The Experts below are selected from a list of 54831 Experts worldwide ranked by ideXlab platform

Peter Young - One of the best experts on this subject based on the ideXlab platform.

  • svy_logistic_Regression a generic sas macro for simple and multiple logistic Regression and creating quality publication ready tables using survey or non survey data
    PLOS ONE, 2019
    Co-Authors: Jacques Muthusi, Samuel Mwalili, Peter Young
    Abstract:

    Introduction Reproducible research is increasingly gaining interest in the research community. Automating the production of research manuscript tables from statistical software can help increase the reproducibility of findings. Logistic Regression is used in studying disease prevalence and associated factors in epidemiological studies and can be easily performed using widely available software including SAS, SUDAAN, Stata or R. However, Output from these software must be processed further to make it readily presentable. There exists a number of procedures developed to organize Regression Output, though many of them suffer limitations of flexibility, complexity, lack of validation checks for input parameters, as well as inability to incorporate survey design. Methods We developed a SAS macro, %svy_logistic_Regression, for fitting simple and multiple logistic Regression models. The macro also creates quality publication-ready tables using survey or non-survey data which aims to increase transparency of data analyses. It further significantly reduces turn-around time for conducting analysis and preparing Output tables while also addressing the limitations of existing procedures. In addition, the macro allows for user-specific actions to handle missing data as well as use of replication-based variance estimation methods. Results We demonstrate the use of the macro in the analysis of the 2013–2014 National Health and Nutrition Examination Survey (NHANES), a complex survey designed to assess the health and nutritional status of adults and children in the United States. The Output presented here is directly from the macro and is consistent with how Regression results are often presented in the epidemiological and biomedical literature, with unadjusted and adjusted model results presented side by side. Conclusions The SAS code presented in this macro is comprehensive, easy to follow, manipulate and to extend to other areas of interest. It can also be incorporated quickly by the statistician for immediate use. It is an especially valuable tool for generating quality, easy to review tables which can be incorporated directly in a publication.

  • svy_logistic_Regression a generic sas macro for simple and multiple logistic Regression and creating quality publication ready tables using survey or non survey data
    bioRxiv, 2019
    Co-Authors: Jacques Muthusi, Samuel Mwalili, Peter Young
    Abstract:

    Abstract Introduction Reproducible research is increasingly gaining interest in the research community. Automating the production of research manuscript tables from statistical software can help increase the reproducibility of findings. Logistic Regression is used in studying disease prevalence and associated factors in epidemiological studies and can be easily performed using widely available software including SAS, SUDAAN, Stata or R. However, Output from these software must be processed further to make it readily presentable. There exists a number of procedures developed to organize Regression Output, though many of them suffer limitations of flexibility, complexity, lack of validation checks for input parameters, as well as inability to incorporate survey design. Methods We developed a SAS macro, %svy_logistic_Regression, for fitting simple and multiple logistic Regression models. The macro also creates quality publication-ready tables using survey or non-survey data which aims to increase transparency of data analyses. It further significantly reduces turn-around time for conducting analysis and preparing Output tables while also addressing the limitations of existing procedures. Results We demonstrate the use of the macro in the analysis of the 2013-2014 National Health and Nutrition Examination Survey (NHANES), a complex survey designed to assess the health and nutritional status of adults and children in the United States. The Output presented here is directly from the macro and is consistent with how Regression results are often presented in the epidemiological and biomedical literature, with unadjusted and adjusted model results presented side by side. Conclusions The SAS code presented in this macro is comprehensive, easy to follow, manipulate and to extend to other areas of interest. It can also be incorporated quickly by the statistician for immediate use. It is an especially valuable tool for generating quality, easy to review tables which can be incorporated directly in a publication.

Jacques Muthusi - One of the best experts on this subject based on the ideXlab platform.

  • svy_logistic_Regression a generic sas macro for simple and multiple logistic Regression and creating quality publication ready tables using survey or non survey data
    PLOS ONE, 2019
    Co-Authors: Jacques Muthusi, Samuel Mwalili, Peter Young
    Abstract:

