Adding datasets (#21)

* added datasets/ folder * added esoph dataset and README
storopoli · Dec 10, 2023 · fd6779d · fd6779d
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diff --git a/README.md b/README.md
@@ -61,6 +61,28 @@ Thousands of users rely on `Stan` for statistical modeling, data analysis, and p
 Models specified using `Turing.jl` are easy to read and write — models work the way you write them.
 Like everything in Julia, `Turing.jl` is [fast](https://arxiv.org/abs/2002.02702).
 
+## Datasets
+
+- `kidiq` (linear regression): 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).
+- `wells` (logistic regression): 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).
+- `esoph` (ordinal regression): data from a case-control study of (o)esophageal cancer in Ille-et-Vilaine, France.
+   Source: Breslow and Day (1980).
+- `roaches` (Poisson regression): data on the efficacy of a pest management system at reducing the number of roaches in urban apartments.
+   Source: Gelman and Hill (2007).
+- `duncan` (robust regression): data from occupation's prestige filled with outliers.
+   Source: Duncan (1961).
+- `cheese` (hierarchical models): data from cheese ratings.
+   A group of 10 rural and 10 urban raters rated 4 types of different cheeses (A, B, C and D) in two samples.
+   Source: Boatwright, McCulloch and Rossi (1999).
+
 ## Author
 
 Jose Storopoli, PhD - [*Lattes* CV](http://lattes.cnpq.br/2281909649311607) - [ORCID](https://orcid.org/0000-0002-0559-5176) - <https://storopoli.io>
@@ -77,7 +99,7 @@ I've made it to be how I would have liked to be introduced to Bayesian statistic
 
 ## References
 
-The references are divided in **books**, **papers** and **software**.
+The references are divided in **books**, **papers**, **software**, and **datasets**.
 
 ### Books
 
@@ -158,6 +180,14 @@ The papers section of the references are divided into **required** and **complem
     problems of p values. *Psychonomic Bulletin & Review*, *14*(5),
     779–804.
     https://doi.org/[10.3758/BF03194105](https://doi.org/10.3758/BF03194105)
+-   Vandekerckhove, J., Matzke, D., Wagenmakers, E.-J., & others. (2015).
+    Model comparison and the principle of parsimony.
+    In J. R. Busemeyer, Z. Wang, J. T. Townsend, & A. Eidels (Eds.),
+    Oxford handbook of computational and mathematical psychology (pp. 300–319).
+    Oxford University Press Oxford.
+-   Vehtari, A., Gelman, A., & Gabry, J. (2015). Practical Bayesian model evaluation
+    using leave-one-out cross-validation and WAIC.
+    https://doi.org/10.1007/s11222-016-9696-4
 
 #### Complementary
 
@@ -221,6 +251,14 @@ The papers section of the references are divided into **required** and **complem
 -   Tarek, M., Xu, K., Trapp, M., Ge, H., & Ghahramani, Z. (2020). DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models. ArXiv:2002.02702 [Cs, Stat]. http://arxiv.org/abs/2002.02702
 -   Xu, K., Ge, H., Tebbutt, W., Tarek, M., Trapp, M., & Ghahramani, Z. (2020). AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms. Symposium on Advances in Approximate Bayesian Inference, 1–10. http://proceedings.mlr.press/v118/xu20a.html
 
+### Datasets
+
+-   Boatwright, P., McCulloch, R., & Rossi, P. (1999). Account-level modeling for trade promotion: An application of a constrained parameter hierarchical model. _Journal of the American Statistical Association_, 94(448), 1063–1073.
+-   Breslow, N. E. & Day, N. E. (1980). **Statistical Methods in Cancer Research. Volume 1: The Analysis of Case-Control Studies**. IARC Lyon / Oxford University Press.
+-   Duncan, O. D. (1961). A socioeconomic index for all occupations. Class: Critical Concepts, 1, 388–426.
+-   Gelman, A., & Hill, J. (2007). **Data analysis using regression and
+    multilevel/hierarchical models**. Cambridge university press.
+
 ## How to cite
 
 To cite this course, please use:

