The Application of Log-Linear Model to Selected Poison Patients

Log-linear model is a technique used to analyze the cross-classification categorical data or the contingency table. It is used to obtain the parsimony models that describe the interaction between the categorical variables in contingency tables. Log-linear models are commonly used in evaluating higher dimensional contingency tables that involves more than two categorical variables. This study focuses on analyzing data of poisoned patients from 2012 to 2014 using log-linear model. There are two model analyzed; model for demographic data of patients and model of poisoning information. For the first model, the variables involved are gender, age, race and state. Variables for the second model are circumstance of exposure, type of exposure, location of exposure, route of exposure and types of poison. Both log-linear models are developed to investigate the association between variables in the model. As a result of this study, the best model for demographic data and poisoning information are the model with three-ways interaction. For the best model of demographic data, there is an association between gender, age and race, race, gender and state as well as age, race and state. Meanwhile, the best model for poisoning information reveals that there is relationship between circumstance of exposure, route of exposure and type of poison, location of exposure, route of exposure and type of poison, circumstance of exposure, type of exposure and route of exposure, circumstance of exposure, location of exposure and route of exposure, circumstance of exposure, type of exposure and type of poison and also type of exposure, location of exposure and type of poison. Keywords: log-linear; demographic; gender; age; race; state; circumstance of exposure; type of exposure; location of exposure; route of exposure; types of poison

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Analysis of Two-Way Contingency Tables : Log-Linear Models and Dual Scaling

Methods of Information in Medicine ◽

10.1055/s-0038-1635493 ◽

1987 ◽

Vol 26 (03) ◽

pp. 104-108

Author(s):

M. A. A. Moussa

Keyword(s):

Linear Models ◽

Contingency Tables ◽

Real Data ◽

Categorical Variables ◽

Bayes Estimators ◽

Dual Scaling ◽

Independence Model ◽

Optimal Weights ◽

Log Linear ◽

Female Nurses

SummaryThe paper focuses upon the measurement of association in two-way contingency tables, using the log-linear models and dual scaling approaches. The former comprises [1] the use of pseudo-Bayes estimators to remove zeros, [2] fitting the resulting smoothed array to all possible configurations of log-linear models, [3] fitting the quasi-independence model to detect anomalous cells that caused deviation from the null-independence model. The latter includes [1] estimation of the optimal weights that maximize the canonical correlation between the two categorical variables by an optimization iterative method, [2] testing the discriminability of the estimated scoring scheme. The two approaches were applied to a set of real data for the study of the association between maternal age at marriage and types of reproductive wastage in a sampling survey conducted in the population of female nurses in Kuwait.

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Log-Linear, Logit-Linear Models: A Didactic

Journal of Educational Statistics ◽

10.3102/10769986006001075 ◽

1981 ◽

Vol 6 (1) ◽

pp. 75-102 ◽

Cited By ~ 4

Author(s):

Frank B. Baker

Keyword(s):

Linear Model ◽

Educational Research ◽

Qualitative Data ◽

Linear Models ◽

Contingency Tables ◽

Basic Logic ◽

Data Analyses ◽

Potential Applications ◽

Log Linear ◽

Model Technique

The recently developed log-linear model technique for the analysis of contingency tables has many potential applications within educational research. This paper describes the two major models, log-linear and logit-linear, that are employed under this approach. The basic logic of each is developed and illustrative data analyses presented. In addition, the underlying communality of the two schemes is shown. The intent was to provide the reader with perspective that will facilitate understanding the approach and its application to the analysis of qualitative data.

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Ordinal Log-Linear Models for Contingency Tables

Folia Oeconomica Stetinensia ◽

10.1515/foli-2016-0017 ◽

2016 ◽

Vol 16 (1) ◽

pp. 264-273

Author(s):

Justyna Brzezińska

Keyword(s):

Contingency Table ◽

Linear Models ◽

Contingency Tables ◽

Linear Analysis ◽

Categorical Variables ◽

Ordinal Variables ◽

Advantages And Disadvantages ◽

Effect Model ◽

Log Linear ◽

Model Algorithm

Abstract A log-linear analysis is a method providing a comprehensive scheme to describe the association for categorical variables in a contingency table. The log-linear model specifies how the expected counts depend on the levels of the categorical variables for these cells and provide detailed information on the associations. The aim of this paper is to present theoretical, as well as empirical, aspects of ordinal log-linear models used for contingency tables with ordinal variables. We introduce log-linear models for ordinal variables: linear-by-linear association, row effect model, column effect model and RC Goodman’s model. Algorithm, advantages and disadvantages will be discussed in the paper. An empirical analysis will be conducted with the use of R.

