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題名 | 資料轉換在穩定性迴歸分析之適用性 |
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作者 | 呂秀英; | 書刊名 | 中華農學會報 |
卷期 | 159 1992.09[民81.09] |
頁次 | 頁29-40 |
分類號 | 434.28 |
關鍵詞 | 迴歸分析; 資料; 轉換; 穩定性; |
語文 | 中文(Chinese) |
中文摘要 | 直線迴歸模式能有效地用於穩定性分析,但必須滿足兩個重要的統計前提:(一)基因型與環境交感效應可以環境之直線函數解釋良好;(二)所有參式基因型之迴歸剩餘均方須互為均質。統計上一般解決均方異質的方法,是將資料予以適當的變數轉換;然而,轉換後的結果在生物學上之解釋往往難以切於實用。倘若轉換資料在理論上確有其必要性,卻未能行之,則恐怕又會對結果的解釋造成相當的影響。因此,本研究配合各種不同遺傳背景的實際植物生長資料之收集與分析,在原始資料無法符合穩定性的迴歸剩餘均方均質的情形下,給予最適之變數轉換,並就資料轉換對穩定性的評估結果之影響作深入之討,結果發現:轉換前後各品系之穩定性係數間仍存在顯著相關,但轉換後因為係數數值間之相對差異變小,有可能會使不穩定品系被誤判為穩定。因此,以穩定性之相對比較的觀點而言,資料轉換並不全然適於解決穩定性直線迴歸均方異質的問題。離值之存在,應是造成穩定性直線迴歸均方異質的主要因素。偵除離值將可更有效地解決迴歸剩餘均方異質的問題,其所評價的品系穩定性結果在刪除離值前後完全一致。 |
英文摘要 | Linear regression model is useful to evaluate the adaptation of a quantitative character to the change of environments. Its proper use requires that: (i) genotype x environment interactions are adequately explained by a linear function of the environment; and (ii) the residual mean squares from the regression are homogeneous across all genotypes. The transformation of data is a well-known technique for reducing error heaterogeneity. From a practical standpoint, the data are usually difficultly interpreted on the transformed scale of measurement. Questions therefore need to be asked about the consequences of using or omitting a transformation when theoretical reasons establish a clear necessity. This study is to confirm to applicability of data transformation by analysing various set of trial data, if their residual mean squares from the linear regression are not homogeneous across all genotypes. Results showed that althought positive correlation between with and without data transformation occurred, an unstable genotype might be wrongly regarded as stable because of the removal of heterogeneity among regressions. Thus the data transformation is not an optimal approach to the error homogeneity of regression analysis in plant stability. Removal of outliers may be an useful alternative in homogenizing the residual variance because no effects on the assessment of stability was found. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。