Comparing Predictors in Multivariate Regression Models: An Extension of Dominance Analysis

2006 ◽  
Vol 31 (2) ◽  
pp. 157-180 ◽  
Author(s):  
Razia Azen ◽  
David V. Budescu
Keyword(s):  
Multiple Regression ◽  
Multivariate Regression ◽  
Regression Models ◽  
Multiple Response ◽  
Relative Importance ◽  
Multivariate Models ◽  
Minimal Set ◽  
Dominance Analysis ◽  
Response Variables

Dominance analysis (DA) is a method used to compare the relative importance of predictors in multiple regression. DA determines the dominance of one predictor over another by comparing their additional R2 contributions across all subset models. In this article DA is extended to multivariate models by identifying a minimal set of criteria for an appropriate generalization of R2 to the case of multiple response variables. The DA results obtained by univariate regression (with each criterion separately) are analytically compared with results obtained by multivariate DA and illustrated with an example. It is shown that univariate dominance does not necessarily imply multivariate dominance (and vice versa), and it is recommended that researchers who wish to account for the correlation among the response variables use multivariate DA to determine the relative importance of predictors.

s-Level Multiple-Response Main-Effects Factorial Plans

1992 ◽  
Vol 42 (3-4) ◽  
pp. 237-246
Author(s):  
U. Batra ◽  
M.L. Aggarwal
Keyword(s):  
Dispersion Matrix ◽  
Multiple Response ◽  
Relative Importance ◽  
Factorial Experiments ◽  
Response Variable ◽  
Main Effects ◽  
Observation Vector ◽  
Response Variables

This paper deals with construction of plans for s-level factorial experiments in which there are p response variables and each respose is affected by one or more factors. The plans are orthogonal for each response variable. Estimates of the parameters in the models for such plans are obtained when Σ, the dispersion matrix of an observation vector is known. The properties of these estimates can be of help in designing the experiment so that the variances of estimates of the parameters can be influenced by their relative importance.

Change in Life Attitudes During Inpatient Psychosomatic Treatment

2021 ◽  
pp. 002216782110400
Author(s):  
Juan Valdés-Stauber ◽  
Helen Kämmerle ◽  
Susanne Bachthaler
Keyword(s):  
Self Efficacy ◽  
Multivariate Regression ◽  
Regression Models ◽  
Effect Sizes ◽  
Life Change ◽  
Life Meaning ◽  
Multivariate Models ◽  
Clinical Variables ◽  
Life Attitudes ◽  
Factors Influencing

Objectives: This study’s primary aim was to investigate whether meaning-based attitudes to life change during inpatient psychosomatic treatment and the factors influencing the extent of this change. Method: This prospective study ( N = 138) was designed as a naturalistic observation. The effectiveness of treatment was investigated through pre–post comparisons of clinical variables and life attitudes (Life Attitude Profile–Revised) using bivariate tests. Factors influencing the extent of changes in life attitudes were investigated using multivariate regression models. Results: Regarding clinical variables, a small but significant improvement in life attitudes was found, with effect sizes ranging from 0.19 to 0.58. Neuroticism correlated negatively with life attitudes at admission but not significantly with the extent of change in life attitudes. In multivariate models, the extent of the therapeutic relationship and neuroticism correlated positively with the extent of improvement in coherence and self-efficacy. The improvement in self-efficacy was associated with an improvement in life attitudes. Discussion: Although life attitudes are robust characteristics of a person, they change during a hospital psychosomatic treatment, similar to the clinical improvement of symptoms. However, the association between the two is weak. People with stronger neuroticism experience a greater increase in life meaning during hospitalization.

Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

2009 ◽  
Vol 34 (3) ◽  
pp. 319-347 ◽  
Author(s):  
Razia Azen ◽  
Nicole Traxel
Keyword(s):  
Logistic Regression ◽  
Random Sampling ◽  
Regression Models ◽  
Applied Research ◽  
Simple Random Sampling ◽  
Relative Importance ◽  
Bootstrap Sampling ◽  
Dominance Analysis ◽  
Logistic Regression Models ◽  
Predictor Importance

This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R2 analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A simulation study, using both simple random sampling from a known population and bootstrap sampling from a single (parent) random sample, was performed to evaluate the bias, sampling distribution, and confidence intervals of quantitative dominance measures as well as the reproducibility of qualitative dominance measures. Results indicated that the bootstrap procedure is feasible and can be used in applied research to generalize logistic regression dominance analysis results to the population of interest. The procedures for determining and interpreting the general dominance of predictors in a logistic regression context are illustrated with an empirical example.

