stan.models: stan.models
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Description
Usage
Format
Examples
Description
data.frame containing the structure of the github repository https://github.com/stan-dev/example-models that contains
examples to run STAN models in R from the book by Gelman and Hill 'Data Analysis
Using Regression Analysis and Multilevel/Hierarchical Models'.
Usage
1
Format
An object of class "data.frame"
Examples
1
2
Example output
Warning message:
In data(stan.models) : data set 'stan.models' not found
chapter r.files
1 3 3.1_OnePredictor.R
2 3 3.1_OnePredictor.R
3 3 3.2_MultiplePredictors.R
4 3 3.3_Interactions.R
5 3 3.4_StatInference.R
6 3 3.5_GraphDisplays.R
7 3 3.5_GraphDisplays.R
8 3 3.5_GraphDisplays.R
9 3 3.6_Diagnostics.R
10 4 4.1_LinearTransformations.R
11 4 4.2_Centering&Standardizing.R
12 4 4.2_Centering&Standardizing.R
13 4 4.2_Centering&Standardizing.R
14 4 4.2_Centering&Standardizing.R
15 4 4.4_LogTransformations.R
16 4 4.4_LogTransformations.R
17 4 4.4_LogTransformations.R
18 4 4.4_LogTransformations.R
19 4 4.5_OtherTransformations.R
20 4 4.6_RegressionModelsForPrediction.R
21 4 4.6_RegressionModelsForPrediction.R
22 4 4.6_RegressionModelsForPrediction.R
23 4 4.6_RegressionModelsForPrediction.R
24 4 4.6_RegressionModelsForPrediction.R
25 4 4.6_RegressionModelsForPrediction.R
26 5 5.1_LogisticRegressionWithOnePredictor.R
27 5 5.2_InterpretingLogisticRegressionCoef.R
28 5 5.4_LogisticRegressionWellsinBangladesh.R
29 5 5.4_LogisticRegressionWellsinBangladesh.R
30 5 5.4_LogisticRegressionWellsinBangladesh.R
31 5 5.5_LogisticRegressionWithInteractions.R
32 5 5.5_LogisticRegressionWithInteractions.R
33 5 5.5_LogisticRegressionWithInteractions.R
34 5 5.5_LogisticRegressionWithInteractions.R
35 5 5.5_LogisticRegressionWithInteractions.R
36 5 5.6_EvaluatingCheckingComparing.R
37 5 5.6_EvaluatingCheckingComparing.R
38 5 5.7_AveragePredictiveComparisons.R
39 5 5.7_AveragePredictiveComparisons.R
40 5 5.8_IdentifiabilityAndSeparation.R
41 6 6.4_ProbitRegression.R
42 6 6.7_MoreComplexGLM.R
43 6 6.7_MoreComplexGLM.R
44 6 6.8_ConstructiveChoiceModels.R
45 7 7.3_SimulationForNonLinearPredictions.R
46 7 7.4_PredictiveSimulationForGLM.R
47 7 7.4_PredictiveSimulationForGLM.R
48 7 7.4_PredictiveSimulationForGLM.R
49 8 8.2_FakeDataSimulationToUnderstandResidualPlots.R
50 8 8.3_SimulatingFromTheFittedModel.R
51 8 8.3_SimulatingFromTheFittedModel.R
52 8 8.3_SimulatingFromTheFittedModel.R
53 8 8.4_PredictiveSimulationToCheckFitOfTimeSeriesModels.R
54 9 9.3_RandomizedExperiments.R
55 9 9.3_RandomizedExperiments.R
56 9 9.4_TreatmentInteractionsAndPoststratification.R
57 9 9.4_TreatmentInteractionsAndPoststratification.R
58 9 9.4_TreatmentInteractionsAndPoststratification.R
59 9 9.4_TreatmentInteractionsAndPoststratification.R
60 9 9.5_ObservationalStudies.R
61 10 10.4_LackOfOverlapWhenTreat.AssignmentIsUnknown.R
