Plot the PCA projection of the projection bases space
Source:R/explore_space.R
explore_space_pca.RdThe 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
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
tourr- 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
explore_space_pca()- 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()
#> signs in all the bases will be flipped in group search_geodesic
#> Adding missing grouping variables: `method`
dplyr::bind_rows(holes_1d_geo, holes_1d_better) %>%
flip_sign(group = method) %>%
str(max = 1)
#> signs in all the bases will be flipped in group search_geodesic
#> List of 3
#> $ basis: num [1:495, 1:5] -0.341 -0.338 -0.344 -0.335 -0.347 ...
#> ..- attr(*, "dimnames")=List of 2
#> $ flip : logi TRUE
#> $ dt : tibble [495 × 8] (S3: tbl_df/tbl/data.frame)
dplyr::bind_rows(holes_1d_geo, holes_1d_better) %>% compute_pca(group = method)
#> signs in all the bases will be flipped in group search_geodesic
#> $pca_summary
#> Standard deviations (1, .., p=5):
#> [1] 1.0433992 1.0155630 0.9888052 0.9850722 0.9653222
#>
#> Rotation (n x k) = (5 x 5):
#> PC1 PC2 PC3 PC4 PC5
#> V1 0.5494898 -0.2012644 0.5927758 0.12915854 -0.5380414
#> V2 -0.3674142 0.5045363 0.1443116 0.73291782 -0.2290317
#> V3 -0.4473056 -0.4083083 0.6605713 0.06397122 0.4390390
#> V4 0.1028552 -0.6665882 -0.4230460 0.60415355 0.0333405
#> V5 0.5936381 0.3063988 0.1116634 0.27758911 0.6813143
#>
#> $aug
#> # A tibble: 2,995 × 14
#> basis index_val info method alpha tries loop id row_num PC1 PC2
#> <list> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 <dbl[…]> 0.749 new_b… PD 0.5 1 1 1 1 -1.83 -0.617
#> 2 <dbl[…]> 0.749 direc… PD NA 2 1 2 2 -1.84 -0.628
#> 3 <dbl[…]> 0.749 direc… PD NA 2 1 3 3 -1.83 -0.606
#> 4 <dbl[…]> 0.749 direc… PD NA 2 1 4 4 -1.83 -0.607
#> 5 <dbl[…]> 0.749 direc… PD NA 2 1 5 5 -1.83 -0.627
#> 6 <dbl[…]> 0.749 direc… PD NA 2 1 6 6 -1.83 -0.596
#> 7 <dbl[…]> 0.749 direc… PD NA 2 1 7 7 -1.83 -0.637
#> 8 <dbl[…]> 0.749 direc… PD NA 2 1 8 8 -1.82 -0.627
#> 9 <dbl[…]> 0.749 direc… PD NA 2 1 9 9 -1.84 -0.607
#> 10 <dbl[…]> 0.749 best_… PD NA 2 1 10 10 -1.83 -0.635
#> # … with 2,985 more rows, and 3 more variables: PC3 <dbl>, PC4 <dbl>, PC5 <dbl>
#>