explore_space_pca {ferrn} | R Documentation |
Plot the PCA projection of the projection bases space
Description
The set of functions returns a primary ggplot object
that plots the data object in a space reduced by PCA.
compute_pca()
computes the PCA and explore_space_pca()
plots the bases in the PCA-projected space
Usage
explore_space_pca(
dt,
details = FALSE,
pca = TRUE,
group = NULL,
color = NULL,
...,
animate = FALSE
)
flip_sign(dt, group = NULL, ...)
compute_pca(dt, group = NULL, random = TRUE, flip = TRUE, ...)
Arguments
dt |
a data object collected by the projection pursuit guided tour optimisation in |
details |
logical; if components other than start, end and interpolation need to be shown |
pca |
logical; if PCA coordinates need to be computed for the data |
group |
the variable to label different runs of the optimiser(s) |
color |
the variable to be coloured by |
... |
other arguments received from |
animate |
logical; if the interpolation path needs to be animated |
random |
logical; if random bases from the basis space need to be added to the data |
flip |
logical; if the sign flipping need to be performed |
Value
explore_space_pca()
a ggplot object for diagnosing the optimisers in the PCA-projected basis space
flip_sign()
a list containing
a matrix of all the bases
a logical value whether a flip of sign is performed
a dataframe of the original dataset
compute_pca()
a list containing
the PCA summary
a dataframe with PC coordinates augmented
See Also
Other main plot functions:
explore_space_tour()
,
explore_trace_interp()
,
explore_trace_search()
Examples
dplyr::bind_rows(holes_1d_geo, holes_1d_better) %>%
bind_theoretical(matrix(c(0, 1, 0, 0, 0), nrow = 5),
index = tourr::holes(), raw_data = boa5
) %>%
explore_space_pca(group = method, details = TRUE) +
scale_color_discrete_botanical()
dplyr::bind_rows(holes_1d_geo, holes_1d_better) %>%
flip_sign(group = method) %>%
str(max = 1)
dplyr::bind_rows(holes_1d_geo, holes_1d_better) %>% compute_pca(group = method)