Rapid quality assessment of bread using developed multivariate models: A simple predictive modeling approach

2020 ◽  
Author(s):  
Aidin Pahlavan ◽  
Mohammad Hassan Kamani ◽  
Amir Hossein Elhamirad ◽  
Zahra Sheikholeslami ◽  
Mohammad Armin ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Predictive Modeling ◽  
Multivariate Regression ◽  
Regression Models ◽  
Multivariate Regression Analysis ◽  
Multivariate Models ◽  
Barbari Bread ◽  
Non Destructive

AbstractThis study was focused on the assessment of relationships among the properties of wheat and their resultant flour, dough and final bread. For this purpose, multivariate linear regression in the form of the step-wise algorithm was applied to evaluate the relation among the flour characteristics of wheat with quality of dough and the final breads (Barbari and Lavash). The results showed that variety of wheat (Orum, Pishgam, and Zareh) could not affect the moisture content and quantity of the flour residue; however, considerable variation was observed on protein content and Zeleny number. The multivariate regression analysis built appropriate models to predict the hardness of the Barbari bread (R2 = 0.98) and specific volume of the Lavash bread (R2 = 0.98). Overall, the results indicated that the regression models in the form of step-wise might be useful as a non-destructive technique for assessing quality of bread.

scholarly journals Multiple Response Variables Regression Models in R: The mcglm Package

2018 ◽  
Vol 84 (4) ◽  
Author(s):  
Wagner Hugo Bonat
Keyword(s):  
Regression Models ◽  
Multiple Response ◽  
Response Variables

scholarly journals 242 Effect of trucking distance on sale price of beef calf and feeder cattle lots sold through video auctions from 2010 through 2018

2020 ◽  
Vol 98 (Supplement_3) ◽  
pp. 10-11
Author(s):  
Esther D McCabe ◽  
Mike E King ◽  
Karol E Fike ◽  
Maggie J Smith ◽  
Glenn M Rogers ◽  
...  
Keyword(s):  
Multiple Regression ◽  
Regression Models ◽  
Average Weight ◽  
Sale Price ◽  
Feeder Cattle ◽  
A Value ◽  
Multiple Regression Models ◽  
Determine Effect

Abstract The objective was to determine effect of trucking distance on sale price of beef calf and feeder cattle lots sold through Superior Livestock Video Auctions from 2010 through 2018. Data analyzed were collected from 211 livestock video auctions. There were 42,043 beef calf lots and 19,680 feeder cattle lots used in these analyses. Six states (Colorado, Iowa, Kansas, Nebraska, Oklahoma, and Texas) of delivery comprised 70% of calf lots and 83% of feeder cattle lots and were used in these analyses. All lot characteristics that could be accurately quantified or categorized were used to develop multiple regression models that evaluated effects of independent factors using backwards selection. A value of P < 0.05 was used to maintain a factor in the final models. Based upon reported state of origin and state of delivery, lots were categorized into one of the following trucking distance categories: 1) Within-State, 2) Short-Haul, 3) Medium-Haul, and 4) Long-Haul. Average weight and number of calves in lots analyzed was 259.2 ± 38.4 kg BW and 100.6 ± 74.3 head, respectively. Average weight and number of feeder cattle in lots analyzed was 358.4 ± 34.3 kg BW and 110.6 ± 104.1 head, respectively. Beef calf lots hauled Within-State sold for more ($169.24/45.36 kg; P < 0.0001) than other trucking distance categories (Table 1). Long-Haul calf lots sold for the lowest (P < 0.0001) price ($166.70/45.36 kg). Within-State and Short-Haul feeder cattle lots sold for the greatest (P < 0.0001) price ($149.96 and $149.81/45.36 kg, respectively; Table 2). Long-Haul feeder cattle lots sold for the lowest (P < 0.0001) price, $148.43/45.36 kg. These results indicate there is a price advantage for lots expected to be hauled shorter distances, likely because of cost and risk associated with transportation.

scholarly journals Diagnostic checking for multivariate regression models

2008 ◽  
Vol 99 (9) ◽  
pp. 1841-1859 ◽  
Author(s):  
Lixing Zhu ◽  
Ruoqing Zhu ◽  
Song Song
Keyword(s):  
Multivariate Regression ◽  
Regression Models ◽  
Diagnostic Checking
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