From 24dd21c7c4fa7719a682209206e441ba414a2ba6 Mon Sep 17 00:00:00 2001 From: erdogant Date: Tue, 17 Dec 2024 14:01:19 +0100 Subject: [PATCH] update docs --- docs/source/Plots.rst | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/docs/source/Plots.rst b/docs/source/Plots.rst index 33319b2..bddf53b 100644 --- a/docs/source/Plots.rst +++ b/docs/source/Plots.rst @@ -83,6 +83,14 @@ Scatter plot Biplot ############### +The biplot show the loadings (arrows) together with the samples (scatterplot). +The loadings can be colored red and blue which indicates the strength of the particular feature in the PC. + +For each principal component (PC), the feature is determined with the largest absolute loading. This indicates which feature contributes the most to each PC and can occur in multiple PCs. +The highest loading values for the features are colored red in the biplot and described as "best" in the output dataframe. +The features that were not seen with highest loadings for any PC are considered weaker features, and are colored blue the biplot. In the output dataframe these features are described as "weak". + + .. code:: python # Make biplot @@ -91,6 +99,25 @@ Biplot # Here again, many other options can be turned on and off model.biplot(SPE=True, HT2=True, legend=1) + # Show the top features + results['topfeat'] + + + # PC feature loading type + # 0 PC1 flavanoids 0.422934 best + # 1 PC2 color_intensity 0.529996 best + # 2 PC3 ash 0.626224 best + # 3 PC4 malic_acid 0.536890 best + # 4 PC5 magnesium 0.727049 best + # 5 PC6 malic_acid 0.536814 best + # 6 PC7 nonflavanoid_phenols 0.595447 best + # 7 PC8 hue 0.436624 best + # 8 PC9 proline 0.575786 best + # 9 PC10 od280/od315_of_diluted_wines 0.523706 best + # 10 PC9 alcohol -0.508619 weak + # 11 PC3 alcalinity_of_ash 0.612080 weak + # 12 PC8 total_phenols -0.405934 weak + # 13 PC6 proanthocyanins -0.533795 weak .. |figP5| image:: ../figs/wine_biplot.png .. |figP6| image:: ../figs/wine_biplot_with_outliers.png