Adjusted R-square
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
R-squared in partial ordination, 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 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)
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