fct_log_likelihood_c

Function written in <code>C++</code> estimating the log likelihood of a given
parameter set.

Routines and tools for assessing the quality of content
analysis on the basis of the Iota Reliability Concept. The concept is
inspired by item response theory and can be applied to any kind of
content analysis which uses a standardized coding scheme and discrete
categories. It is also applicable for content analysis conducted by
artificial intelligence. The package provides reliability measures
for a complete scale as well as for every single category. Analysis of
subgroup-invariance and error corrections are implemented. This
information can support the development process of a coding scheme and
allows a detailed inspection of the quality of the generated data.
Equations and formulas working in this package are part of Berding et
al. (2022)<doi:10.3389/feduc.2022.818365> and Berding and Pargmann
(2022) <doi:10.30819/5581>.

Berding Florian

iotarelr

Iota Inter Coder Reliability for Content Analysis

Pargmann Julia

fct_log_likelihood_c function

<dl><dt>categorial_sizes</dt>
<dd><code>Vector</code> containing the sizes of the different
categories. That is amount of a category on all cases.</dd>
<dt>aem</dt>
<dd><code>Matrix</code> in aem form. This matrix reports the true category
in the rows and the assigned categories in the columns. The cells represent
the probabilities that a coding unit of category i is assigned to category j.</dd>
<dt>obs_pattern_shape</dt>
<dd><code>Matrix</code> containing the unique patterns found
in the data. Ideally this matrix is generated by the function
<code>get_patterns()</code>.</dd>
<dt>obs_pattern_frq</dt>
<dd><code>Vector</code> containing the frequencies of the
patterns. Ideally it is generated by the the function
<code>get_patterns()</code>.</dd>
<dt>categorical_levels</dt>
<dd><code>Vector</code> containing all possible categories of
the content analysis.</dd></dl>

Arguments

Estimating log-likelihood — fct_log_likelihood_c

<dl>

<dt>categorial_sizes</dt>
<dd><code>Vector</code> containing the sizes of the different
categories. That is amount of a category on all cases.</dd>


<dt>aem</dt>
<dd><code>Matrix</code> in aem form. This matrix reports the true category
in the rows and the assigned categories in the columns. The cells represent
the probabilities that a coding unit of category i is assigned to category j.</dd>


<dt>obs_pattern_shape</dt>
<dd><code>Matrix</code> containing the unique patterns found
in the data. Ideally this matrix is generated by the function
<code>get_patterns()</code>.</dd>


<dt>obs_pattern_frq</dt>
<dd><code>Vector</code> containing the frequencies of the
patterns. Ideally it is generated by the the function
<code>get_patterns()</code>.</dd>


<dt>categorical_levels</dt>
<dd><code>Vector</code> containing all possible categories of
the content analysis.</dd>

</dl>

fct_log_likelihood_c: Estimating log-likelihood

Description

Usage

Value

Arguments

References