cca
,
rda
, capscale
) as a multiple response
linear model (lm
). This allows finding influence
statistics (influence.measures
). This also allows
deriving several other statistics, but most of these are biased and
misleading, since refitting ignores a major component of variation in
constrained ordination.as.mlm(x)
"mlm"
documented with lm
.lm
). This regression ignores residual
unconstrained variation in the data, and therefore estimates of
standard error are strongly biased and much too low. You can get
statistics like $t$-values of coefficients, but you should not use
these because of this bias. Some useful information you can get with
refitted models are statistics for detecting influential observations
(influence.measures
including
cooks.distance
, hatvalues
).cca
, rda
, capscale
,
cca.object
, lm
, summary.mlm
,
influence.measures
.data(varespec)
data(varechem)
mod <- cca(varespec ~ Al + P + K, data=varechem)
lmod <- as.mlm(mod)
## Coefficients
lmod
coef(mod)
## Influential observations
influence.measures(lmod)
plot(mod, type = "n")
points(mod, cex = 10*hatvalues(lmod), pch=16, xpd = TRUE)
text(mod, display = "bp", col = "blue")
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