Correspondence Analysis (CA) and a battery of inference tests via InPosition. The battery includes permutation and bootstrap tests.
epCA.inference.battery(
DATA,
DESIGN = NULL,
make_design_nominal = TRUE,
masses = NULL,
weights = NULL,
hellinger = FALSE,
symmetric = TRUE,
graphs = TRUE,
k = 0,
test.iters = 100,
critical.value = 2
)
Returns two lists ($Fixed.Data and $Inference.Data). For
$Fixed.Data, see epCA
, coreCA
for details on the
descriptive (fixed-effects) results.
$Inference.Data returns:
Permutation tests of components. p-values ($p.vals) and distributions of eigenvalues ($eigs.perm) for each component
Bootstrap tests of measures (columns). See
boot.ratio.test
output details.
Permutation tests of components. p-values ($p.val) and distributions of total inertia ($inertia.perm)
original data to perform a CA on.
a design matrix to indicate if rows belong to groups.
a boolean. If TRUE (default), DESIGN is a vector that indicates groups (and will be dummy-coded). If FALSE, DESIGN is a dummy-coded matrix.
a diagonal matrix or column-vector of masses for the row items.
a diagonal matrix or column-vector of weights for the column it
a boolean. If FALSE (default), Chi-square distance will be used. If TRUE, Hellinger distance will be used.
a boolean. If TRUE (default) symmetric factor scores for rows and columns are computed. If FALSE, the simplex (column-based) will be returned.
a boolean. If TRUE (default), graphs and plots are provided
(via epGraphs
)
number of components to return.
number of iterations
numeric. A value, analogous to a z- or t-score to be used to determine significance (via bootstrap ratio).
Derek Beaton, Joseph Dunlop, and Hervé Abdi.
epCA.inference.battery
performs correspondence analysis and inference
tests on a data matrix.
If the expected time to compute the results
(based on test.iters
) exceeds 1 minute, you will be asked (via
command line) if you want to continue.
epCA
, epMCA
,
epMCA.inference.battery
, caChiTest
##warning: this example takes a while to compute. This is why it is reduced.
data(authors)
ca.authors.res <- epCA.inference.battery(authors$ca$data/100)
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