diff --git a/datasets/cheese.csv b/datasets/cheese.csv
@@ -0,0 +1,161 @@
+"cheese","rater","background","y"
+"A",1,"rural",67
+"A",1,"rural",66
+"B",1,"rural",51
+"B",1,"rural",53
+"C",1,"rural",75
+"C",1,"rural",70
+"D",1,"rural",68
+"D",1,"rural",66
+"A",2,"rural",76
+"A",2,"rural",76
+"B",2,"rural",56
+"B",2,"rural",65
+"C",2,"rural",82
+"C",2,"rural",82
+"D",2,"rural",81
+"D",2,"rural",77
+"A",3,"rural",80
+"A",3,"rural",84
+"B",3,"rural",71
+"B",3,"rural",67
+"C",3,"rural",82
+"C",3,"rural",82
+"D",3,"rural",71
+"D",3,"rural",70
+"A",4,"rural",79
+"A",4,"rural",83
+"B",4,"rural",63
+"B",4,"rural",60
+"C",4,"rural",80
+"C",4,"rural",83
+"D",4,"rural",66
+"D",4,"rural",71
+"A",5,"rural",74
+"A",5,"rural",72
+"B",5,"rural",55
+"B",5,"rural",58
+"C",5,"rural",76
+"C",5,"rural",79
+"D",5,"rural",75
+"D",5,"rural",74
+"A",6,"rural",66
+"A",6,"rural",71
+"B",6,"rural",49
+"B",6,"rural",49
+"C",6,"rural",72
+"C",6,"rural",70
+"D",6,"rural",65
+"D",6,"rural",65
+"A",7,"rural",63
+"A",7,"rural",68
+"B",7,"rural",38
+"B",7,"rural",33
+"C",7,"rural",72
+"C",7,"rural",71
+"D",7,"rural",70
+"D",7,"rural",72
+"A",8,"rural",67
+"A",8,"rural",60
+"B",8,"rural",56
+"B",8,"rural",48
+"C",8,"rural",72
+"C",8,"rural",70
+"D",8,"rural",67
+"D",8,"rural",69
+"A",9,"rural",59
+"A",9,"rural",65
+"B",9,"rural",41
+"B",9,"rural",48
+"C",9,"rural",61
+"C",9,"rural",70
+"D",9,"rural",57
+"D",9,"rural",59
+"A",10,"rural",72
+"A",10,"rural",64
+"B",10,"rural",55
+"B",10,"rural",57
+"C",10,"rural",78
+"C",10,"rural",78
+"D",10,"rural",72
+"D",10,"rural",67
+"A",1,"urban",67
+"A",1,"urban",64
+"B",1,"urban",44
+"B",1,"urban",54
+"C",1,"urban",75
+"C",1,"urban",83
+"D",1,"urban",64
+"D",1,"urban",60
+"A",2,"urban",83
+"A",2,"urban",85
+"B",2,"urban",60
+"B",2,"urban",63
+"C",2,"urban",91
+"C",2,"urban",90
+"D",2,"urban",67
+"D",2,"urban",69
+"A",3,"urban",74
+"A",3,"urban",75
+"B",3,"urban",58
+"B",3,"urban",54
+"C",3,"urban",77
+"C",3,"urban",80
+"D",3,"urban",64
+"D",3,"urban",66
+"A",4,"urban",81
+"A",4,"urban",84
+"B",4,"urban",58
+"B",4,"urban",62
+"C",4,"urban",86
+"C",4,"urban",90
+"D",4,"urban",71
+"D",4,"urban",67
+"A",5,"urban",83
+"A",5,"urban",79
+"B",5,"urban",54
+"B",5,"urban",57
+"C",5,"urban",91
+"C",5,"urban",82
+"D",5,"urban",78
+"D",5,"urban",84
+"A",6,"urban",84
+"A",6,"urban",82
+"B",6,"urban",65
+"B",6,"urban",61
+"C",6,"urban",80
+"C",6,"urban",87
+"D",6,"urban",78
+"D",6,"urban",84
+"A",7,"urban",89
+"A",7,"urban",84
+"B",7,"urban",70
+"B",7,"urban",76
+"C",7,"urban",79
+"C",7,"urban",89
+"D",7,"urban",89
+"D",7,"urban",91
+"A",8,"urban",80
+"A",8,"urban",80
+"B",8,"urban",62
+"B",8,"urban",56
+"C",8,"urban",85
+"C",8,"urban",86
+"D",8,"urban",77
+"D",8,"urban",79
+"A",9,"urban",74
+"A",9,"urban",73
+"B",9,"urban",56
+"B",9,"urban",61
+"C",9,"urban",78
+"C",9,"urban",79
+"D",9,"urban",77
+"D",9,"urban",82
+"A",10,"urban",77
+"A",10,"urban",82
+"B",10,"urban",64
+"B",10,"urban",57
+"C",10,"urban",86
+"C",10,"urban",86
+"D",10,"urban",81
+"D",10,"urban",83
diff --git a/datasets/duncan.csv b/datasets/duncan.csv
@@ -0,0 +1,46 @@
+"profession","type","income","education","prestige"
+"accountant","prof",62,86,82
+"pilot","prof",72,76,83
+"architect","prof",75,92,90
+"author","prof",55,90,76
+"chemist","prof",64,86,90
+"minister","prof",21,84,87
+"professor","prof",64,93,93
+"dentist","prof",80,100,90
+"reporter","wc",67,87,52
+"engineer","prof",72,86,88
+"undertaker","prof",42,74,57
