Get standard errors for log-multiplicative association scores and intrinsic association coefficients.
se(x, ...)# S3 method for assoc
se(x, type = c("se", "quasi.se"), ...)
# S3 method for rc
se(x, type = c("se", "quasi.se"), ...)
# S3 method for hmskew
se(x, type = c("se", "quasi.se"), ...)
# S3 method for yrcskew
se(x, type = c("se", "quasi.se"), ...)
# S3 method for rcL
se(x, type = c("se", "quasi.se"), ...)
the type of standard errors to be computed (see “Details” below).
currently unused.
An object of the same form as the assoc component of the model, but with standard errors rather than
the corresponding coefficients.
Currently, only jackknife or bootstrap standard errors are supported, depending on the se argument
passed when fitting the model. Some care is needed before using such standard errors and confidence
intervals. First one must ensure all model replicates converged to a correct solution, especially for bootstrap;
second, when relying on normal confidence intervals computed from these standard errors, one must ensure that the
coefficients estimators follow a normal distribution. Both checks can be performed by calling
plot.boot on the boot.results component of the assoc object of the models (not
supported for jackknife), with the index argument identifying the coefficient of interest (call colnames
on the t member of the boot.results object to find out the index you need).
If outliers are present, standard errors and confidence intervals will be artificially large; to fix this, the
tolerance argument must be set to a smaller value when fitting the models (which may in turn require
increasing the value of the iterMax argument if convergence is too slow). Once outliers are removed, if
coefficient estimates are still not normally distributed, robust bootstrap confidence intervals can be computed
using boot.ci on the same object, provided a large number of replicates (> 1000) were computed.
For each replicate, stable scores and intrinsic association coefficients are identified using an orthogonal
Procrustes analysis to suppress meaningless variations due to random reflections, permutations and rotations of
dimensions (Milan & Whittaker, 1995). For hmskew and hmskewL models, a rotation within
each pair of dimensions and a permutation of pairs of dimensions is performed, but no reflection as it would change
the sign of intrinsic association coefficients.
Quasi-standard errors are computed using qvcalc. See the help page for this function for
details and references about them.
Milan, L., and J. Whittaker (1995). Application of the Parametric Bootstrap to Models that Incorporate a Singular Value Decomposition. Journal of the Royal Statistical Society. Series C (Applied Statistics) 44(1), 31-49.
# NOT RUN {
# See ?rc about Wong (2010)
# }
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