log_linear_fit

Fits a log-linear model to the data provided, using the design matrix provided.
Names for the effects will be "rows1", "cols1" etc. If there are remaining entries,
they can be specified as the "effect_names" character vector.
This function is a wrapper around a call to glm() that handles some of the details
of the call and packages the output in a more convenient form.

Fit a variety of models to two-way tables with ordered categories.
Most of the models are appropriate to apply to tables of that have correlated ordered
response categories. There is a particular interest in rater data and models for rescore
tables. Some utility functions (e.g., Cohen's kappa and weighted kappa) support
more general work on rater agreement.
Because the names of the models are very similar, the functions that implement them are
organized by last name of the primary author of the article or book that suggested the model,
with the name of the function beginning with that author's name and an underscore. This
may make some models more difficult to locate if one doesn't have the original sources. The
vignettes and tests can help to locate models of interest. For more dertaiils see the following references:
Agresti, A. (1983) <doi:10.1016/0167-7152(83)90051-2> "A Simple Diagonals-Parameter Symmetry And Quasi-Symmetry Model",
Agrestim A. (1983) <doi:10.2307/2531022> "Testing Marginal Homogeneity for Ordinal Categorical Variables",
Agresti, A. (1988) <doi:10.2307/2531866> "A Model For Agreement Between Ratings On An Ordinal Scale",
Agresti, A. (1989) <doi:10.1016/0167-7152(89)90104-1> "An Agreement Model With Kappa As Parameter",
Agresti, A. (2010 ISBN:978-0470082898) "Analysis Of Ordinal Categorical Data",
Bhapkar, V. P. (1966) <doi:10.1080/01621459.1966.10502021> "A Note On The Equivalence Of Two Test Criteria For Hypotheses In Categorical Data",
Bhapkar, V. P. (1979) <doi:10.2307/2530344> "On Tests Of Marginal Symmetry And Quasi-Symmetry In Two And Three-Dimensional Contingency Tables",
Bowker, A. H. (1948) <doi:10.2307/2280710> "A Test For Symmetry In Contingency Tables",
Clayton, D. G. (1974) <doi:10.2307/2335638> "Some Odds Ratio Statistics For The Analysis Of Ordered Categorical Data",
Cliff, N. (1993) <doi:10.1037/0033-2909.114.3.494> "Dominance Statistics: Ordinal Analyses To Answer Ordinal Questions",
Cliff, N. (1996 ISBN:978-0805813333) "Ordinal Methods For Behavioral Data Analysis",
Goodman, L. A. (1979) <doi:10.1080/01621459.1979.10481650> "Simple Models For The Analysis Of Association In Cross-Classifications Having Ordered Categories",
Goodman, L. A. (1979) <doi:10.2307/2335159> "Multiplicative Models For Square Contingency Tables With Ordered Categories",
Ireland, C. T., Ku, H. H., & Kullback, S. (1969) <doi:10.2307/2286071> "Symmetry And Marginal Homogeneity Of An r × r Contingency Table",
Ishi-kuntz, M. (1994 ISBN:978-0803943766) "Ordinal Log-linear Models",
McCullah, P. (1977) <doi:10.2307/2345320> "A Logistic Model For Paired Comparisons With Ordered Categorical Data",
McCullagh, P. (1978) <doi:10.2307/2335224> A Class Of Parametric Models For The Analysis Of Square Contingency Tables With Ordered Categories",
McCullagh, P. (1980) <doi:10.1111/j.2517-6161.1980.tb01109.x> "Regression Models For Ordinal Data",
Penn State: Eberly College of Science (undated) <https://online.stat.psu.edu/stat504/lesson/11> "Stat 504: Analysis of Discrete Data, 11. Advanced Topics I",
Schuster, C. (2001) <doi:10.3102/10769986026003331> "Kappa As A Parameter Of A Symmetry Model For Rater Agreement",
Shoukri, M. M. (2004 ISBN:978-1584883210). "Measures Of Interobserver Agreement",
Stuart, A. (1953) <doi:10.2307/2333101> "The Estimation Of And Comparison Of Strengths Of Association In Contingency Tables",
Stuart, A. (1955) <doi:10.2307/2333387> "A Test For Homogeneity Of The Marginal Distributions In A Two-Way Classification",
von Eye, A., & Mun, E. Y. (2005 ISBN:978-0805849677) "Analyzing Rater Agreement: Manifest Variable Methods".

John R. Donoghue

ordinalTables

Fit Models to Two-Way Tables with Correlated Ordered Response
Categories

log_linear_fit function

<dl><dt>n</dt>
<dd>matrix of observed counts to be fit</dd>
<dt>x</dt>
<dd>design matrix for predictor variables</dd>
<dt>effect_names</dt>
<dd>character vector of additional names to apply to the columns of x
The default is NULL, in which case the columns will be labeled "model1" etc.</dd></dl>

Arguments

Fits a log-linear model to the data provided, using the design matrix provided.
Names for the effects will be "rows1", "cols1" etc.  If there are remaining entries,
they can be specified as the "effect_names" character vector.
This function is a wrapper around a call to glm() that handles some of the details
of the call and packages the output in a more convenient form. — log_linear_fit

<dl>

<dt>n</dt>
<dd>matrix of observed counts to be fit</dd>


<dt>x</dt>
<dd>design matrix for predictor variables</dd>


<dt>effect_names</dt>
<dd>character vector of additional names to apply to the columns of x
The default is NULL, in which case the columns will be labeled "model1" etc.</dd>

</dl>

Fits a log-linear model to the data provided, using the design matrix provided.
Names for the effects will be "rows1", "cols1" etc.  If there are remaining entries,
they can be specified as the "effect_names" character vector.
This function is a wrapper around a call to glm() that handles some of the details
of the call and packages the output in a more convenient form.

Description

Usage

Value

Arguments