EM_algo_c {iotarelr} | R Documentation |
Parameter estimation via EM Algorithm with Condition Stage
Description
Function written in C++
for estimating the parameters of the model
via Expectation Maximization (EM Algorithm).
Usage
EM_algo_c(
obs_pattern_shape,
obs_pattern_frq,
obs_internal_count,
categorical_levels,
random_starts,
max_iterations,
rel_convergence,
con_step_size,
con_random_starts,
con_max_iterations,
con_rel_convergence,
fast,
trace,
con_trace
)
Arguments
obs_pattern_shape |
Matrix containing the unique patterns found
in the data. Ideally this matrix is generated by the function
get_patterns() .
|
obs_pattern_frq |
Vector containing the frequencies of the
patterns. Ideally it is generated by the the function
get_patterns() .
|
obs_internal_count |
Matrix containing the relative frequencies
of each category within each pattern. Ideally this matrix is generated by
the function get_patterns() .
|
categorical_levels |
Vector containing all possible categories of
the content analysis.
|
random_starts |
Integer for determining how often the algorithm
should restart with randomly chosen values for the Assignment Error Matrix
and the categorical sizes.
|
max_iterations |
Integer for determining the maximum number of iterations
for each random start.
|
rel_convergence |
Double for determining the convergence criterion. The
algorithm stops if the relative change is smaller than this criterion.
|
con_step_size |
Double for specifying the size for increasing or
decreasing the probabilities during the condition stage of estimation.
This value should not be less than 1e-3.
|
con_random_starts |
Integer for the number of random starts
within the condition stage.
|
con_max_iterations |
Integer for the maximum number of iterations
during the condition stage.
|
con_rel_convergence |
Double for determining the convergence
criterion during condition stage. The algorithm stops if the relative change
is smaller than this criterion.
|
fast |
Bool If TRUE a fast estimation is applied during the
condition stage. This option ignores all parameters beginning with "con_".
If FALSE the estimation described in Berding and
Pargmann (2022) is used. Default is TRUE .
|
trace |
TRUE for printing progress information on the console.
FALSE if this information should not be printed.
|
con_trace |
TRUE for printing progress information on the console
during estimations in the condition stage. FALSE if this information
should not be printed.
|
Value
Function returns a list
with the estimated parameter sets for
every random start. Every parameter set contains the following components:
log_likelihood |
Log likelihood of the estimated solution.
|
aem |
Estimated Assignment Error Matrix (aem). The rows represent the
true categories while the columns stand for the assigned categories. The cells
describe the probability that a coding unit of category i is assigned to
category j.
|
categorial_sizes |
Vector of estimated sizes for each
category.
|
convergence |
If the algorithm converged within the iteration limit
TRUE . FALSE in every other case.
|
iteration |
Number of iterations when the algorithm was terminated.
|
References
Berding, Florian, and Pargmann, Julia (2022).Iota Reliability Concept
of the Second Generation.Measures for Content Analysis Done by
Humans or Artificial Intelligences. Berlin: Logos.
https://doi.org/10.30819/5581
[Package
iotarelr version 0.1.5
Index]