View source: R/InferencesMultinom4CA-3.R
fastBoot4CA | R Documentation |
fastBoot4CA
:
create for a Correspondence Analysis
(CA) a Bootstrap Cube
obtained from bootstrapping the observations
from a contingency table.fastBoot4CA
:
Creates Bootstrap cubes for the I and J sets
of a CA. The Bootstrap cubes are
obtained from bootstrapping the entries/cells
of the contingency table.
fastBoot4CA
uses the multinomial distribution
to generate the
Bootstrap samples (function rmultinom
)
fastBoot4CA
uses the transition formula
to get
the values of the column factors.
Gives also the bootstrap eigenvalues
(if eigen = TRUE
).
fastBoot4CA(
X,
Fi = NULL,
Fj = NULL,
delta = NULL,
nf2keep = 3,
nIter = 1000,
critical.value = 2,
eig = FALSE,
alphaLevel = 0.05
)
X |
the data matrix |
Fi |
(default = |
Fj |
(default = |
delta |
(default = |
nf2keep |
How many factors to
keep for the analysis ( |
nIter |
(Default = 1000). Number of Iterations (i.e. number of Bootstrap samples). |
critical.value |
( |
eig |
if TRUE compute bootstrapped confidence intervals (CIs) for the eigenvalues (default is FALSE). |
alphaLevel |
the alpha level to compute confidence intervals for the eigenvalues (with CIS at 1-alpha). Default is .05 |
Note: the rmultinom()
function
cannot handle numbers of observations that are too high
(i.e., roughly larger than 10^9), so if the table total
is larger than 10^8, the table is recoded so that
its sum is roughly equal to 10^8.
Planned development: A compact version that gives only
bootstrap ratios (not the whole brick
BootstrapBricks
).
fastBoot4CA
should be used only
when the data consists in a real
contingency table with a
relatively large N
.
Bootstrap estimates are obtained by creating
bootstrap contingency tables
from a multinomial distribution.
Permutation tests are obtained by creating
contingency tables matching H0
(i.e., multinomial with Pij = Pi*Pj
)
Permutation tests will not work with MCA though
because in MCA a variable is coded
with a set of 0/1 columns (complete disjonctive
coding scheme)—A coding scheme which implies that
the columns are not
independent (because they come in blocks).
a list with 1) bootCube.i
of
Bootstrapped factor scores (I-set)
2)
bootRatios.i
: the bootstrap ratios
(BR)
for
bootRatiosSignificant.i
: the Significant
BRs;
a list with bootCube.j
:
An Items * Dimension * Iteration Brick of
Bootstrapped factor scores (J-set);
bootRatios.j
: the bootstrap ratios (BR);
bootRatiosSignificant.j
: the Significant
BRs;
eigenValues
the nIter
* nL
table
of eigenvalues; eigenCIs
: the CIs for the
eigenvalues.
Hervé Abdi
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