Visualize output of prcomp.
plot_prcomp( data, variance_cap = 0.8, maxcat = 50L, prcomp_args = list(scale. = TRUE), geom_label_args = list(), title = NULL, ggtheme = theme_gray(), theme_config = list(), nrow = 3L, ncol = 3L, parallel = FALSE )
Arguments
| data | input data |
|---|---|
| variance_cap | maximum cumulative explained variance allowed for all principal components. Default is 80%. |
| maxcat | maximum categories allowed for each discrete feature. The default is 50. |
| prcomp_args | a list of other arguments to prcomp |
| geom_label_args | a list of other arguments to geom_label |
| title | plot title starting from page 2. |
| ggtheme | complete ggplot2 themes. The default is theme_gray. |
| theme_config | a list of configurations to be passed to theme. |
| nrow | number of rows per page |
| ncol | number of columns per page |
| parallel | enable parallel? Default is |
Value
invisibly return the named list of ggplot objects
Details
When cumulative explained variance exceeds variance_cap, remaining principal components will be ignored. Set variance_cap to 1 for all principal components.
Discrete features containing more categories than maxcat specifies will be ignored.
Note
Discrete features will be dummify-ed first before passing to prcomp.
Missing values may create issues in prcomp. Consider na.omit your input data first.
Features with zero variance are dropped.