    Introduction Reproducible research is increasingly gaining interest in the research community. Automating the production of research manuscript tables from statistical software can help increase the reproducibility of findings. Logistic Regression is used in studying disease prevalence and associated factors in epidemiological studies and can be easily performed using widely available software including SAS, SUDAAN, Stata or R. However, Output from these software must be processed further to make it readily presentable. There exists a number of procedures developed to organize Regression Output, though many of them suffer limitations of flexibility, complexity, lack of validation checks for input parameters, as well as inability to incorporate survey design. Methods We developed a SAS macro, %svy_logistic_Regression, for fitting simple and multiple logistic Regression models. The macro also creates quality publication-ready tables using survey or non-survey data which aims to increase transparency of data analyses. It further significantly reduces turn-around time for conducting analysis and preparing Output tables while also addressing the limitations of existing procedures. In addition, the macro allows for user-specific actions to handle missing data as well as use of replication-based variance estimation methods. Results We demonstrate the use of the macro in the analysis of the 2013–2014 National Health and Nutrition Examination Survey (NHANES), a complex survey designed to assess the health and nutritional status of adults and children in the United States. The Output presented here is directly from the macro and is consistent with how Regression results are often presented in the epidemiological and biomedical literature, with unadjusted and adjusted model results presented side by side. Conclusions The SAS code presented in this macro is comprehensive, easy to follow, manipulate and to extend to other areas of interest. It can also be incorporated quickly by the statistician for immediate use. It is an especially valuable tool for generating quality, easy to review tables which can be incorporated directly in a publication.

  • svy_logistic_Regression a generic sas macro for simple and multiple logistic Regression and creating quality publication ready tables using survey or non survey data
    bioRxiv, 2019
    Co-Authors: Jacques Muthusi, Samuel Mwalili, Peter Young
    Abstract:

    Abstract Introduction Reproducible research is increasingly gaining interest in the research community. Automating the production of research manuscript tables from statistical software can help increase the reproducibility of findings. Logistic Regression is used in studying disease prevalence and associated factors in epidemiological studies and can be easily performed using widely available software including SAS, SUDAAN, Stata or R. However, Output from these software must be processed further to make it readily presentable. There exists a number of procedures developed to organize Regression Output, though many of them suffer limitations of flexibility, complexity, lack of validation checks for input parameters, as well as inability to incorporate survey design. Methods We developed a SAS macro, %svy_logistic_Regression, for fitting simple and multiple logistic Regression models. The macro also creates quality publication-ready tables using survey or non-survey data which aims to increase transparency of data analyses. It further significantly reduces turn-around time for conducting analysis and preparing Output tables while also addressing the limitations of existing procedures. Results We demonstrate the use of the macro in the analysis of the 2013-2014 National Health and Nutrition Examination Survey (NHANES), a complex survey designed to assess the health and nutritional status of adults and children in the United States. The Output presented here is directly from the macro and is consistent with how Regression results are often presented in the epidemiological and biomedical literature, with unadjusted and adjusted model results presented side by side. Conclusions The SAS code presented in this macro is comprehensive, easy to follow, manipulate and to extend to other areas of interest. It can also be incorporated quickly by the statistician for immediate use. It is an especially valuable tool for generating quality, easy to review tables which can be incorporated directly in a publication.

Samuel Mwalili - One of the best experts on this subject based on the ideXlab platform.

  • svy_logistic_Regression a generic sas macro for simple and multiple logistic Regression and creating quality publication ready tables using survey or non survey data
    PLOS ONE, 2019
    Co-Authors: Jacques Muthusi, Samuel Mwalili, Peter Young
    Abstract:

    Introduction Reproducible research is increasingly gaining interest in the research community. Automating the production of research manuscript tables from statistical software can help increase the reproducibility of findings. Logistic Regression is used in studying disease prevalence and associated factors in epidemiological studies and can be easily performed using widely available software including SAS, SUDAAN, Stata or R. However, Output from these software must be processed further to make it readily presentable. There exists a number of procedures developed to organize Regression Output, though many of them suffer limitations of flexibility, complexity, lack of validation checks for input parameters, as well as inability to incorporate survey design. Methods We developed a SAS macro, %svy_logistic_Regression, for fitting simple and multiple logistic Regression models. The macro also creates quality publication-ready tables using survey or non-survey data which aims to increase transparency of data analyses. It further significantly reduces turn-around time for conducting analysis and preparing Output tables while also addressing the limitations of existing procedures. In addition, the macro allows for user-specific actions to handle missing data as well as use of replication-based variance estimation methods. Results We demonstrate the use of the macro in the analysis of the 2013–2014 National Health and Nutrition Examination Survey (NHANES), a complex survey designed to assess the health and nutritional status of adults and children in the United States. The Output presented here is directly from the macro and is consistent with how Regression results are often presented in the epidemiological and biomedical literature, with unadjusted and adjusted model results presented side by side. Conclusions The SAS code presented in this macro is comprehensive, easy to follow, manipulate and to extend to other areas of interest. It can also be incorporated quickly by the statistician for immediate use. It is an especially valuable tool for generating quality, easy to review tables which can be incorporated directly in a publication.

