Loads the classic Rossi recidivism study dataset used in biostatistical benchmarks for survival models with time-varying coefficients and covariates (e.g., JSS Vol 61, Code 01). The study tracks 432 convicts released from Maryland state prisons over a 52-week follow-up period.
Usage
rossi_data(format = c("csv", "sas"))Source
Rossi, P. H., Berk, R. A., & Lenihan, K. J. (1980). *Money, work, and crime: Some experimental results*. Academic Press.
Value
A tibble with 432 rows containing recidivism follow-up data:
- week
Follow-up time until arrest or censoring (1–52 weeks).
- arrest
Event indicator (1 = arrested, 0 = censored/did not re-offend).
- fin
Financial aid treatment (1 = received financial aid, 0 = control).
- age
Age at release from prison in years.
- race
Race indicator (1 = Black, 0 = other).
- wexp
Prior full-time work experience (1 = yes, 0 = no).
- mar
Marital status (1 = married, 0 = unmarried).
- paro
Release on parole (1 = yes, 0 = no).
- prio
Number of prior convictions.
- educ
Education level coded as 2 (grades 2-5) through 6 (some college).
- emp1–emp52
Weekly employment status indicator across the 52 weeks.
Examples
df <- rossi_data()
head(df[, 1:10])
#> # A tibble: 6 × 10
#> week arrest fin age race wexp mar paro prio educ
#> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 20 1 0 27 1 0 0 1 3 3
#> 2 17 1 0 18 1 0 0 1 8 4
#> 3 25 1 0 19 0 1 0 1 13 3
#> 4 52 0 1 23 1 1 1 1 1 5
#> 5 52 0 0 19 0 1 0 1 3 3
#> 6 52 0 0 24 1 1 0 0 2 4