Using Wherry's AdjustedR2and Mallow'sCpfor Model Selection From All Possible Regressions

Olejnik, Stephen; Mills, Jamie D.; Keselman, H. J.

doi:10.1080/00220970009600643

Cited by 39 publications

(15 citation statements)

References 11 publications

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“…Similar results emerged consistently across all models in the present study (see Tables 3 and 4 for specific β, F , and p values). Adjusted R 2 , an indication of the amount of variance explained by the model while taking into account extra explanatory variables including covariates (Olejnik, Mills, & Keselman, 2000), is also reported in Tables 3 and 4.…”

Section: Resultsmentioning

confidence: 99%

Relationships Between Drinking Motives and Smoking Expectancies Among Daily Smokers Who Are Also Problem Drinkers

Foster

Zvolensky

Garey

et al. 2014

Journal of Dual Diagnosis

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Objective There is a high co-occurrence of problem drinking and regular cigarette smoking, and cognitive processes (e.g., motivation to use, expectations about the consequences of use) related to each are positively associated with one another. We explored drinking motives in relation to cognitive-based smoking processes among smokers with problematic drinking. We expected that drinking coping motives would be associated with smoking consequences related to negative reinforcement and negative personal outcomes, and inflexibility of smoking behavior; observed effects for coping motives would be unique from shared variance with other motives and incrementally evident beyond the variance accounted for by tobacco-related health problems, smoking rate, negative affectivity, cannabis use, and gender. Methods The sample included 195 individuals recruited into a larger study of smoking cessation treatments (i.e., they were interested in quitting), who were heavy drinkers and smoked daily. Participants were primarily male (n = 122, 63%), fairly young (Mage=30.3 years; SD=12.46), and predominantly White/Caucasian (n = 175, 80%). Roughly 57% (n = 111) had at least one comorbid Axis I disorder, the most common being social anxiety (n = 21, 11%) and generalized anxiety disorder (n = 12, 6%). Results Coping drinking motives predicted negative smoking consequences, negative reinforcement, and smoking inflexibility. Enhancement drinking motives marginally predicted positive reinforcement. Conformity drinking motives predicted smoking consequences related to appetite/weight control. Social drinking motives predicted negative reinforcement and barriers to cessation, and marginally predicted positive reinforcement. Conclusions Theoretical models and clinical activities focused on smoking cessation among problem drinkers may benefit from considering the role of drinking motives, particularly coping-oriented motives, to better understanding cognitive-based smoking processes.

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Section: Resultsmentioning

confidence: 99%

Relationships Between Drinking Motives and Smoking Expectancies Among Daily Smokers Who Are Also Problem Drinkers

Foster

Zvolensky

Garey

et al. 2014

Journal of Dual Diagnosis

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“…The best-subset regression analyses were conducted in R using the leaps package (Lumley, 2017). The adjusted R 2 was examined to guide optimal model selection (Olejnik et al, 2000). Up to the best five predictors were requested, for parsimony and interpretability.…”

Section: Discussionmentioning

confidence: 99%

Clinical and parental predictors of emotion regulation following cognitive behaviour therapy in children with autism

Tajik-Parvinchi

Farmus

Cribbie

et al. 2020

Autism

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Difficulties with emotion regulation are common in children with autism. Although interventions targeting emotion regulation show promise, children’s individual treatment responses vary, and it is important to understand the factors that contribute to treatment change. The present study aimed to identify pre-treatment child characteristics and parent psychopathology that predict treatment response in a 10-week manualized cognitive behaviour therapy intervention for children with autism, 8–12 years of age. Exploratory best-subset regression analyses were first carried out to identify the optimal set of predictors. Logistic regressions were then conducted to determine whether these variables predicted reliable improvement. Outcome variables consisted of the lability/negativity and the emotion regulation subscales of the Emotion Regulation Checklist. Predictors included pre-treatment developmental, clinical, and parent psychopathology variables. Analyses revealed that youth who started the treatment with higher verbal reasoning, higher impairment in social motivation, and more anxious parents were more likely to show reliable improvements in emotion regulation. Youth who started the treatment with higher internalizing scores had lower odds of showing reliable improvement. Implications of our findings include facilitation of active involvement, avoidance of complex language, and the provision of additional supports. Further suggestions to inform clinical practice are discussed. Lay abstract Children with autism commonly experience difficulty controlling their emotions. Although existing treatments are successful in teaching critical emotion regulation skills, not all children improve. It is important to identify the factors that influence treatment response to be able to reach more children. This study aimed to identify child and parent characteristics that predict treatment response in a 10-week cognitive behaviour therapy treatment for children with autism, 8–12 years of age, and their parents. We found that youth who started the treatment with higher verbal abilities, who were more anxious in social situations, and had parents who were more anxious, were more likely to improve in learning new emotion regulation skills. We also found that children who had more physical discomforts or complaints before starting the treatment were less likely to improve in their negative expressions of emotion. Our study suggests that it is important for clinicians to promote active involvement and learning by avoiding complex language and to use more visual materials to supplement the learning process, and make sure that sessions are sensitive to the individual needs of participants.

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“…In other cases, it can be a tool for predicting time series [6]. In regression problems, cross validation can be used as a model-selection technique instead of Mallow's criteria [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Tenfold Bootstrap Procedure for Support Vector Machines

Vrigazova¹,

Ivanov²

2020

csci

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Cross validation is often used to split input data into training and test set in Support vector machines. The two most commonly used cross validation versions are the tenfold and leave-one-out cross validation. Another commonly used resampling method is the random test/train split. The advantage of these methods is that they avoid overﬁtting in the model and perform model selection. They, however, can increase the computational time for ﬁtting Support vector machines with the increase of the size of the dataset. In this research, we propose an alternative for ﬁtting SVM, which we call the tenfold bootstrap for Support vector machines. This resampling procedure can signiﬁcantly reduce execution time despite the big number of observations, while preserving model’s accuracy. With this ﬁnding, we propose a solution to the problem of slow execution time when ﬁtting support vector machines on big datasets.

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Using Wherry's AdjustedR²and Mallow'sC_pfor Model Selection From All Possible Regressions

Cited by 39 publications

References 11 publications

Relationships Between Drinking Motives and Smoking Expectancies Among Daily Smokers Who Are Also Problem Drinkers

Relationships Between Drinking Motives and Smoking Expectancies Among Daily Smokers Who Are Also Problem Drinkers

Clinical and parental predictors of emotion regulation following cognitive behaviour therapy in children with autism

Tenfold Bootstrap Procedure for Support Vector Machines

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