An adjusted coefficient of determination (<i>R</i><sup>2</sup>) for generalized linear mixed models in one go

Piepho, Hans‐Peter

doi:10.1002/bimj.202200290

Cited by 16 publications

(8 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To obtain the new adsorbent material, functionalization by impregnation of Amberlite XAD7HP resin was performed using the dry SIR method [66][67][68]. The supplier of the desired pendant group used tri-n-octylphosphine oxide-TOPO.…”

Section: Preparation Of Xad7hp-topo Materialsmentioning

confidence: 99%

Adsorption of Scandium Ions by Amberlite XAD7HP Polymeric Adsorbent Loaded with Tri-n-Octylphosphine Oxide

Daminescu,

Duteanu,

Ciopec

et al. 2024

Molecules

View full text Add to dashboard Cite

In an actual economic context, the demand for scandium has grown due to its applications in top technologies. However, further development of new technologies will lead to an increase in the market for Sc related to such technologies. The present study aims to improve and upgrade existing technology in terms of efficient scandium recovery, proposing a new material with selective adsorptive properties for scandium recovery. To highlight the impregnation of Amberlite XAD7HP resin with tri-n-octylphosphine oxide extractant by the solvent-impregnated resin method, the obtained adsorbent material was characterized by physico-chemical techniques. Further, the specific surface of the adsorbent and the zero-point charge of the adsorbent surface have been determined. Different parameters, such as initial concentration, adsorbent amount, contact time, or temperature, have been studied. The initial pH effect was investigated when a maximum adsorption capacity of 31.84 mg g−1 was obtained at pH > 3, using 0.1 g of adsorbent and a contact time of 90 min and 298 K. An attempt was made to discuss and provide a clear representation of the studied adsorption process, proposing a specific mechanism for Sc(III) recovery from aqueous solutions through kinetic, thermodynamic, and equilibrium studies. Adsorption/desorption studies reveal that the prepared adsorbent material can be reused five times.

show abstract

Section: Preparation Of Xad7hp-topo Materialsmentioning

confidence: 99%

Adsorption of Scandium Ions by Amberlite XAD7HP Polymeric Adsorbent Loaded with Tri-n-Octylphosphine Oxide

Daminescu,

Duteanu,

Ciopec

et al. 2024

Molecules

View full text Add to dashboard Cite

show abstract

“…n = number of data points X i = observed values Xi = predicted values R Squared-The coefficient of determination, abbreviated as R 2 or r 2 and pronounced "R squared" in statistics, is the fraction of the variation in the dependent variable that is predicted from the independent variable (Piepho 2023).…”

Section: Evaluation Of the Modelmentioning

confidence: 99%

Predicting the Productivity of Municipality Workers: A Comparison of Six Machine Learning Algorithms

Bijalwan,

Gupta,

Mendiratta

et al. 2024

Economies

View full text Add to dashboard Cite

One of the most significant areas of local government in the world is the municipality sector. It provides various services to the residents and businesses in their areas, such as water supply, sewage disposal, healthcare, education, housing, and transport. Municipalities also promote social and economic development and ensure democratic and accountable governance. It also helps in encouraging the involvement of communities in local matters. Workers of Municipalities need to maintain their services regularly to the public. The productivity of the employees is just one of the main important factors that influence the overall organizational performance. This article compares various machine learning algorithms such as XG Boost, Random Forest (RF), Histogram Gradient Boosting Regressor, LGBM Regressor, Ada Boost Regressor, and Gradient Boosting Regressor on the dataset of municipality workers. The study aims to propose a machine learning approach to predict and evaluate the productivity of municipality workers. The evaluation of the overall targeted and actual productivity of each department shows that out of 12 different departments, only 5 departments were able to meet their targeted productivity. A 3D Scatter plot visually displays the incentive given by the department to each worker based on their productivity. The results show that XG Boost performs best in comparison with the other five algorithms, as the value of R Squared is 0.71 and MSE (Mean Squared Error) is 0.01.

show abstract

“…Central to this process is selecting appropriate evaluation metrics and reporting on them consistently according to the TRIPOD guidelines (Collins et al, 2015). For regression models, R 2 and mean squared error (MSE; or variations thereof) are commonly used, although it is currently unclear how R 2 should be calculated in a multilevel setting (Piepho, 2023;Rights & Sterba, 2023). In classification tasks, common metrics include accuracy, sensitivity, and specificity.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

A Template and Tutorial for Preregistering Studies Using Passive Smartphone Measures

Langener,

Siepe,

Elsherif

et al. 2024

Preprint

View full text Add to dashboard Cite

Passive smartphone measures hold significant potential and are increasingly employed in psychological and biomedical research to capture an individual's behavior. These measures involve the near-continuous and unobtrusive collection of data from smartphones without requiring active input from participants. For example, GPS sensors are used to determine the (social) context of a person, and accelerometers to measure movement. However, utilizing passive smartphone measures presents methodological challenges during data collection and analysis. Researchers must make multiple decisions when working with such measures, which can result in different conclusions. Unfortunately, the transparency of these decision-making processes is often lacking. The implementation of open science practices is only beginning to emerge in digital phenotyping studies and varies widely across studies. Well-intentioned researchers may fail to report on some decisions due to the variety of choices that must be made. To address this issue and enhance reproducibility in digital phenotyping studies, we propose the adoption of preregistration as a way forward. Although there have been some attempts to preregister digital phenotyping studies, a template for registering such studies is currently missing. This could be problematic due to the high level of complexity that requires a well-structured template. Therefore, our objective was to develop a preregistration template that is easy to use and understandable for researchers. Additionally, we explain this template and provide resources to assist researchers in making informed decisions regarding data collection, cleaning, and analysis. Overall, we aim to make researchers' choices explicit, enhance transparency, and elevate the standards for studies utilizing passive smartphone measures.

show abstract

An adjusted coefficient of determination (R²) for generalized linear mixed models in one go

Cited by 16 publications

References 32 publications

Adsorption of Scandium Ions by Amberlite XAD7HP Polymeric Adsorbent Loaded with Tri-n-Octylphosphine Oxide

Adsorption of Scandium Ions by Amberlite XAD7HP Polymeric Adsorbent Loaded with Tri-n-Octylphosphine Oxide

Predicting the Productivity of Municipality Workers: A Comparison of Six Machine Learning Algorithms

A Template and Tutorial for Preregistering Studies Using Passive Smartphone Measures

Contact Info

Product

Resources

About

An adjusted coefficient of determination (R2) for generalized linear mixed models in one go

Cited by 16 publications

References 32 publications

Adsorption of Scandium Ions by Amberlite XAD7HP Polymeric Adsorbent Loaded with Tri-n-Octylphosphine Oxide

Adsorption of Scandium Ions by Amberlite XAD7HP Polymeric Adsorbent Loaded with Tri-n-Octylphosphine Oxide

Predicting the Productivity of Municipality Workers: A Comparison of Six Machine Learning Algorithms

A Template and Tutorial for Preregistering Studies Using Passive Smartphone Measures

Contact Info

Product

Resources

About

An adjusted coefficient of determination (R²) for generalized linear mixed models in one go