Abstract
One of the applications of Fisher's linear discriminant function (FDF) is its use in transforming multivariate data into a new univariate variable. This then makes possible a new option for the variance analysis of multivariate data, in addition to the multivariate analysis of variance (MANOVA). The aim of this work was to select groups of seven characteristics of quality in coffee seedlings using six criteria for selection, to use the FDF to transform such groupings of characteristics into a new variable, and then to compare interpretation of the results obtained from the univariate and multivariate analyses of variance of the characteristics and this new variable, with a view to its use in evaluating coffee seedlings. A randomised block design was used to assess the effect of organic fertiliser on the formation of seedlings in coffee cv. Catuai Vermelho IAC-44, evaluating the following characteristics: seedling height, diameter, root length, dry weight of shoots and roots, leaf area, number of leaves and total dry weight. According to the selection criteria used, different subsets of the selected characteristics are possible. The use of the FDF is shown to be viable in discriminating between treatments. Univariate analysis of the new variable obtained with the FDF and multivariate analysis (MANOVA) was able to detect differences between the treatments, however, it is simpler to apply FDF methodology.
Highlights
In experiments with coffee seedlings, usually more than one response variable are measured in order to improve characterisation of the plants and treatments being used
The transformed data obtained with the Fisher linear discriminant function for the 151 combinations of characteristics, underwent different tests for univariate and multivariate analysis
The characteristics combined for evaluation by multivariate analysis (MANAVA) or Fisher’s linear discriminant function (FDF) were denoted by CC
Summary
In experiments with coffee seedlings, usually more than one response variable are measured in order to improve characterisation of the plants and treatments being used. These experiments have as options in the evaluation, univariate analysis of variance (ANOVA), and multivariate analysis of variance (MANAVA), which is rarely used. It must be pointed out that the level of significance of this joint conclusion will not be known, as it derives from a combination of results. Whereas in the case of multivariate analysis, and despite the level of significance being determined, since the analysis is carried out using all the characteristics, it is more complex than univariate analysis, demanding a more careful interpretation of the results
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