cqo(formula, family, data = list(), weights = NULL, subset = NULL,
na.action = na.fail, etastart = NULL, mustart = NULL,
coefstart = NULL, control = qrrvglm.control(...), offset = NULL,
method = "cqo.fit", model = FALSE, x.arg = TRUE, y.arg = TRUE,
contrasts = NULL, constraints = NULL, extra = NULL,
smart = TRUE, ...)"qrrvglm".
Note that the slot misc has a list component called
deviance.Bestof which gives the history of deviances over all
the iterations.Bestof in qrrvglm.control.
For reproducibility of the results, it pays to set a different
random number seed before calling cqo (the function
set.seed does this). The function cqo
chooses initial values for C using .Init.Poisson.QO()
if Use.Init.Poisson.QO=TRUE, else random numbers. Unless ITolerances=TRUE or EqualTolerances=FALSE,
CQO is computationally expensive. It pays to keep the rank down to 1
or 2. If EqualTolerances=TRUE and ITolerances=FALSE then
the cost grows quickly with the number of species and sites (in terms of
memory requirements and time). The data needs to conform quite closely
to the statistical model, and the environmental range of the data should
be wide in order for the quadratics to fit the data well (bell-shaped
response surfaces). If not, RR-VGLMs will be more appropriate because
the response is linear on the transformed scale (e.g., log or logit)
and the ordination is called constrained linear ordination or CLO.
Like many regression models, CQO is sensitive to outliers (in the environmental and species data), sparse data, high leverage points, multicollinearity etc. For these reasons, it is necessary to examine the data carefully for these features and take corrective action (e.g., omitting certain species, sites, environmental variables from the analysis, transforming certain environmental variables, etc.). Any optimum lying outside the convex hull of the site scores should not be trusted. Fitting a CAO is recommended first, then upon transformations etc., possibly a CQO can be fitted.
For binary data, it is necessary to have `enough' data. In general,
the number of sites $n$ ought to be much larger than the number of
species S, e.g., at least 100 sites for two species. Compared
to count (Poisson) data, numerical problems occur more frequently
with presence/absence (binary) data. For example, if Rank=1
and if the response data for each species is a string of all absences,
then all presences, then all absences (when enumerated along the latent
variable) then infinite parameter estimates will occur. In general,
setting ITolerances=TRUE may help.
This function was formerly called cgo. It has been renamed to
reinforce a new nomenclature described in Yee (2006).
lv for $R=1$
else lv1, lv2, etc. in the output). Here, $R$
is the rank or the number of ordination axes. Each species'
response is then a regression of these latent variables using quadratic
polynomials on a transformed scale (e.g., log for Poisson counts, logit
for presence/absence responses). The solution is obtained iteratively
in order to maximize the log-likelihood function, or equivalently,
minimize the deviance.
The central formula (for Poisson and binomial species data) is
given by
$$\eta = B_1^T x_1 + A \nu +
\sum_{m=1}^M (\nu^T D_m \nu) e_m$$
where $x_1$ is a vector (usually just a 1 for an intercept),
$x_2$ is a vector of environmental variables, $\nu=C^T
x_2$ is a $R$-vector of latent variables, $e_m$ is
a vector of 0s but with a 1 in the $m$th position.
The $\eta$ are a vector of linear/additive predictors,
e.g., the $m$th element is $\eta_m = \log(E[Y_m])$ for the $m$th species. The matrices $B_1$,
$A$, $C$ and $D_m$ are estimated from the data, i.e.,
contain the regression coefficients. The tolerance matrices
satisfy $T_s = -\frac12 D_s^{-1}$.
Many important CQO details are directly related to arguments
in qrrvglm.control, e.g., the argument Norrr
specifies which variables comprise $x_1$.
Theoretically, the four most popular cqo correspond to the Poisson, binomial,
normal, and negative binomial distributions. The latter is a
2-parameter model. All of these are implemented, as well as the
2-parameter gamma. The Poisson is or should be catered for by
quasipoissonff and poissonff, and the
binomial by quasibinomialff and binomialff.
Those beginning with "quasi" have dispersion parameters that
are estimated for each species.
For initial values, the function .Init.Poisson.QO should
work reasonably well if the data is Poisson with species having equal
tolerances. It can be quite good on binary data too. Otherwise the
Cinit argument in qrrvglm.control can be used.
It is possible to relax the quadratic form to an additive model. The
result is a data-driven approach rather than a model-driven approach,
so that CQO is extended to constrained additive ordination
(CAO) when $R=1$. See cao for more details.
In this documentation, $M$ is the number of linear predictors, $S$ is the number of responses (species). Then $M=S$ for Poisson and binomial species data, and $M=2S$ for negative binomial and gamma distributed species data.
ter Braak, C. J. F. and Prentice, I. C. (1988) A theory of gradient analysis. Advances in Ecological Research, 18, 271--317. Yee, T. W. (2006) Constrained additive ordination. Ecology, 87, 203--213.
qrrvglm.control,
Coef.qrrvglm,
predict.qrrvglm,
rcqo,
cao,
uqo,
rrvglm, poissonff,
binomialff,
negbinomial,
gamma2,
lvplot.qrrvglm,
persp.qrrvglm,
trplot.qrrvglm, vglm,
set.seed,
hspider.Documentation accompanying the
# Example 1; Fit an unequal tolerances model to the hunting spiders data
hspider[,1:6]=scale(hspider[,1:6]) # Standardize the environmental variables
set.seed(1234)Run the code above in your browser using DataLab