scholarly journals Modelling Tabular Data with an Ordered Outcome

1996 ◽  
Vol 1 (3) ◽  
pp. 1-10
Author(s):  
Vernon Gayle
Keyword(s):  
Model Building ◽  
Linear Models ◽  
Statistical Modelling ◽  
Categorical Variables ◽  
Sociological Research ◽  
Ratio Model ◽  
Ordered Categories ◽  
Continuation Ratio ◽  
Recent Developments ◽  
Log Linear

A large amount of data that is considered within sociological studies consists of categorical variables that lend themselves to tabular analysis. In the sociological analysis of data regarding social class and educational attainment, for example, the variables of interest can often plausibly be considered as having a substantively interesting order. Standard log-linear models do not take ordinality into account, thereby potentially they may disregard useful information. Analyzing tables where the response variable has ordered categories through model building has been problematic in software packages such as GLIM (Aitken et al., 1989). Recent developments in statistical modelling have offered new possibilities and this paper explores one option, namely the continuation ratio model which was initially reported by Fienberg and Mason (1979). The fitting of this model to data in tabular form is possible in GLIM although not especially trivial and by and large this approach has not been employed in sociological research. In this paper I outline the continuation ratio model and comment upon how it can be fitted to data by sociologists using the GLIM software. In addition I present a short description of the relative merits of such an approach. Presenting this paper in an electronic format facilitates the possibility of replicating the analysis. The data is appended to the paper in the appropriate format along with a copy of the GLIM transcript. A dumped GLIM4 file is also attached.

Log-Linear Models with Dependent Spatial Data

1983 ◽  
Vol 15 (6) ◽  
pp. 801-813 ◽  
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.

scholarly journals The Application of Log-Linear Model to Selected Poison Patients

2020 ◽  
pp. 1-7
Author(s):  
Fatin N.S.A. ◽  
Norlida M.N. ◽  
Siti Z.M.J.
Keyword(s):  
Linear Model ◽  
Linear Models ◽  
Demographic Data ◽  
Contingency Tables ◽  
Categorical Variables ◽  
State Variables ◽  
Exposure Route ◽  
Higher Dimensional ◽  
Route Of Exposure ◽  
Log Linear

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

Generalized Log-Linear Models of Housing Choice

1988 ◽  
Vol 20 (1) ◽  
pp. 55-69 ◽  
Author(s):  
M C Deurloo ◽  
F M Dieleman ◽  
W A V Clark
Keyword(s):  
Linear Models ◽  
Nonlinear Effects ◽  
Design Matrix ◽  
Housing Choice ◽  
Ordered Categories ◽  
Public And Private ◽  
Income Effects ◽  
Additional Information ◽  
Log Linear

By incorporating the structure of polytomous variables with ordered categories in the design matrix, nonstandard logit models are used to analyze housing choice. The detailed effects of income, age, and type of housing market on choice are examined. The additional information that is incorporated in the modeling leads to a more parsimonious representation of the data. The results confirm the central and substantial role of income; income effects are linear for owners but there are nonlinear effects for public and private renters. There are important age and region interaction effects on choice for households originally in the rental sector, and for former owners the value of the previous dwelling influences choice.

Life after Chi-Squared: An Introduction to Log-Linear Analysis

1998 ◽  
Vol 43 (8) ◽  
pp. 837-842 ◽  
Author(s):  
David L Streiner ◽  
Elizabeth Lin
Keyword(s):  
Linear Models ◽  
Linear Analysis ◽  
Categorical Variables ◽  
Factors Associated ◽  
Several Variables ◽  
Chi Squared ◽  
Log Linear

Chi-squared tests are used to examine the relationships among categorical variables. However, they are difficult to use and interpret when more than 2 variables are involved. In such cases, it is better to use a related statistic, called log-linear analysis. This article is an introduction to log-linear models, illustrating how they can be used to tease apart relationships among several variables in looking at the factors associated with photonumerophobia.

scholarly journals Heatmaps for Patterns of Association in log-Linear Models

2020 ◽  
Vol 6 ◽  
pp. 237802311989921
Author(s):  
Mauricio Bucca
Keyword(s):  
Social Mobility ◽  
Linear Models ◽  
Categorical Variables ◽  
Visualization Technique ◽  
Odds Ratios ◽  
Detailed Characterization ◽  
Log Odds ◽  
Sociological Work ◽  
Log Linear

Log-linear models offer a detailed characterization of the association between categorical variables, but the breadth of their outputs is difficult to grasp because of the large number of parameters these models entail. Revisiting seminal findings and data from sociological work on social mobility, the author illustrates the use of heatmaps as a visualization technique to convey the complex patterns of association captured by log-linear models. In particular, turning log odds ratios derived from a model’s predicted counts into heatmaps makes it possible to summarize large amounts of information and facilitates comparison across models’ outcomes.

scholarly journals Modelling Synergy using Manifest Categorical Variables

1998 ◽  
Vol 22 (3) ◽  
pp. 537-557 ◽  
Author(s):  
Alexander von Eye ◽  
Christof Schuster ◽  
William M. Rogers
Keyword(s):  
Social Sciences ◽  
Linear Models ◽  
Contraceptive Use ◽  
Natural Sciences ◽  
Alternative Methods ◽  
Categorical Variables ◽  
The Social ◽  
Log Linear ◽  
The Relationship

This paper discusses methods to model the concept of synergy at the level of manifest categorical variables. First, a classification of concepts of synergy is presented. A dditive and nonadditive concepts of synergy are distinguished. Most prominent among the nonadditive concepts is superadditive synergy. Examples are given from the natural sciences and the social sciences. M delling focuses on the relationship between the agents involved in a synergetic process. These relationships are expressed in form of contrasts, expressed in effect coding vectors in design matrices for nonstandard log-linear models. A method by Schuster is used to transform design matrices such that parameters reflect the proposed relationships. A n example reanalyses data presented by Bishop, Fienberg, and Holland (1975) that describe the development of thromboembolisms in women who differ in their patterns of contraceptive use and smoking. Alternative methods of analysis are com pared. Implications for developmental research are discussed.

scholarly journals Meteorological conditions and incidence of Legionnaires' disease in Glasgow, Scotland: application of statistical modelling

2012 ◽  
Vol 141 (4) ◽  
pp. 687-696 ◽  
Author(s):  
C. E. DUNN ◽  
B. ROWLINGSON ◽  
R. S. BHOPAL ◽  
P. DIGGLE
Keyword(s):  
Seasonal Variation ◽  
Wind Speed ◽  
Air Temperature ◽  
Linear Models ◽  
Meteorological Data ◽  
Statistical Modelling ◽  
Linear Regression Models ◽  
Explanatory Variables ◽  
Legionnaires Disease ◽  
Log Linear

SUMMARYThis study investigated the relationships between Legionnaires' disease (LD) incidence and weather in Glasgow, UK, by using advanced statistical methods. Using daily meteorological data and 78 LD cases with known exact date of onset, we fitted a series of Poisson log-linear regression models with explanatory variables for air temperature, relative humidity, wind speed and year, and sine-cosine terms for within-year seasonal variation. Our initial model showed an association between LD incidence and 2-day lagged humidity (positive, P = 0·0236) and wind speed (negative, P = 0·033). However, after adjusting for year-by-year and seasonal variation in cases there were no significant associations with weather. We also used normal linear models to assess the importance of short-term, unseasonable weather values. The most significant association was between LD incidence and air temperature residual lagged by 1 day prior to onset (P = 0·0014). The contextual role of unseasonably high air temperatures is worthy of further investigation. Our methods and results have further advanced understanding of the role which weather plays in risk of LD infection.

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