Description Usage Arguments Details Value References See Also Examples
The functions finds the adjusted R-square.
1 2 3 4 | ## Default S3 method:
RsquareAdj(x, n, m, ...)
## S3 method for class 'rda'
RsquareAdj(x, ...)
|
x |
Unadjusted R-squared or an object from which the terms for evaluation or adjusted R-squared can be found. |
n, m |
Number of observations and number of degrees of freedom in the fitted model. |
... |
Other arguments (ignored). |
The default method finds the adjusted
R-squared from the unadjusted R-squared, number of observations, and
number of degrees of freedom in the fitted model. The specific
methods find this information from the fitted result
object. There are specific methods for rda
,
cca
, lm
and glm
. Adjusted,
or even unadjusted, R-squared may not be available in some cases,
and then the functions will return NA
. There is no adjusted
R-squared in cca
, in partial rda
, and
R-squared values are available only for gaussian
models in glm
.
The raw R-squared of partial rda
gives the
proportion explained after removing the variation due to conditioning
(partial) terms; Legendre et al. (2011) call this semi-partial
R-squared. The adjusted R-squared is found as
the difference of adjusted R-squared values of joint effect
of partial and constraining terms and partial term alone, and it is
the same as the adjusted R-squared of component
[a] = X1|X2
in two-component variation partition in
varpart
.
The functions return a list of items r.squared
and
adj.r.squared
.
Legendre, P., Oksanen, J. and ter Braak, C.J.F. (2011). Testing the significance of canonical axes in redundancy analysis. Methods in Ecology and Evolution 2, 269–277.
Peres-Neto, P., P. Legendre, S. Dray and D. Borcard. 2006. Variation partitioning of species data matrices: estimation and comparison of fractions. Ecology 87, 2614–2625.
varpart
uses RsquareAdj
.
1 2 3 4 5 6 7 | data(mite)
data(mite.env)
## rda
m <- rda(decostand(mite, "hell") ~ ., mite.env)
RsquareAdj(m)
## default method
RsquareAdj(0.8, 20, 5)
|
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