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Datasets for rstanarm examples

Source: R/doc-datasets.R
rstanarm-datasets.Rd

Small datasets for use in rstanarm examples and vignettes.

Format

bball1970

Data on hits and at-bats from the 1970 Major League Baseball season for 18 players.

Source: Efron and Morris (1975).

18 obs. of 5 variables

  • Player Player's last name

  • Hits Number of hits in the first 45 at-bats of the season

  • AB Number of at-bats (45 for all players)

  • RemainingAB Number of remaining at-bats (different for most players)

  • RemainingHits Number of remaining hits

bball2006

Hits and at-bats for the entire 2006 American League season of Major League Baseball.

Source: Carpenter (2009)

302 obs. of 2 variables

  • y Number of hits

  • K Number of at-bats

kidiq

Data from a survey of adult American women and their children (a subsample from the National Longitudinal Survey of Youth).

Source: Gelman and Hill (2007)

434 obs. of 4 variables

  • kid_score Child's IQ score

  • mom_hs Indicator for whether the mother has a high school degree

  • mom_iq Mother's IQ score

  • mom_age Mother's age

mortality

Surgical mortality rates in 12 hospitals performing cardiac surgery in babies.

Source: Spiegelhalter et al. (1996).

12 obs. of 2 variables

  • y Number of deaths

  • K Number of surgeries

pbcLong,pbcSurv

Longitudinal biomarker and time-to-event survival data for 40 patients with primary biliary cirrhosis who participated in a randomised placebo controlled trial of D-penicillamine conducted at the Mayo Clinic between 1974 and 1984.

Source: Therneau and Grambsch (2000)

304 obs. of 8 variables (pbcLong) and 40 obs. of 7 variables (pbcSurv)

  • age in years

  • albumin serum albumin (g/dl)

  • logBili logarithm of serum bilirubin

  • death indicator of death at endpoint

  • futimeYears time (in years) between baseline and the earliest of death, transplantion or censoring

  • id numeric ID unique to each individual

  • platelet platelet count

  • sex gender (m = male, f = female)

  • status status at endpoint (0 = censored, 1 = transplant, 2 = dead)

  • trt binary treatment code (0 = placebo, 1 = D-penicillamine)

  • year time (in years) of the longitudinal measurements, taken as time since baseline)

radon

Data on radon levels in houses in the state of Minnesota.

Source: Gelman and Hill (2007)

919 obs. of 4 variables

  • log_radon Radon measurement from the house (log scale)

  • log_uranium Uranium level in the county (log scale)

  • floor Indicator for radon measurement made on the first floor of the house (0 = basement, 1 = first floor)

  • county County name (factor)

roaches

Data on the efficacy of a pest management system at reducing the number of roaches in urban apartments.

Source: Gelman and Hill (2007)

262 obs. of 6 variables

  • y Number of roaches caught

  • roach1 Pretreatment number of roaches

  • treatment Treatment indicator

  • senior Indicator for only elderly residents in building

  • exposure2 Number of days for which the roach traps were used

tumors

Tarone (1982) provides a data set of tumor incidence in historical control groups of rats; specifically endometrial stromal polyps in female lab rats of type F344.

Source: Gelman and Hill (2007)

71 obs. of 2 variables

  • y Number of rats with tumors

  • K Number of rats

wells

A survey of 3200 residents in a small area of Bangladesh suffering from arsenic contamination of groundwater. Respondents with elevated arsenic levels in their wells had been encouraged to switch their water source to a safe public or private well in the nearby area and the survey was conducted several years later to learn which of the affected residents had switched wells.

Souce: Gelman and Hill (2007)

3020 obs. of 5 variables

  • switch Indicator for well-switching

  • arsenic Arsenic level in respondent's well

  • dist Distance (meters) from the respondent's house to the nearest well with safe drinking water.

  • assoc Indicator for member(s) of household participate in community organizations

  • educ Years of education (head of household)

References

Carpenter, B. (2009) Bayesian estimators for the beta-binomial model of batting ability. https://web.archive.org/web/20220618114439/https://lingpipe-blog.com/2009/09/23/

Efron, B. and Morris, C. (1975) Data analysis using Stein's estimator and its generalizations. Journal of the American Statistical Association 70(350), 311–319.

Gelman, A. and Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, Cambridge, UK. https://sites.stat.columbia.edu/gelman/arm/

Spiegelhalter, D., Thomas, A., Best, N., & Gilks, W. (1996) BUGS 0.5 Examples. MRC Biostatistics Unit, Institute of Public health, Cambridge, UK.

Tarone, R. E. (1982) The use of historical control information in testing for a trend in proportions. Biometrics 38(1):215–220.

Therneau, T. and Grambsch, P. (2000) Modeling Survival Data: Extending the Cox Model. Springer-Verlag, New York, US.

Examples

if (.Platform$OS.type != "windows" || .Platform$r_arch != "i386") {
# Using 'kidiq' dataset 
fit <- stan_lm(kid_score ~ mom_hs * mom_iq, data = kidiq, 
               prior = R2(location = 0.30, what = "mean"),
               # the next line is only to make the example go fast enough
               chains = 1, iter = 500, seed = 12345)
pp_check(fit, nreps = 20)
# \donttest{
bayesplot::color_scheme_set("brightblue")
pp_check(fit, plotfun = "stat_grouped", stat = "median", 
         group = factor(kidiq$mom_hs, labels = c("No HS", "HS")))
# }
}
#> 
#> SAMPLING FOR MODEL 'lm' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.24 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:   1 / 500 [  0%]  (Warmup)
#> Chain 1: Iteration:  50 / 500 [ 10%]  (Warmup)
#> Chain 1: Iteration: 100 / 500 [ 20%]  (Warmup)
#> Chain 1: Iteration: 150 / 500 [ 30%]  (Warmup)
#> Chain 1: Iteration: 200 / 500 [ 40%]  (Warmup)
#> Chain 1: Iteration: 250 / 500 [ 50%]  (Warmup)
#> Chain 1: Iteration: 251 / 500 [ 50%]  (Sampling)
#> Chain 1: Iteration: 300 / 500 [ 60%]  (Sampling)
#> Chain 1: Iteration: 350 / 500 [ 70%]  (Sampling)
#> Chain 1: Iteration: 400 / 500 [ 80%]  (Sampling)
#> Chain 1: Iteration: 450 / 500 [ 90%]  (Sampling)
#> Chain 1: Iteration: 500 / 500 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.782 seconds (Warm-up)
#> Chain 1:                0.264 seconds (Sampling)
#> Chain 1:                1.046 seconds (Total)
#> Chain 1: 
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.