AUTO_VI$feature_pca_plot {autovi} | R Documentation |
Draw a summary Plot for principal component analysis conducted on extracted features
Description
This function draws a summary Plot for principal component analysis conducted on extracted features
Usage
AUTO_VI$feature_pca_plot( feature_pca = self$feature_pca(), x = PC1, y = PC2, col_by_set = TRUE)
Arguments
feature_pca |
Dataframe. A data frame containing the rotated features. |
x |
Symbol. The x variable. See also ggplot2::tidyeval. |
y |
Symbol. The y variable. See also ggplot2::tidyeval. |
col_by_set |
Boolena. Whether to color points by sets (observed, null, and boot). |
Details
By default, it will visualize PC2 vs PC1. User can choose to visualize other principal components.
Value
A ggplot
.
Examples
keras_model <- try(get_keras_model("vss_phn_32"))
if (!inherits(keras_model, "try-error")) {
myvi <- auto_vi(lm(dist ~ speed, data = cars), keras_model)
myvi$lineup_check(extract_feature_from_layer = "global_max_pooling2d")
myvi$feature_pca_plot()
}
[Package autovi version 0.4.0 Index]