  • svy_logistic_Regression a generic sas macro for simple and multiple logistic Regression and creating quality publication ready tables using survey or non survey data
    bioRxiv, 2019
    Co-Authors: Jacques Muthusi, Samuel Mwalili, Peter Young
    Abstract:

    Abstract Introduction Reproducible research is increasingly gaining interest in the research community. Automating the production of research manuscript tables from statistical software can help increase the reproducibility of findings. Logistic Regression is used in studying disease prevalence and associated factors in epidemiological studies and can be easily performed using widely available software including SAS, SUDAAN, Stata or R. However, Output from these software must be processed further to make it readily presentable. There exists a number of procedures developed to organize Regression Output, though many of them suffer limitations of flexibility, complexity, lack of validation checks for input parameters, as well as inability to incorporate survey design. Methods We developed a SAS macro, %svy_logistic_Regression, for fitting simple and multiple logistic Regression models. The macro also creates quality publication-ready tables using survey or non-survey data which aims to increase transparency of data analyses. It further significantly reduces turn-around time for conducting analysis and preparing Output tables while also addressing the limitations of existing procedures. Results We demonstrate the use of the macro in the analysis of the 2013-2014 National Health and Nutrition Examination Survey (NHANES), a complex survey designed to assess the health and nutritional status of adults and children in the United States. The Output presented here is directly from the macro and is consistent with how Regression results are often presented in the epidemiological and biomedical literature, with unadjusted and adjusted model results presented side by side. Conclusions The SAS code presented in this macro is comprehensive, easy to follow, manipulate and to extend to other areas of interest. It can also be incorporated quickly by the statistician for immediate use. It is an especially valuable tool for generating quality, easy to review tables which can be incorporated directly in a publication.

Scott Simpson - One of the best experts on this subject based on the ideXlab platform.

Cees G M Snoek - One of the best experts on this subject based on the ideXlab platform.

  • spherical Regression learning viewpoints surface normals and 3d rotations on n spheres
    Computer Vision and Pattern Recognition, 2019
    Co-Authors: Shuai Liao, Efstratios Gavves, Cees G M Snoek
    Abstract:

    Many computer vision challenges require continuous Outputs, but tend to be solved by discrete classification. The reason is classification's natural containment within a probability n-simplex, as defined by the popular softmax activation function. Regular Regression lacks such a closed geometry, leading to unstable training and convergence to suboptimal local minima. Starting from this insight we revisit Regression in convolutional neural networks. We observe many continuous Output problems in computer vision are naturally contained in closed geometrical manifolds, like the Euler angles in viewpoint estimation or the normals in surface normal estimation. A natural framework for posing such continuous Output problems are n-spheres, which are naturally closed geometric manifolds defined in the R^(n+1) space. By introducing a spherical exponential mapping on n-spheres at the Regression Output, we obtain well-behaved gradients, leading to stable training. We show how our spherical Regression can be utilized for several computer vision challenges, specifically viewpoint estimation, surface normal estimation and 3D rotation estimation. For all these problems our experiments demonstrate the benefit of spherical Regression. All paper resources are available at https://github.com/leoshine/Spherical_Regression.

  • spherical Regression learning viewpoints surface normals and 3d rotations on n spheres
    arXiv: Computer Vision and Pattern Recognition, 2019
    Co-Authors: Shuai Liao, Efstratios Gavves, Cees G M Snoek
    Abstract:

    Many computer vision challenges require continuous Outputs, but tend to be solved by discrete classification. The reason is classification's natural containment within a probability $n$-simplex, as defined by the popular softmax activation function. Regular Regression lacks such a closed geometry, leading to unstable training and convergence to suboptimal local minima. Starting from this insight we revisit Regression in convolutional neural networks. We observe many continuous Output problems in computer vision are naturally contained in closed geometrical manifolds, like the Euler angles in viewpoint estimation or the normals in surface normal estimation. A natural framework for posing such continuous Output problems are $n$-spheres, which are naturally closed geometric manifolds defined in the $\mathbb{R}^{(n+1)}$ space. By introducing a spherical exponential mapping on $n$-spheres at the Regression Output, we obtain well-behaved gradients, leading to stable training. We show how our spherical Regression can be utilized for several computer vision challenges, specifically viewpoint estimation, surface normal estimation and 3D rotation estimation. For all these problems our experiments demonstrate the benefit of spherical Regression. All paper resources are available at this https URL.