| RsquareAdj {vegan} | R Documentation |
The functions finds the adjusted R-square.
## Default S3 method: RsquareAdj(x, n, m, ...) ## S3 method for class 'rda' RsquareAdj(x, ...) ## S3 method for class 'cca' RsquareAdj(x, permutations = 1000, ...)
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. |
permutations |
Number of permutations to use when computing the adjusted
R-squared for a cca. The permutations can be calculated in parallel by
specifying the number of cores which is passed to |
... |
Other arguments (ignored) except in the case of cca in
which these arguments are passed to |
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
in partial rda, and
R-squared values are available only for gaussian
models in glm.
The adjusted, R-squared of cca is computed using a
permutation approach developed by Peres-Neto et al. (2006). By default 1000
permutations are used.
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.
data(mite) data(mite.env) ## rda m <- rda(decostand(mite, "hell") ~ ., mite.env) RsquareAdj(m) ## cca m <- cca(decostand(mite, "hell") ~ ., mite.env) RsquareAdj(m) ## default method RsquareAdj(0.8, 20, 5)