rda) for multivariate responses in
repeated observation design. They were originally suggested for
ecological communities. They should be easier to interpret than
traditional constrained ordination. They can also be used to study how
the effects of a factor A depend on the levels of a factor
B, that is A + A:B, in a multivariate response
experiment.prc(response, treatment, time, ...)
## S3 method for class 'prc':
summary(object, axis = 1, scaling = 3, digits = 4, ...)
## S3 method for class 'prc':
plot(x, species = TRUE, select, scaling = 3, axis = 1, type = "l",
xlab, ylab, ylim, lty = 1:5, col = 1:6, pch, legpos, cex = 0.8,
...)prc result object.scaling in scores.rda.TRUE for the selected
species"l" for lines, "p" for points
or "b" for both.legend. A guess is
made if this is not supplied, and NA will suppress legend.rda and returns its
result object (see cca.object). However, a special
summary and plot methods display returns differently
than in rda.treatment must be the
control: use function relevel to guarantee the correct
reference level. The current version will ignore user setting of
contrasts and always use treatment contrasts
(contr.treatment). The time must be an unordered
factor.rda with a single
factor for treatment and a single factor for time points
in repeated observations. In rda model is defined as rda(response ~ treatment *
time + Condition(time)). Since the time appears twice in the
model formula, its main effects will be aliased, and only the main
effect of treatment and interaction terms are available, and will be
used in PRC. Instead of usual multivariate ordination diagrams, PRC
uses canonical (regression) coefficients and species scores for a
single axis. All that the current functions do is to provide a special
summary and plot methods that display the
rda results in the PRC fashion. The current version only
works with default contrasts (contr.treatment) in which
the coefficients are contrasts against the first level, and the levels
must be arranged so that the first level is the control (or a
baseline). If necessary, you must change the baseline level with
function relevel.
Function summary prints the species scores and the
coefficients. Function plot plots coefficients against
time using matplot, and has similar defaults.
The graph (and PRC) is meaningful only if the first treatment
level is the control, as the results are contrasts to the first level
when unordered factors are used. The plot also displays species scores
on the right vertical axis using function
linestack. Typically the number of species is so high
that not all can be displayed with the default settings, but users can
reduce character size or padding (air) in
linestack, or select only a subset of the
species. A legend will be displayed unless suppressed with
legpos = NA, and the functions tries to guess where to put the
legend if legpos is not supplied.
rda, anova.cca.# Chlorpyrifos experiment and experimental design
data(pyrifos)
week <- gl(11, 12, labels=c(-4, -1, 0.1, 1, 2, 4, 8, 12, 15, 19, 24))
dose <- factor(rep(c(0.1, 0, 0, 0.9, 0, 44, 6, 0.1, 44, 0.9, 0, 6), 11))
# PRC
mod <- prc(pyrifos, dose, week)
mod # RDA
summary(mod) # PRC
logabu <- colSums(pyrifos)
plot(mod, select = logabu > 100)
# Permutations should be done only within one week, and we only
# are interested on the first axis
anova(mod, strata = week, first=TRUE, perm.max = 100)Run the code above in your browser using DataLab