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On the correspondence of deviances and maximum-likelihood and interval estimates from log-linear to logistic regression modelling

Royal Society Open Science ◽

10.1098/rsos.191483 ◽

2020 ◽

Vol 7 (1) ◽

pp. 191483

Author(s):

W. Jing ◽

M. Papathomas

Keyword(s):

New York ◽

Logistic Regression ◽

Maximum Likelihood ◽

Linear Model ◽

Contingency Table ◽

Linear Models ◽

Maximum Likelihood Estimates ◽

Categorical Variables ◽

Model Parameters ◽

Log Linear

Consider a set of categorical variables P where at least one, denoted by Y , is binary. The log-linear model that describes the contingency table counts implies a logistic regression model, with outcome Y . Extending results from Christensen (1997, Log-linear models and logistic regression , 2nd edn. New York, NY, Springer), we prove that the maximum-likelihood estimates (MLE) of the logistic regression parameters equals the MLE for the corresponding log-linear model parameters, also considering the case where contingency table factors are not present in the corresponding logistic regression and some of the contingency table cells are collapsed together. We prove that, asymptotically, standard errors are also equal. These results demonstrate the extent to which inferences from the log-linear framework translate to inferences within the logistic regression framework, on the magnitude of main effects and interactions. Finally, we prove that the deviance of the log-linear model is equal to the deviance of the corresponding logistic regression, provided that no cell observations are collapsed together when one or more factors in P ∖ { Y } become obsolete. We illustrate the derived results with the analysis of a real dataset.

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Log-Linear Models with Dependent Spatial Data

Environment and Planning A Economy and Space ◽

10.1068/a150801 ◽

1983 ◽

Vol 15 (6) ◽

pp. 801-813 ◽

Cited By ~ 21

Author(s):

B Fingleton

Keyword(s):

Spatial Data ◽

Statistical Models ◽

Linear Models ◽

Square Lattice ◽

Categorical Variables ◽

Systematic Sampling ◽

Pairwise Correlations ◽

Independent Observations ◽

Intersection Points ◽

Log Linear

Log-linear models are an appropriate means of determining the magnitude and direction of interactions between categorical variables that in common with other statistical models assume independent observations. Spatial data are often dependent rather than independent and thus the analysis of spatial data by log-linear models may erroneously detect interactions between variables that are spurious and are the consequence of pairwise correlations between observations. A procedure is described in this paper to accommodate these effects that requires only very minimal assumptions about the nature of the autocorrelation process given systematic sampling at intersection points on a square lattice.

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Estimates for cell counts and common odds ratio in three-way contingency tables by homogeneous log-linear models with missing data

AStA Advances in Statistical Analysis ◽

10.1007/s10182-016-0275-y ◽

2016 ◽

Vol 101 (1) ◽

pp. 51-65 ◽

Cited By ~ 1

Author(s):

Haresh D. Rochani ◽

Robert L. Vogel ◽

Hani M. Samawi ◽

Daniel F. Linder

Keyword(s):

Missing Data ◽

Odds Ratio ◽

Linear Models ◽

Contingency Tables ◽

Cell Counts ◽

Common Odds Ratio ◽

Log Linear

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A Cascaded Unsupervised Model for PoS Tagging

ACM Transactions on Asian and Low-Resource Language Information Processing ◽

10.1145/3447759 ◽

2021 ◽

Vol 20 (1) ◽

pp. 1-23

Author(s):

Necva Bölücü ◽

Burcu Can

Keyword(s):

Linear Model ◽

Language Processing ◽

Bayesian Model ◽

Linear Models ◽

Syntactic Category ◽

Semantic Parsing ◽

Pos Tagging ◽

Part Of Speech ◽

Sentence Level ◽

Log Linear

Part of speech (PoS) tagging is one of the fundamental syntactic tasks in Natural Language Processing, as it assigns a syntactic category to each word within a given sentence or context (such as noun, verb, adjective, etc.). Those syntactic categories could be used to further analyze the sentence-level syntax (e.g., dependency parsing) and thereby extract the meaning of the sentence (e.g., semantic parsing). Various methods have been proposed for learning PoS tags in an unsupervised setting without using any annotated corpora. One of the widely used methods for the tagging problem is log-linear models. Initialization of the parameters in a log-linear model is very crucial for the inference. Different initialization techniques have been used so far. In this work, we present a log-linear model for PoS tagging that uses another fully unsupervised Bayesian model to initialize the parameters of the model in a cascaded framework. Therefore, we transfer some knowledge between two different unsupervised models to leverage the PoS tagging results, where a log-linear model benefits from a Bayesian model’s expertise. We present results for Turkish as a morphologically rich language and for English as a comparably morphologically poor language in a fully unsupervised framework. The results show that our framework outperforms other unsupervised models proposed for PoS tagging.

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