Extact PVDs from pickle file
Usage
extract_figs_from_pickle(
n_s = 1,
dataset_name = "sample_data",
output_folder = "output",
rda_filename = "data.RData",
picklename = paste0(dataset_name, "_subtype", n_s - 1, ".pickle"),
use_rds = TRUE,
biomarker_groups = readr::read_rds(fs::path(output_folder, "biomarker_groups.rds")),
biomarker_levels = readr::read_rds(fs::path(output_folder, "biomarker_levels.rds")),
...
)
Arguments
- n_s
number of latent subgroups; helps construct
picklename
- dataset_name
root name of dataset
- output_folder
where to find the dataset
- rda_filename
name of rda file containing environment used to run analyses
- picklename
the name of the pickle file to open
- use_rds
logical whether to use previously cached results
- biomarker_groups
data.frame with group colors, etc
- biomarker_levels
list containing biomarker ordinal level info
- ...
Arguments passed on to
plot_positional_var
samples_sequence
todo
samples_f
todo
n_samples
todo
score_vals
todo
biomarker_labels
todo
ml_f_EM
todo
cval
todo
subtype_order
todo
biomarker_order
todo
title_font_size
todo
stage_font_size
todo
stage_label
todo
stage_rot
todo
stage_interval
todo
label_font_size
todo
label_rot
todo
cmap
biomarker_colours
a character vector of colors
subtype_titles
todo
separate_subtypes
todo
save_path
todo
save_kwargs
todo
results
todo
biomarker_events_table
todo
biomarker_event_names
todo
biomarker_plot_order
todo
synchronize_y_axes
todo
use_labels
whether to use biomarker labels or variable names
Value
a "PVD_list
(a list of PVD
objects from autoplot.PF()
)
Examples
output_path <-
fs::path_package("extdata/sim_data", package = "fxtas")
if (dir.exists(output_path)) {
figs <- extract_figs_from_pickle(
output_folder = output_path,
n = 3
)
figs
}
#> $`Subtype 1`
#>
#> $`Subtype 2`
#>
#> $`Subtype 3`
#>
#> attr(,"class")
#> [1] "PVD_list" "list"
#> attr(,"biomarker_labels")
#> Biomarker 1 Biomarker 2 Biomarker 3 Biomarker 4 Biomarker 5
#> "Biomarker 1" "Biomarker 2" "Biomarker 3" "Biomarker 4" "Biomarker 5"
#> attr(,"biomarker_event_names")
#> <labelled<character>[15]>
#> [1] Biomarker 1: 1 Biomarker 2: 1 Biomarker 3: 1 Biomarker 4: 1 Biomarker 5: 1
#> [6] Biomarker 1: 2 Biomarker 2: 2 Biomarker 3: 2 Biomarker 4: 2 Biomarker 5: 2
#> [11] Biomarker 1: 3 Biomarker 2: 3 Biomarker 3: 3 Biomarker 4: 3 Biomarker 5: 3
#>
#> Labels:
#> value label
#> Biomarker 1: 0 Biomarker 1: 0
#> Biomarker 1: 1 Biomarker 1: 1
#> Biomarker 1: 2 Biomarker 1: 2
#> Biomarker 1: 3 Biomarker 1: 3
#> Biomarker 2: 0 Biomarker 2: 0
#> Biomarker 2: 1 Biomarker 2: 1
#> Biomarker 2: 2 Biomarker 2: 2
#> Biomarker 2: 3 Biomarker 2: 3
#> Biomarker 3: 0 Biomarker 3: 0
#> Biomarker 3: 1 Biomarker 3: 1
#> Biomarker 3: 2 Biomarker 3: 2
#> Biomarker 3: 3 Biomarker 3: 3
#> Biomarker 4: 0 Biomarker 4: 0
#> Biomarker 4: 1 Biomarker 4: 1
#> Biomarker 4: 2 Biomarker 4: 2
#> Biomarker 4: 3 Biomarker 4: 3
#> Biomarker 5: 0 Biomarker 5: 0
#> Biomarker 5: 1 Biomarker 5: 1
#> Biomarker 5: 2 Biomarker 5: 2
#> Biomarker 5: 3 Biomarker 5: 3
#> attr(,"biomarker_groups")
#> # A tibble: 5 × 3
#> biomarker biomarker_group group_color
#> * <chr> <chr> <chr>
#> 1 Biomarker 1 group 1 #88CCEE
#> 2 Biomarker 2 group 1 #88CCEE
#> 3 Biomarker 3 group 2 #CC6677
#> 4 Biomarker 4 group 2 #CC6677
#> 5 Biomarker 5 group 3 #888888
#> attr(,"biomarker_levels")
#> $`Biomarker 1`
#> [1] 0 1 2 3
#>
#> $`Biomarker 2`
#> [1] 0 1 2 3
#>
#> $`Biomarker 3`
#> [1] 0 1 2 3
#>
#> $`Biomarker 4`
#> [1] 0 1 2 3
#>
#> $`Biomarker 5`
#> [1] 0 1 2 3
#>
#> attr(,"class")
#> [1] "levels_list" "list"