Use the provided propensity scores to stratify persons. Additional stratification variables for stratifications can also be used.

stratifyByPs( population, numberOfStrata = 5, stratificationColumns = c(), baseSelection = "all" )

population | A data frame with the three columns described below |
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numberOfStrata | How many strata? The boundaries of the strata are automatically defined to contain equal numbers of target persons. |

stratificationColumns | Names of one or more columns in the |

baseSelection | What is the base selection of subjects where the strata bounds are to be determined? Strata are defined as equally-sized strata inside this selection. Possible values are "all", "target", and "comparator". |

Returns a tibble with the same columns as the input data plus one extra column: stratumId.

The data frame should have the following three columns:

rowId (numeric): A unique identifier for each row (e.g. the person ID).

treatment (integer): Column indicating whether the person is in the target (1) or comparator (0) group.

propensityScore (numeric): Propensity score.

rowId <- 1:200 treatment <- rep(0:1, each = 100) propensityScore <- c(runif(100, min = 0, max = 1), runif(100, min = 0, max = 1)) data <- data.frame(rowId = rowId, treatment = treatment, propensityScore = propensityScore) result <- stratifyByPs(data, 5)