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Tytuł:
Independence Analysis of Nominal Data with the Use of Log-Linear Models in R
Autorzy:
Brzezińska, Justyna
Powiązania:
https://bibliotekanauki.pl/articles/465735.pdf
Data publikacji:
2012
Wydawca:
Główny Urząd Statystyczny
Tematy:
Log-linear models
cross-tabulation
qualitative data
independence analysis of nominal data
Opis:
Log-linear models are used to analyze the relationship between two or more categorical (e.g. nominal or ordinal) variables. The term log-linear derives from the fact that one can, through logarithmic transformations, restate the problem of analyzing multi-way frequency tables in terms that are very similar to ANOVA. Specifically, one may think of the multi-way frequency table to reflect various main effects and interaction effects that add together in a linear fashion to bring about the observed table of frequencies. There are several types of models between dependence and independence: homogenous association, partial association, conditional association and null model. Expected cell frequencies are obtained with the use of iterative proportional fitting algorithm (IPF) [Deming, Stephen 1940]. The next step is to derive model coefficients for single variables as well as for interaction parameter and the most useful tool for interpreting model parameter is odds and odds ratio. Log-linear models are available in R software with the use of loglm function in MASS library and glm function in stats library. In this paper log-linear analysis will be presented with the use of available packages on empirical datasets in economic area.
Źródło:
Statistics in Transition new series; 2012, 13, 2; 311-320
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The effect of binary data transformation in categorical data clustering
Autorzy:
Cibulková, Jana
Šulc, Zdenek
Sirota, Sergej
Rezanková, Hana
Powiązania:
https://bibliotekanauki.pl/articles/1194463.pdf
Data publikacji:
2019-07-02
Wydawca:
Główny Urząd Statystyczny
Tematy:
hierarchical cluster analysis
nominal variable
binary variable
categorical data
similarity measures
evaluation criteria
generated data
Opis:
This paper focuses on hierarchical clustering of categorical data and compares two approaches which can be used for this task. The first one, an extremely common approach, is to perform a binary transformation of the categorical variables into sets of dummy variables and then use the similarity measures suited for binary data. These similarity measures are well examined, and they occur in both commercial and non-commercial software. However, a binary transformation can possibly cause a loss of information in the data or decrease the speed of the computations. The second approach uses similarity measures developed for the categorical data. But these measures are not so well examined as the binary ones and they are not implemented in commercial software. The comparison of these two approaches is performed on generated data sets with categorical variables and the evaluation is done using both the internal and the external evaluation criteria. The purpose of this paper is to show that the binary transformation is not necessary in the process of clustering categorical data since the second approach leads to at least comparably good clustering results as the first approach.
Źródło:
Statistics in Transition new series; 2019, 20, 2; 33-47
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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