# Summary Functions

#### 2026-03-03

There are two summary functions included with the rCISSVAE package that can help visualize the data clusters and model suitability to the data.

## Per-cluster Summary

The `cluster_summary()` function creates a data summary table stratified by missingness cluster. The function builds on `gtsummary::tbl_summary()`, so gtsummary-like statistics can be used for summarizing variables ( see tbl_summary() documentation for details ).

    library(tidyverse)
    library(reticulate)
    library(rCISSVAE)
    library(kableExtra)
    library(gtsummary)

    data(df_missing)
    data(clusters)

    ## Integer clusters must be passed in as a factor
    cluster_summary(data = df_missing, factor(clusters$clusters),
    include = setdiff(names(df_missing), "index"),
    statistic = list(
      all_continuous() ~ "{mean} ({sd})",
      all_categorical() ~ "{n} / {N}\n ({p}%)"),
      missing = "always")

Characteristic | N | 0 N = 2,000 1 | 1 N = 2,000 1 | 2 N = 2,000 1 | 3 N = 2,000 1
--- | --- | --- | --- | --- | ---
Age | 8,000 | 10.10 (2.04) | 10.19 (2.08) | 10.21 (2.14) | 10.29 (2.06)
Unknown |  | 0 | 0 | 0 | 0
Salary | 8,000 | 5.81 (0.61) | 5.83 (0.62) | 5.83 (0.61) | 5.81 (0.60)
Unknown |  | 0 | 0 | 0 | 0
ZipCode10001 | 8,000 | 646 / 2,000 (32%) | 674 / 2,000 (34%) | 663 / 2,000 (33%) | 645 / 2,000 (32%)
Unknown |  | 0 | 0 | 0 | 0
ZipCode20002 | 8,000 | 703 / 2,000 (35%) | 652 / 2,000 (33%) | 655 / 2,000 (33%) | 687 / 2,000 (34%)
Unknown |  | 0 | 0 | 0 | 0
ZipCode30003 | 8,000 | 651 / 2,000 (33%) | 674 / 2,000 (34%) | 682 / 2,000 (34%) | 668 / 2,000 (33%)
Unknown |  | 0 | 0 | 0 | 0
Y11 | 4,878 | -21 (10) | -16 (9) | 8 (5) | -3 (6)
Unknown |  | 1,281 | 1,288 | 0 | 553
Y12 | 4,882 | 69 (11) | -26 (9) | 55 (6) | -24 (8)
Unknown |  | 1,264 | 1,283 | 0 | 571
Y13 | 4,890 | 77 (12) | -25 (9) | 98 (12) | -17 (7)
Unknown |  | 1,289 | 1,264 | 0 | 557
Y14 | 4,871 | 73 (12) | -21 (8) | 125 (16) | -11 (6)
Unknown |  | 1,300 | 1,283 | 0 | 546
Y15 | 4,859 | 76 (12) | -12 (6) | 141 (19) | -14 (6)
Unknown |  | 1,273 | 1,293 | 0 | 575
Y21 | 4,865 | -33 (12) | -28 (11) | 1 (7) | -12 (7)
Unknown |  | 1,266 | 1,292 | 0 | 577
Y22 | 4,906 | 69 (12) | -40 (12) | 54 (6) | -36 (10)
Unknown |  | 1,266 | 1,276 | 0 | 552
Y23 | 4,902 | 79 (13) | -38 (11) | 104 (13) | -29 (9)
Unknown |  | 1,273 | 1,275 | 0 | 550
Y24 | 4,854 | 75 (12) | -32 (10) | 135 (18) | -22 (7)
Unknown |  | 1,302 | 1,287 | 0 | 557
Y25 | 4,894 | 78 (13) | -22 (8) | 153 (21) | -25 (8)
Unknown |  | 1,257 | 1,294 | 0 | 555
Y31 | 5,933 | -18 (10) | -13 (9) | 13 (5) | 1 (6)
Unknown |  | 192 | 1,285 | 0 | 590
Y32 | 5,944 | 74 (11) | -24 (10) | 62 (7) | -21 (8)
Unknown |  | 206 | 1,287 | 0 | 563
Y33 | 5,987 | 84 (13) | -23 (10) | 108 (13) | -14 (7)
Unknown |  | 203 | 1,267 | 0 | 543
Y34 | 5,949 | 81 (13) | -17 (8) | 136 (17) | -7 (6)
Unknown |  | 195 | 1,275 | 0 | 581
Y35 | 5,946 | 83 (13) | -8 (6) | 153 (20) | -10 (7)
Unknown |  | 204 | 1,285 | 0 | 565
Y41 | 5,968 | -8 (4) | -5 (3) | 6 (2) | 1 (2)
Unknown |  | 184 | 1,279 | 0 | 569
Y42 | 5,978 | 35 (6) | -11 (4) | 29 (4) | -9 (3)
Unknown |  | 199 | 1,282 | 0 | 541
Y43 | 5,987 | 39 (7) | -10 (3) | 49 (6) | -6 (3)
Unknown |  | 217 | 1,242 | 0 | 554
Y44 | 5,977 | 37 (7) | -8 (3) | 62 (9) | -3 (2)
Unknown |  | 186 | 1,280 | 0 | 557
Y45 | 5,914 | 39 (7) | -4 (3) | 70 (10) | -5 (2)
Unknown |  | 204 | 1,305 | 0 | 577
Y51 | 5,923 | -5.4 (3.6) | -2.9 (3.0) | 6.9 (1.9) | 2.5 (2.0)
Unknown |  | 222 | 1,279 | 0 | 576
Y52 | 5,966 | 32 (5) | -8 (3) | 26 (3) | -6 (3)
Unknown |  | 209 | 1,283 | 0 | 542
Y53 | 6,024 | 35 (6) | -6 (3) | 44 (6) | -3 (2)
Unknown |  | 184 | 1,243 | 0 | 549
Y54 | 5,953 | 34 (6) | -5 (3) | 55 (7) | -1 (2)
Unknown |  | 217 | 1,281 | 0 | 549
Y55 | 5,950 | 35 (6) | -2 (2) | 62 (9) | -2 (2)
Unknown |  | 207 | 1,292 | 0 | 551
1 Mean (SD); n / N (%)

