The function acc_multivariate_outlier uses the same rules as acc_robust_univariate_outlier for the identification of outliers. In the following example, we examine systolic and diastolic blood pressure measurements in relation to age and weight, and a table output is created:

# Load dataquieR
library(dataquieR)

# Load data
sd1 <- prep_get_data_frame("ship")

# Load metadata
file_name <- system.file("extdata", "ship_meta_v2.xlsx", package = "dataquieR")
prep_load_workbook_like_file(file_name)
meta_data_item <- prep_get_data_frame("item_level") # item_level is a sheet in ship_meta_v2.xlsx

# Apply indicator function
MVO_SBP0.1 <- acc_multivariate_outlier(
  variable_group = c("SBP_0.1", "DBP_0.1", "AGE_0", "BODY_WEIGHT_0"),
  study_data = sd1,
  meta_data = meta_data_item,
  id_vars = "ID",
  label_col = "LABEL"
)
MVO_SBP0.1$SummaryTable
Variables Tukey (N) 3SD (N) Hubert (N) Sigma-gap (N) NUM_acc_ud_outlm PCT_acc_ud_outlm GRADING
SBP_0.1 | DBP_0.1 | AGE_0 | BODY_WEIGHT_0 108 37 46 3 3 0.14 1


The number of outliers varies considerably depending on the criterion. Subsequently a parallel-coordinate-plot may be requested to further inspect results:

MVO_SBP0.1$SummaryPlot

Another example is the inspection of the first and second systolic blood pressure measurements:

MVO_DBP <- acc_multivariate_outlier(
  variable_group = c("SBP_0.1", "SBP_0.2"),
  study_data = sd1,
  meta_data = meta_data_item,
  label_col = "LABEL"
)
MVO_DBP$SummaryTable
Variables Tukey (N) 3SD (N) Hubert (N) Sigma-gap (N) NUM_acc_ud_outlm PCT_acc_ud_outlm GRADING
SBP_0.1 | SBP_0.2 128 39 14 6 6 0.28 1 


MVO_DBP$SummaryPlot