3. Description
This program does canonical analysis of principal
coordinates (canonical correlation analysis or
canonical discriminant analysis) based on any distance
measure as described by Anderson and Robinson
(2003) and Anderson and Willis (2003). The test is
done by permutation (using the trace and first
canonical root statistics) and canonical axes for
ordination are also given in the output.
4. Characteristics
1. Eigenvalues and eigenvectors from the principal
coordinate analysis. The latter are the PCO axes
that can be used to plot an unconstrained
(metric MDS) of the data.
2. Canonical correlations and squared canonical
correlations
3. Canonical axes scores (position of multivariate
points on the canonical axes to be used for
plotting).
4. Correlations of each of the original variables with
each of the canonical axes.
5. 5. Correlations of each X variable with each of the
canonical axes (if a canonical correlation is done).
6. Diagnostics used to determine the appropriate value
for the choice of m. The criterion used is either the
value of m resulting in the minimum
misclassification error (in the case of groups) or the
minimum residual sum of squares (in the case of X
containing one or more quantitative variables).
Also, m must not exceed p or N and is chosen so that
the proportion of the variability explained by the first
m PCO axes is more than 60%and less than 100% of
the total variability in the original dissimilarity
matrix.
6. 7. In the case of groups, a table of results for the “leave-
one-out” classification of individual observations to
groups is given, along with the misclassification error
for the choice of m used.
8. If requested, the results of a permutation test using
the two different test statistics, (trace and largest
root).
8. Description:
The program offers essentially two options: one can
either ask for a forward selection of individual
variables, or for a forward selection of sets of variables.
The first is useful in the general case, e.g. for fitting
individual environmental variables sequentially in the
linear model. The second is useful for the situation
where one wishes to fit a sequential model of whole
sets of variables. For example, in the paper by
Anderson et al. (2004), there were seven sets of
environmental variables of interest.
9. Characteristics
1. Ambient sediment grain size variables (GS1 – GS4),
2. Depositional environment classification (contrasts
between High, Medium and Low depositional
environments, labeled HvML and MvL).
3. Trapped sediment characteristics (Sdep, gt125, Perfin)
4. Erosion variables (bed height movement, labeled BH and
sdBH)
5. Distance from the mouth of the estuary (D and D2).
6. Chlorophyll a (Chla) and
7. Organics (Ora)
11. We seek to learn to handle various PC programs, to
increase computer skills or the speed of this, we also
learned to distinguish various programs, some of
which are harmful to the pc, as these when they are
installed directly enter and search programs PC Main,
to let this virus, including some come with viruses
already covered, ie programs are disguised virus
(trojans, worms, etc).
We also learned to manage these programs, which
helped us to solve problems of various kinds, various
solutions gave us some shorter than others or more
rapid and more efficient, in short, we facilitated in
many aspects of PC operation.
12. In conclusion to these programs we saw, we learned to
distinguish those harmful to the pc, but others who
help us in different ways, some of us pc facilitates the
management and streamline it, and others help us to
distinguish which programs, files, etc, are harmful or
harmful to the pc.
Thank you for your attention.
