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Generate sample data with a count outcome for bs_count() examples

Source: R/sample-data.R
sample_ps_data_count.Rd

Produces a synthetic data frame with a Poisson-distributed count outcome (rbc_tot, total intra-operative RBC units transfused), along with baseline covariates and an optional multiply-imputed stacked format. Mirrors the dataset used in tp.pm.count.balncing_score.sas.

Usage

sample_ps_data_count(n = 500L, seed = 42L, n_imputations = 1L)

Arguments

n

Number of patients. Default 500L.

seed

Random seed. Default 42L. The fixed default gives the same demo dataset on every call. NULL draws from the caller's random-number stream, as ps_match() does: the data differ from call to call, and set.seed() before the call reproduces them. A negative seed gives the same data as its absolute value.

n_imputations

If > 1, returns n_imputations datasets stacked in the long format, with an _IMPUTATION_ index column. Each is an independent draw of n patients, covariates and outcome alike, that reuses the id values 1 to n. They exercise the stacked format; they do not resemble real imputations, in which one patient's observed values and outcome agree across datasets. Default 1L (single complete dataset).

Value

A data frame with columns: id, rbc_tot (count), age, female, diabetes, hypertension, hct_pr (pre-op haematocrit), cpb_time (cardiopulmonary bypass time, minutes), and optionally _IMPUTATION_ when n_imputations > 1.

See also

bs_count(), bs_continuous()

Examples

dta <- sample_ps_data_count(n = 200, seed = 7)
hist(dta$rbc_tot, main = "RBC units distribution")


# Stacked multiply-imputed version (3 imputations)
dta_mi <- sample_ps_data_count(n = 200, seed = 7, n_imputations = 3)
table(dta_mi[["_IMPUTATION_"]])
#> 
#>   1   2   3 
#> 200 200 200