- 學位論文
以離散時間存活分析方法分析生物防治資料
Applications of discrete-time survival analysis to biological control data
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摘要
在論文中我們以離散時間存活模式來分析生物防治資料。所分析的資料由國立屏東科技大學植物保護系 生物防治研究室提供。試驗的資料包含三種立枯絲核菌（Rhizoctonia solani Ktihn），代號為：R1、R2、R3，和七個木黴菌（Trichoderma spp.），其代號分別為：T1-T7。主要分析目的是找出哪一種木黴菌，對立枯絲核菌菌絲的生長抑制效果較佳。 當事件的時間中如果有太多的等值之情況時，以離散時間存活模式來分析較為合適。透過模型的建立與畫出風險函數圖跟存活函數圖，我們就可以知道不同的木黴菌拮抗立枯絲核菌的功效如何。另外，我們探討了模型中的時間變數之另一種替代形式與使用不同的連結函數對結果的影響。我們並針對所建立的模型之假設做驗證。最後我們再將結果和詹(2003)與林(2007)結果做比較。
並列摘要
This thesis uses the Discrete-Time Survival model to analyze the biological control data. The data was provided by Biological Control Laboratory, Department of Plant Protection, National PingTung University of Science & Technology. In the experiment , three kinds of Rhizoctonia solani Ktihn( R1, R2 and R3 respectively), and seven Trichoderma spp. (T1-T7) were used. The main purpose is to determine which Trichoderma spp. would best restrain the propagation of Rhizoctonia solani Ktihn. When event times are highly discrete due to a problem known as “ties”, the Discrete-Time Survival model is suitable to analyze the data. By constructing the models, plotting the hazard functions and survival functions, we can know the effects of different Trichoderma spp. that restrain the propagation of Rhizoctonia solani Ktihn. Moreover, this thesis discusses the alternative specification of time and the effect of using another link function. In addition, we also check the assumptions of the model. Finally, the results are also compared with those obtained by Chan(2003) and Lin(2007).
參考文獻
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延伸閱讀
- Yan, B. T. (2015). 以時間解析紅外差異吸收光譜法結合數學的相關性分析方法研究細菌視紫質光迴圈的質子傳遞過程與結構變化 [master's thesis, National Tsing Hua University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0016-0508201514075141
- 徐邦晏、趙育德、曾冠錡、歐柏廷、龔思豪（2020）。保存型與去活化型病毒檢體運送培養基的效能評估。檢驗及品保雜誌，9(4)，165-174。https://doi.org/10.6573/JTQA.202010_9(4).01
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- Lunchick, C., Honeycutt, R., & Klonne, D. (2005). Biological Monitoring Use in Refining the Exposure Assessment of Agricultural Operators. Scandinavian Journal of Work, Environment & Health, 31(suppl 1), 82-89. https://www.airitilibrary.com/Article/Detail?DocID=03553140-200512-201012010072-201012010072-82-89
- Ferber, K., & Archer, K. J. (2015). Modeling Discrete Survival Time Using Genomic Feature Data. Cancer Informatics, (2015-S2), 37-43. https://doi.org/10.4137/CIN.S17275