Open julianesiebourg opened 1 year ago
This example data did not do well using the standard options for the plate scoring function in the multi_plate_wrapper.
samples <- tibble::tribble( ~OriginalSampleID, ~StudyID, ~Diagnosis, ~SeverityGroup, ~Status, ~Gender, ~Age, 1L, "S4", "Disease", "Severe", "high", "Male", 91, 2L, "S4", "Disease", "MildModerate", "normal", "Female", 80, 3L, "S4", "Disease", "MildModerate", "normal", "Male", 82, 4L, "S4", "Disease", "MildModerate", "normal", "Female", 66, 5L, "S4", "Disease", "MildModerate", "normal", "Male", 89, 6L, "S4", "Disease", "Severe", "normal", "Male", 64, 7L, "S4", "Disease", "Severe", "normal", "Male", 61, 8L, "S4", "Disease", "MildModerate", "normal", "Female", 77, 9L, "S4", "Disease", "Severe", "high", "Male", 86, 10L, "S4", "Disease", "MildModerate", "normal", "Male", 65, 11L, "S4", "Disease", "MildModerate", "normal", "Male", 73, 12L, "S4", "Disease", "MildModerate", "normal", "Male", 72, 13L, "S4", "Disease", "MildModerate", "normal", "Male", 76, 14L, "S4", "Disease", "MildModerate", "normal", "Male", 62, 15L, "S4", "Disease", "MildModerate", "normal", "Female", 78, 16L, "S4", "Control", "Control", "normal", "Female", 70, 17L, "S4", "Control", "Control", "normal", "Male", 75, 18L, "S4", "Control", "Control", "normal", "Female", 75, 19L, "S4", "Control", "Control", "normal", "Female", 73, 20L, "S4", "Control", "Control", "normal", "Female", 75, 21L, "S4", "Control", "Control", "normal", "Male", 57, 22L, "S4", "Control", "Control", "normal", "Female", 69, 23L, "S4", "Control", "Control", "normal", "Female", 74, 24L, "S4", "Control", "Control", "normal", "Female", 72, 25L, "S4", "Control", "Control", "normal", "Male", 63, 26L, "S4", "Control", "Control", "normal", "Male", 55, 27L, "S4", "Control", "Control", "normal", "Female", 55, 28L, "S4", "Control", "Control", "normal", "Female", 63, 29L, "S4", "Control", "Control", "normal", "Female", 66, 30L, "S3", "Control", "Control", "high", "Female", 48, 31L, "S3", "Control", "Control", "high", "Male", 63, 32L, "S2", "Control", "Control", "high", "Female", 67, 33L, "S2", "Control", "Control", "high", "Female", 82, 34L, "S2", "Control", "Control", "high", "Female", 81, 35L, "S1", "Control", "Control", "high", "Male", 72, 36L, "S1", "Control", "Control", "high", "Female", 63, 37L, "S2", "Control", "Control", "normal", "Female", 79, 38L, "S2", "Control", "Control", "normal", "Male", 72, 39L, "S2", "Control", "Control", "normal", "Male", 66, 40L, "S2", "Control", "Control", "normal", "Female", 77, 41L, "S2", "Control", "Control", "normal", "Male", 62, 42L, "S2", "Control", "Control", "normal", "Male", 81, 43L, "S2", "Control", "Control", "normal", "Male", 75, 44L, "S3", "Disease", "MildModerate", "normal", "Female", 47, 45L, "S3", "Disease", "MildModerate", "normal", "Male", 55, 46L, "S2", "Disease", "MildModerate", "normal", "Male", 75, 47L, "S2", "Disease", "MildModerate", "normal", "Female", 87, 48L, "S2", "Disease", "MildModerate", "normal", "Female", 53, 49L, "S2", "Disease", "MildModerate", "normal", "Female", 71, 50L, "S2", "Disease", "MildModerate", "normal", "Female", 78, 51L, "S2", "Disease", "MildModerate", "normal", "Female", 76, 52L, "S2", "Disease", "MildModerate", "normal", "Male", 73, 53L, "S2", "Disease", "MildModerate", "normal", "Female", 65, 54L, "S3", "Disease", "MildModerate", "high", "Female", 52, 55L, "S3", "Disease", "MildModerate", "high", "Male", 45, 56L, "S3", "Disease", "MildModerate", "high", "Male", 48, 57L, "S3", "Disease", "MildModerate", "high", "Male", 47, 58L, "S2", "Disease", "MildModerate", "high", "Female", 75, 59L, "S2", "Disease", "MildModerate", "high", "Male", 63, 60L, "S2", "Disease", "MildModerate", "high", "Male", 62, 61L, "S2", "Disease", "MildModerate", "high", "Female", 77, 62L, "S1", "Disease", "MildModerate", "high", "Female", 69, 63L, "S1", "Disease", "MildModerate", "high", "Male", 83, 64L, "S1", "Disease", "MildModerate", "high", "Female", 67, 65L, "S3", "Disease", "Severe", "normal", "Female", 56, 66L, "S3", "Disease", "Severe", "normal", "Male", 54, 67L, "S3", "Disease", "Severe", "normal", "Male", 50, 68L, "S2", "Disease", "Severe", "high", "Female", 74, 69L, "S2", "Disease", "Severe", "normal", "Male", 70, 70L, "S2", "Disease", "Severe", "normal", "Female", 75, 71L, "S2", "Disease", "Severe", "normal", "Male", 78, 72L, "S2", "Disease", "Severe", "normal", "Female", 57, 73L, "S2", "Disease", "Severe", "normal", "Male", 70, 74L, "S2", "Disease", "Severe", "normal", "Male", 72, 75L, "S2", "Disease", "Severe", "normal", "Female", 76, 76L, "S2", "Disease", "Severe", "high", "Male", 76, 77L, "S2", "Disease", "Severe", "high", "Male", 86, 78L, "S2", "Disease", "Severe", "high", "Male", 74, 79L, "S2", "Disease", "Severe", "high", "Male", 59, 80L, "S2", "Disease", "Severe", "high", "Female", 73, 81L, "S3", "Disease", "Severe", "high", "Male", 65, 82L, "S3", "Disease", "Severe", "high", "Female", 66, 83L, "S2", "Disease", "Severe", "high", "Female", 53, 84L, "S2", "Disease", "Severe", "high", "Male", 73, 85L, "S1", "Disease", "Severe", "high", "Female", 57, 86L, "S1", "Disease", "Severe", "high", "Male", 77 ) bc <- BatchContainer$new( dimensions = list( "plate" = 1, "row" = 8, "column" = 11 ) ) assign_random(bc, samples) # set scoring function for each balance variable scoring_funcs <- purrr::map( params$balance_variables, ~ mk_plate_scoring_functions(bc, row = "row", column = "column", group = .x, p = 2, penalize_lines = "soft") ) %>% unlist() names(scoring_funcs) <- params$balance_variables bc$scoring_f <- scoring_funcs traces <- optimize_design( bc, max_iter = 5000, quiet = TRUE, # not actually needed... since here is only one plate shuffle_proposal_func = mk_subgroup_shuffling_function( subgroup_vars = "plate", restrain_on_subgroup_levels = 1 ), acceptance_func = accept_leftmost_improvement )
Changing the mk_plate_scoring_functions options p = 1, penalize_lines = "none" works a lot better.
p = 1, penalize_lines = "none"
This example data did not do well using the standard options for the plate scoring function in the multi_plate_wrapper.
Changing the mk_plate_scoring_functions options
p = 1, penalize_lines = "none"works a lot better.