+"lawyer","prof",76,98,89
+"physician","prof",76,97,97
+"welfare.worker","prof",41,84,59
+"teacher","prof",48,91,73
+"conductor","wc",76,34,38
+"contractor","prof",53,45,76
+"factory.owner","prof",60,56,81
+"store.manager","prof",42,44,45
+"banker","prof",78,82,92
+"bookkeeper","wc",29,72,39
+"mail.carrier","wc",48,55,34
+"insurance.agent","wc",55,71,41
+"store.clerk","wc",29,50,16
+"carpenter","bc",21,23,33
+"electrician","bc",47,39,53
+"RR.engineer","bc",81,28,67
+"machinist","bc",36,32,57
+"auto.repairman","bc",22,22,26
+"plumber","bc",44,25,29
+"gas.stn.attendant","bc",15,29,10
+"coal.miner","bc",7,7,15
+"streetcar.motorman","bc",42,26,19
+"taxi.driver","bc",9,19,10
+"truck.driver","bc",21,15,13
+"machine.operator","bc",21,20,24
+"barber","bc",16,26,20
+"bartender","bc",16,28,7
+"shoe.shiner","bc",9,17,3
+"cook","bc",14,22,16
+"soda.clerk","bc",12,30,6
+"watchman","bc",17,25,11
+"janitor","bc",7,20,8
+"policeman","bc",34,47,41
+"waiter","bc",8,32,10
diff --git a/datasets/esoph.csv b/datasets/esoph.csv
@@ -0,0 +1,89 @@
+"agegp","alcgp","tobgp","ncases","ncontrols"
+"25-34","0-39g/day","0-9g/day",0,40
+"25-34","0-39g/day","10-19",0,10
+"25-34","0-39g/day","20-29",0,6
+"25-34","0-39g/day","30+",0,5
+"25-34","40-79","0-9g/day",0,27
+"25-34","40-79","10-19",0,7
+"25-34","40-79","20-29",0,4
+"25-34","40-79","30+",0,7
+"25-34","80-119","0-9g/day",0,2
+"25-34","80-119","10-19",0,1
+"25-34","80-119","30+",0,2
+"25-34","120+","0-9g/day",0,1
+"25-34","120+","10-19",1,0
+"25-34","120+","20-29",0,1
+"25-34","120+","30+",0,2
+"35-44","0-39g/day","0-9g/day",0,60
+"35-44","0-39g/day","10-19",1,13
+"35-44","0-39g/day","20-29",0,7
+"35-44","0-39g/day","30+",0,8
+"35-44","40-79","0-9g/day",0,35
+"35-44","40-79","10-19",3,20
+"35-44","40-79","20-29",1,13
+"35-44","40-79","30+",0,8
+"35-44","80-119","0-9g/day",0,11
+"35-44","80-119","10-19",0,6
+"35-44","80-119","20-29",0,2
+"35-44","80-119","30+",0,1
+"35-44","120+","0-9g/day",2,1
+"35-44","120+","10-19",0,3
+"35-44","120+","20-29",2,2
+"45-54","0-39g/day","0-9g/day",1,45
+"45-54","0-39g/day","10-19",0,18
+"45-54","0-39g/day","20-29",0,10
+"45-54","0-39g/day","30+",0,4
+"45-54","40-79","0-9g/day",6,32
+"45-54","40-79","10-19",4,17
+"45-54","40-79","20-29",5,10
+"45-54","40-79","30+",5,2
+"45-54","80-119","0-9g/day",3,13
+"45-54","80-119","10-19",6,8
+"45-54","80-119","20-29",1,4
+"45-54","80-119","30+",2,2
+"45-54","120+","0-9g/day",4,0
+"45-54","120+","10-19",3,1
+"45-54","120+","20-29",2,1
+"45-54","120+","30+",4,0
+"55-64","0-39g/day","0-9g/day",2,47
+"55-64","0-39g/day","10-19",3,19
+"55-64","0-39g/day","20-29",3,9
+"55-64","0-39g/day","30+",4,2
+"55-64","40-79","0-9g/day",9,31
+"55-64","40-79","10-19",6,15
+"55-64","40-79","20-29",4,13
+"55-64","40-79","30+",3,3
+"55-64","80-119","0-9g/day",9,9
+"55-64","80-119","10-19",8,7
+"55-64","80-119","20-29",3,3
+"55-64","80-119","30+",4,0
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+"55-64","120+","10-19",6,1
+"55-64","120+","20-29",2,1
+"55-64","120+","30+",5,1
+"65-74","0-39g/day","0-9g/day",5,43
+"65-74","0-39g/day","10-19",4,10
+"65-74","0-39g/day","20-29",2,5
+"65-74","0-39g/day","30+",0,2
+"65-74","40-79","0-9g/day",17,17
+"65-74","40-79","10-19",3,7
+"65-74","40-79","20-29",5,4
+"65-74","80-119","0-9g/day",6,7
+"65-74","80-119","10-19",4,8
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+"65-74","80-119","30+",1,0
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