RsquareAdj | 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^2
from the
unadjusted R^2
, 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
(also used for distance-based RDA),
cca
, lm
and glm
. Adjusted,
or even unadjusted, R^2
may not be available in
some cases, and then the functions will return NA
.
R^2
values are available only for
gaussian
models in glm
.
The adjusted, R^2
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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