Generating correlated discrete ordinal data using R and SAS IML.
Correlated ordinal data are common in many areas of research. The data may arise from longitudinal studies in biology, medical, or clinical fields. The prominent characteristic of these data is that the within-subject observations are correlated, whilst between-subject observations are independent....
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Elsevier
2011
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my.upm.eprints.247582015-09-23T02:30:26Z http://psasir.upm.edu.my/id/eprint/24758/ Generating correlated discrete ordinal data using R and SAS IML. Ibrahim, Noor Akma S. , Suliadi Correlated ordinal data are common in many areas of research. The data may arise from longitudinal studies in biology, medical, or clinical fields. The prominent characteristic of these data is that the within-subject observations are correlated, whilst between-subject observations are independent. Many methods have been proposed to analyze correlated ordinal data. One way to evaluate the performance of a proposed model or the performance of small or moderate size data sets is through simulation studies. It is thus important to provide a tool for generating correlated ordinal data to be used in simulation studies. In this paper, we describe a macro program on how to generate correlated ordinal data based on R language and SAS IML. Elsevier 2011 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/24758/1/Generating%20correlated%20discrete%20ordinal%20data%20using%20R%20and%20SAS%20IML.pdf Ibrahim, Noor Akma and S. , Suliadi (2011) Generating correlated discrete ordinal data using R and SAS IML. Computer Methods and Programs in Biomedicine , 104 (3). pp. 122-132. ISSN 0169-2607; ESSN:1872-7565 http://www.elsevier.com/ 10.1016/j.cmpb.2011.06.003 English |
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Universiti Putra Malaysia |
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English English |
| description |
Correlated ordinal data are common in many areas of research. The data may arise from longitudinal studies in biology, medical, or clinical fields. The prominent characteristic of these data is that the within-subject observations are correlated, whilst between-subject observations are independent. Many methods have been proposed to analyze correlated ordinal data. One way to evaluate the performance of a proposed model or the performance of small or moderate size data sets is through simulation studies. It is thus important to provide a tool for generating correlated ordinal data to be used in simulation studies. In this paper, we describe a macro program on how to generate correlated ordinal data based on R language and SAS IML. |
| format |
Article |
| author |
Ibrahim, Noor Akma S. , Suliadi |
| spellingShingle |
Ibrahim, Noor Akma S. , Suliadi Generating correlated discrete ordinal data using R and SAS IML. |
| author_facet |
Ibrahim, Noor Akma S. , Suliadi |
| author_sort |
Ibrahim, Noor Akma |
| title |
Generating correlated discrete ordinal data using R and SAS IML. |
| title_short |
Generating correlated discrete ordinal data using R and SAS IML. |
| title_full |
Generating correlated discrete ordinal data using R and SAS IML. |
| title_fullStr |
Generating correlated discrete ordinal data using R and SAS IML. |
| title_full_unstemmed |
Generating correlated discrete ordinal data using R and SAS IML. |
| title_sort |
generating correlated discrete ordinal data using r and sas iml. |
| publisher |
Elsevier |
| publishDate |
2011 |
| url |
http://psasir.upm.edu.my/id/eprint/24758/1/Generating%20correlated%20discrete%20ordinal%20data%20using%20R%20and%20SAS%20IML.pdf http://psasir.upm.edu.my/id/eprint/24758/ http://www.elsevier.com/ |
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13.251813 |