calc.SE.rowcluster {clustord} | R Documentation |
Calculate standard errors of clustering parameters.
Description
Calculate SE of parameters fitted using clustord
.
Usage
calc.SE.rowcluster(long.df, clust.out, optim.control = default.optim.control())
calc.SE.bicluster(long.df, clust.out, optim.control = default.optim.control())
Arguments
long.df |
The data frame, in long format, as passed to |
clust.out |
A |
optim.control |
control list for the |
Details
Use calc.SE.rowcluster
to calculate SE for row clustering and column
clustering, or calc.SE.bicluster
to calculate SE for biclustering.
Calculates SE by running optimHess
(see optim
) on
the incomplete-data log-likelihood to find the hessian at the fitted parameter
values from clustord
.
Then the square roots of the diagonal elements of the negative inverse of the
hessian are the standard errors of the parameters
i.e. SE <- sqrt(diag(solve(-optim.hess))
.
Note that SE values are only calculated for the independent parameters. For example, if the constraint on the row clustering parameters is set to constraint_sum_zero = TRUE, where the last row clustering parameter is the negative sum of the other parameters, SE values will only be calculated for the first RG-1 parameters, the independent ones. This applies similarly to individual column effect coefficients, etc.
The function requires an input which is the output of
clustord
, which includes the component outvect
, the
final vector of independent parameter values from the EM algorithm, which
will correspond to a subset of the parameter values in parlist.out
.
Value
The standard errors corresponding to the elements of clust.out$outvect
.
Functions
-
calc.SE.rowcluster()
: SE for rowclustering -
calc.SE.bicluster()
: SE for biclustering