
fitting the model, e.g., in the functions Coef.qrrvglm and
lvplot.qrrvglm.
Yee, T. W. (2004) A new technique for maximum-likelihood canonical Gaussian ordination. Ecological Monographs, 74, 685--701.
Yee, T. W. (2006) Constrained additive ordination. Ecology, 87, 203--213.
cqo,
Coef.qrrvglm-class,
print.Coef.qrrvglm,
lvplot.qrrvglm.
# NOT RUN {
set.seed(123)
x2 <- rnorm(n <- 100)
x3 <- rnorm(n)
x4 <- rnorm(n)
latvar1 <- 0 + x3 - 2*x4
lambda1 <- exp(3 - 0.5 * ( latvar1-0)^2)
lambda2 <- exp(2 - 0.5 * ( latvar1-1)^2)
lambda3 <- exp(2 - 0.5 * ((latvar1+4)/2)^2) # Unequal tolerances
y1 <- rpois(n, lambda1)
y2 <- rpois(n, lambda2)
y3 <- rpois(n, lambda3)
set.seed(111)
# vvv p1 <- cqo(cbind(y1, y2, y3) ~ x2 + x3 + x4, poissonff, trace = FALSE)
# }
# NOT RUN {
lvplot(p1, y = TRUE, lcol = 1:3, pch = 1:3, pcol = 1:3)
# }
# NOT RUN {
# vvv Coef(p1)
# vvv print(Coef(p1), digits=3)
# }
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