MULTILEVEL NON-LINIER REGRESSION FOR REPEATED MEASURMENT DATA AS STUDY OF PEANUT GROWTH
Peanut is one of the most important legume commodities in Indonesia. In its implementation, a lot of research has been done related to this plant. However, in studies conducted by growth models, it is very rarely studied. Therefore, researchers are interested in modeling the growth of peanuts. One o...
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Universitas Pattimura
2022-09-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/5753 |
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| author | Arie Purwanto Umul Aiman |
| author_facet | Arie Purwanto Umul Aiman |
| author_sort | Arie Purwanto |
| collection | DOAJ |
| description | Peanut is one of the most important legume commodities in Indonesia. In its implementation, a lot of research has been done related to this plant. However, in studies conducted by growth models, it is very rarely studied. Therefore, researchers are interested in modeling the growth of peanuts. One of the models that can be used is a multilevel regression model for the case of repeated measurement data. Multilevel regression was chosen because it is considered to provide more information than other regression models. On the other hand, the nonlinear model was chosen based on the tendency of the initial plot of the data obtained. The research method used is a case study in the study of peanut growth. This study aims to build the best model based on the tested model. The Restricted Estimator Maximum Likelihood (REML) parameter estimation method was chosen because it is considered to have unbiased parameter estimates. The best model is based on the lowest Akaike Information Criterion (AIC) generated from a predetermined model. The results obtained indicate that the multilevel parabolic regression model is the model with the best AIC size. In addition, it was found that there was an Interclass Correlation (ICC) of 81.19% which indicated a difference in variability between levels. |
| format | Article |
| id | doaj-art-ec2ecba532984e18b3c66ffe0ce2d093 |
| institution | DOAJ |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-ec2ecba532984e18b3c66ffe0ce2d0932025-08-20T03:05:38ZengUniversitas PattimuraBarekeng1978-72272615-30172022-09-0116386186810.30598/barekengvol16iss3pp861-8685753MULTILEVEL NON-LINIER REGRESSION FOR REPEATED MEASURMENT DATA AS STUDY OF PEANUT GROWTHArie Purwanto0Umul Aiman1Mathematic Study Program, Faculty of Teacher Training and Education, Mercu Buana Yogyakarta UniversityAgrotechnology, Faculty of Agroindustry, Mercu Buana Yogyakarta UniversityPeanut is one of the most important legume commodities in Indonesia. In its implementation, a lot of research has been done related to this plant. However, in studies conducted by growth models, it is very rarely studied. Therefore, researchers are interested in modeling the growth of peanuts. One of the models that can be used is a multilevel regression model for the case of repeated measurement data. Multilevel regression was chosen because it is considered to provide more information than other regression models. On the other hand, the nonlinear model was chosen based on the tendency of the initial plot of the data obtained. The research method used is a case study in the study of peanut growth. This study aims to build the best model based on the tested model. The Restricted Estimator Maximum Likelihood (REML) parameter estimation method was chosen because it is considered to have unbiased parameter estimates. The best model is based on the lowest Akaike Information Criterion (AIC) generated from a predetermined model. The results obtained indicate that the multilevel parabolic regression model is the model with the best AIC size. In addition, it was found that there was an Interclass Correlation (ICC) of 81.19% which indicated a difference in variability between levels.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/5753peanutregressionmultilevel |
| spellingShingle | Arie Purwanto Umul Aiman MULTILEVEL NON-LINIER REGRESSION FOR REPEATED MEASURMENT DATA AS STUDY OF PEANUT GROWTH Barekeng peanut regression multilevel |
| title | MULTILEVEL NON-LINIER REGRESSION FOR REPEATED MEASURMENT DATA AS STUDY OF PEANUT GROWTH |
| title_full | MULTILEVEL NON-LINIER REGRESSION FOR REPEATED MEASURMENT DATA AS STUDY OF PEANUT GROWTH |
| title_fullStr | MULTILEVEL NON-LINIER REGRESSION FOR REPEATED MEASURMENT DATA AS STUDY OF PEANUT GROWTH |
| title_full_unstemmed | MULTILEVEL NON-LINIER REGRESSION FOR REPEATED MEASURMENT DATA AS STUDY OF PEANUT GROWTH |
| title_short | MULTILEVEL NON-LINIER REGRESSION FOR REPEATED MEASURMENT DATA AS STUDY OF PEANUT GROWTH |
| title_sort | multilevel non linier regression for repeated measurment data as study of peanut growth |
| topic | peanut regression multilevel |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/5753 |
| work_keys_str_mv | AT ariepurwanto multilevelnonlinierregressionforrepeatedmeasurmentdataasstudyofpeanutgrowth AT umulaiman multilevelnonlinierregressionforrepeatedmeasurmentdataasstudyofpeanutgrowth |