62 10 10.4_LackOfOverlapWhenTreat.AssignmentIsUnknown.R
63 10 10.4_LackOfOverlapWhenTreat.AssignmentIsUnknown.R
64 10 10.4_LackOfOverlapWhenTreat.AssignmentIsUnknown.R
65 10 10.5_CasualEffectsUsingIV.R
66 10 10.5_CasualEffectsUsingIV.R
67 10 10.6_IVinaRegressionFramework.R
68 10 10.6_IVinaRegressionFramework.R
69 12 12.2_PartialPoolingWithNoPredictors.R
70 12 12.3_PartialPoolingWithPredictors.R
71 12 12.3_PartialPoolingWithPredictors.R
72 12 12.4_FittingMLMinR.R
73 12 12.4_FittingMLMinR.R
74 12 12.8_Prediction.R
75 13 13.1_VaryingIntercepts&Slopes.R
76 13 13.1_VaryingIntercepts&Slopes.R
77 13 13.1_VaryingIntercepts&Slopes.R
78 13 13.1_VaryingIntercepts&Slopes.R
79 13 13.4_UnderstandingCorrelationsBetweenIntercepts&Slopes.R
80 13 13.4_UnderstandingCorrelationsBetweenIntercepts&Slopes.R
81 13 13.5_Non-NestedModels.R
82 13 13.5_Non-NestedModels.R
83 14 14.1_State-LevelOpinionsFromNationalPolls.R
84 14 14.1_State-LevelOpinionsFromNationalPolls.R
85 19 19.4_RedundantParameters&IntentionallyNonidentifiableModels.R
86 19 19.4_RedundantParameters&IntentionallyNonidentifiableModels.R
87 19 19.4_RedundantParameters&IntentionallyNonidentifiableModels.R
88 19 19.4_RedundantParameters&IntentionallyNonidentifiableModels.R
89 19 19.5_ParameterExpansion.R
90 19 19.5_ParameterExpansion.R
91 20 20.5_MultilevelPowerCalculationUsingFake-DataSimulation.R
92 20 20.5_MultilevelPowerCalculationUsingFake-DataSimulation.R
93 21 21.6_SummarizingtheAmmountofPartialPooling.R
94 21 21.6_SummarizingtheAmmountofPartialPooling.R
95 21 21.7_AddingAPredictorCanIncreaseResidualVariance.R
96 21 21.7_AddingAPredictorCanIncreaseResidualVariance.R
97 21 21.7_AddingAPredictorCanIncreaseResidualVariance.R
98 22 22.4_DoingANOVUsingMLM.R
99 23 23.1_MultilevelAspectsofDataCollection.R
100 23 23.1_MultilevelAspectsofDataCollection.R
101 23 23.1_MultilevelAspectsofDataCollection.R
102 23 23.1_MultilevelAspectsofDataCollection.R
103 23 23.1_MultilevelAspectsofDataCollection.R
104 24 24.2_BehavioralLearningExperiment.R
105 25 25.4_RadomImputationofaSingleVariable.R
106 25 25.4_RadomImputationofaSingleVariable.R
107 25 25.4_RadomImputationofaSingleVariable.R
108 25 25.4_RadomImputationofaSingleVariable.R
109 25 25.5_ImputationofSeveralMissingVariables.R
stan.files stan.obj.output
1 kidscore_momhs.stan kidscore_momhs
2 kidscore_momiq.stan kidscore_momiq
3 kidiq_multi_preds.stan kidiq_multi_preds
4 kidiq_interaction.stan kidiq_interaction
5 kidiq_multi_preds.stan kidiq_multi_preds
6 kidscore_momiq.stan stanfit.2
7 kidiq_multi_preds.stan stanfit.3
8 kidiq_interaction.stan stanfit.4
9 kidscore_momiq.stan kidscore_momiq.sf
10 earn_height.stan earn_height
11 kidiq_interaction.stan kidiq_interaction
12 kidiq_interaction_c.stan kidiq_interaction_c
13 kidiq_interaction_c2.stan kidiq_interaction_c2
14 kidiq_interaction_z.stan kidiq_interaction_z
15 logearn_height.stan logearn_height.sf
16 log10earn_height.stan log10earn_height.sf
