I have a set data (100, 61), 100 is sampling point(observation), 61 is features. I have done data reducement using PCA before, but the plot of coeff 1 and 2 (PCA 1 and 2) has many overlap data. Hence I want to try LDA which has an euclidean distance between classes that expected can perform better than PCA. But I have difficulties to perform LDA using fisheriris function, I refer to this link
but those only plot two feature (PL and PW). But in my case I need to plot 61 features in the same plot as perform in PCA as coeff1/coeff2. How do I can perform data projection using LDA(which considering euclidean distance) ?