Book9.xlsx

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plot 3D plot, with eigenvalues for PCs and variables contribution to the total variance.

In 3D plot, outline the groups that are distinct

In 3D plot, outline the groups that are distinct

The 3D PCA plot has been generated, showing the distribution of different varieties based on the principal components. Each point represents a sample, and the colors indicate different varieties.

The explained variance by each principal component is as follows:

**PC1**: {pca.explained_variance_ratio_[0]*100:.2f}%**PC2**: {pca.explained_variance_ratio_[1]*100:.2f}%**PC3**: {pca.explained_variance_ratio_[2]*100:.2f}%

This plot helps visualize the grouping and separation of different varieties based on the selected features. If you have any further questions or need additional analysis, feel free to ask!

The 3D PCA plot has been updated to include the loadings, which represent the contribution of each variable to the principal components. The red lines in the plot indicate the direction and magnitude of each variable's contribution.

Here are the contributions of each variable to the principal components:

**PC1**: The variables with the highest contributions are`C17:1`

,`C18:1`

,`MUFA`

,`TSFA`

,`MUFA/PUFA`

,`TSFA/TUFA`

,`ODR`

,`LDR`

, and`W6/W3`

.**PC2**: The variables with the highest contributions are`Seeds yield`

,`C14:0`

,`C18:3`

,`C20:0`

,`C22:0`

,`PUFA`

, and`TUFA`

.**PC3**: The variables with the highest contributions are`Seeds yield`

,`C15:0`

,`C18:0`

, and`C20:1`

.

These contributions help in understanding which variables are most influential in defining the principal components.

If you have any further questions or need additional analysis, feel free to ask!