GO1 | R Documentation |
The functions fit unconstrained maximum likelihood ordination with unit-width Gaussian response models.
GO1(comm, tot = max(comm), freqlim = 5, parallel = 1, trace = TRUE, ...)
GO(comm, k = 1, tot = max(comm), freqlim = 5, family = c("poisson",
"binomial"), far = 10, init, trace = TRUE, ...)
metaGO(comm, k = 1, trymax = 3, firstOK = TRUE, trace = FALSE, ...)
## S3 method for class 'GO'
plot(x, choices = 1, label = FALSE, marginal = FALSE,
cex = 0.7, col = 1:6, ...)
## S3 method for class 'GO'
anova(object, ...)
spanodev(mod1, mod2 = NULL, ...)
## S3 method for class 'GO'
predict(object, newdata, type = c("response", "link"), ...)
## S3 method for class 'GO'
calibrate(object, newdata, ...)
comm |
Community data frame. |
tot |
Total abundance used in Binomial models. This can be
either a single value for all data, or a vector with value for each
row of |
freqlim |
Minimum number of occurrence for analysed species. |
parallel |
Number of parallel processes. |
trace |
logical; print information on the progress of the analysis. |
k |
Number of estimated gradients (axes). |
family |
Error distribution. Can be either |
far |
Threshold distance for species optima regarded as alien and frozen in fitting. |
init |
Initial configuration to start the iterations. This
should be a matrix, and number of rows should match the community
data, and number coluns the number of gradients ( |
trymax |
Maximum number of random starts. |
firstOK |
Do not launch random starts if default start succeeds. |
x |
Fitted model. |
choices |
The axis or axes plotted. |
label |
Label species responses. |
marginal |
Plot marginal responses or slice through origin of other dimensions. |
cex |
Character size for |
col |
Colours of response curves. |
object |
Ordination result object. |
mod1 , mod2 |
Compared result objects |
newdata |
New gradient locations to |
type |
Predictions in the scale of responses or in the scale of link function. |
... |
Other parameters passed to functions. In |
Function is under development and unreleased. It will be released
under different name in vegan. The current version is only
provided for review purposes. The function and its support functions
require vegan, although this requirements is not explicitly
made. The optimization is based on nlm
function, and passes
arguments to this function.
Function anova
can analyse two nested models or a single model
against null model of flat responses using parametric tests based on
quasi-Likelihood. Function spanodev
performs similar test
split by species. Function predict
returns estimated response
curves, and newdata
can be gradient locations. Function
calibrate
returns estimated gradient locations, and newdata
can be community data.
The plot
function displays fitted respose curves against one
ordination axis. In principle, the ordination can be rotated using
vegan function MDSrotate
, but this requires
a version that agrees to analyse GO
results. Traditional
ordination plots of SU scores and species optima can be displayed
with ordiplot
(vegan package). The function
is written so that several other vegan and standard R functions
can handle results.
GO1
: Alternating estimation of species parameters and
gradient locations in one dimension.
GO
: Simultaneous estimation of species parameters and
gradient locations.
metaGO
: Start GO several times from random configurations if
default start fails or optionally always
spanodev
: Comparison of goodness of fit for individual species.
Jari Oksanen
cgo
in VGAM package.
library(vegan) ## *must* be given before using the function
data(varespec)
mod <- GO(varespec, k=2, far=5, tot=100, family="binomial", iterlim=1000)
plot(mod, label=TRUE)
ordiplot(mod, type="t")
ordiplot(mod, dis="si", type="t")
anova(mod)
mod1 <- update(mod, k=1)
anova(mod1, mod)
spanodev(mod1)
spanodev(mod1, mod)
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