## Missingness Heatmap

    cluster_heatmap(
      data = df_missing,
      clusters = paste0("Cluster ", clusters$clusters), ## Adds 'Cluster' to the cluster label
      cols_ignore = "index",
      observed_color = "#23013aff", ## A dark purple
      missing_color = "yellow")

    ## `use_raster` is automatically set to TRUE for a matrix with more than
    ## 2000 columns You can control `use_raster` argument by explicitly
    ## setting TRUE/FALSE to it.
    ##
    ## Set `ht_opt$message = FALSE` to turn off this message.

    ## 'magick' package is suggested to install to give better rasterization.
    ##
    ## Set `ht_opt$message = FALSE` to turn off this message.

## By-cluster imputation loss function

After running the model, you can get the per-cluster validation set imputation loss using the `performance_by_cluster()` function. Set ‘return_validation_dataset = TRUE’ in the `run_cissvae()` function to be able to use performance_by_cluster on the result object. If the validation dataset (val_data in result object) and imputed validation dataset (val_imputed in the result object) are not returned, the imputation loss cannot be calculated.

If the `run_cissvae()` function was used to generate clusters, set `return_clusters=TRUE` and the clusters will be part of the return object. Otherwise, use the ‘clusters’ parameter in `performance_by_cluster()` to input the clusters.

    result = run_cissvae(
      data = df_missing,
      index_col = "index",
      val_proportion = 0.1, ## pass a vector for different proportions by cluster
      columns_ignore = c("Age", "Salary", "ZipCode10001", "ZipCode20002", "ZipCode30003"), ## If there are columns in addition to the index you want to ignore when selecting validation set, list them here. In this case, we ignore the 'demographic' columns because we do not want to remove data from them for validation purposes.
      clusters = clusters$clusters, ## we have precomputed cluster labels so we pass them here
      epochs = 5,
      return_silhouettes = FALSE,
      return_history = TRUE,  # Get detailed training history
      verbose = FALSE,
      return_model = TRUE, ## Allows for plotting model schematic
      device = "cpu",  # Explicit device selection
      layer_order_enc = c("unshared", "shared", "unshared"),
      layer_order_dec = c("shared", "unshared", "shared"),
      return_validation_dataset = TRUE
    )

    cat(paste("Check necessary returns:", paste0(names(result), collapse = ", ")))

    ## Check necessary returns: imputed_dataset, model, training_history, val_data, val_imputed

    performance_by_cluster(res = result,
      group_col = NULL,
      clusters = clusters$clusters,
      feature_cols = NULL, ## default, all numeric columns excluding group_col & cols_ignore
      by_group = FALSE,
      by_cluster = TRUE,
      cols_ignore =  c( "index", "Age", "Salary", "ZipCode10001", "ZipCode20002", "ZipCode30003") ## columns to not score
      )

    ## $overall
    ##        mse bce imputation_error
    ## 1 90.99783  NA         90.99783
    ##
    ## $per_cluster
    ##   cluster       mse bce imputation_error
    ## 1       0  48.70336  NA         48.70336
    ## 2       1  96.68600  NA         96.68600
    ## 3       2  80.57138  NA         80.57138
    ## 4       3 142.86713  NA        142.86713