17 logearn_height_male.stan logearn_height_male.sf
18 logearn_logheight.stan logearn_logheight.sf
19 kidscore_momwork.stan kidscore_momwork.sf
20 mesquite.stan mesquite.sf
21 mesquite_log.stan mesquite_log.sf
22 mesquite_volume.stan mesquite_volume.sf
23 mesquite_vas.stan mesquite_vas.sf
24 mesquite_va.stan mesquite_va.sf
25 mesquite_vash.stan mesquite_vash.sf
26 nes_logit.stan nes_logit.sf
27 nes_logit.stan sf
28 wells_dist.stan wells_dist.sf
29 wells_dist100.stan wells_dist100.sf
30 wells_d100ars.stan wells_d100ars.sf
31 wells_interaction.stan wells_interaction.sf
32 wells_interaction_c.stan wells_interaction_c.sf
33 wells_daae_c.stan wells_daae_c.sf
34 wells_dae_c.stan wells_dae_c.sf
35 wells_dae_inter_c.stan wells_dae_inter_c.sf
36 wells_predicted.stan wells_predicted.sf
37 wells_predicted_log.stan wells_predicted_log.sf
38 wells_dae.stan wells_dae.sf
39 wells_dae_inter.stan wells_dae_inter.sf
40 separation.stan separation.sf
41 wells_probit.stan wells_probit.sf1
42 earnings1.stan earnings1.sf1
43 earnings2.stan earnings2.sf1
44 wells_logit.stan wells_logit.sf1
45 congress.stan congress.sf1
46 wells.stan wells.sf1
47 earnings1.stan earnings1.sf1
48 earnings2.stan earnings2.sf1
49 grades.stan grades.sf1
50 lightspeed.stan lightspeed.sf1
51 roaches.stan roaches.sf1
52 roaches_overdispersion.stan roaches_overdispersion.sf1
53 unemployment.stan unemployment.sf1
54 electric_tr.stan sf.1
55 electric_trpre.stan sf.2
56 electric_tr.stan electric_tr.sf
57 electric_trpre.stan electric_trpre.sf
58 electric_inter.stan electric_inter.sf
59 electric_inter.stan sf
60 electric_supp.stan sf
61 ideo_two_pred.stan ideo_two_pred.sf1
62 ideo_two_pred.stan ideo_two_pred.sf2
63 ideo_interactions.stan ideo_interactions.sf1
64 ideo_reparam.stan ideo_reparam.sf1
65 sesame_one_pred_a.stan sesame_one_pred_a.sf1
66 sesame_one_pred_a.stan sesame_one_pred_b.sf1
67 sesame_one_pred_a.stan sesame_one_pred_2a.sf1
68 sesame_one_pred_a.stan sesame_one_pred_2b.sf1
69 radon_intercept.stan radon_intercept.sf1
70 radon_complete_pool.stan radon_complete_pool.sf1
71 radon_no_pool.stan radon_no_pool.sf1
72 radon_intercept.stan radon_intercept.sf1
73 radon_no_pool.stan radon_no_pool.sf1
74 radon_group.stan #radon_group.sf1
75 radon_vary_si.stan radon_vary_si.sf1
76 y_x.stan radon_no_pool.sf1
77 y_x.stan radon_complete_pool.sf1
78 radon_inter_vary.stan radon_inter_vary.sf1
79 earnings_vary_si.stan earnings_vary_si.sf1
80 earnings_vary_si.stan earnings_vary_si.sf2
81 pilots.stan pilots.sf1
82 earnings_latin_square.stan earnings_latin_square.sf1
83 election88.stan election88.sf1
84 election88_full.stan election88_full.sf1
85 radon.stan radon.sf1
86 radon_redundant.stan radon_redundant.sf1
87 pilots.stan pilots.sf1
88 election88.stan election88.sf1
89 pilots_expansion.stan pilots_expansion.sf1
90 election88_expansion.stan election88_expansion.sf1
91 hiv.stan hiv.sf1
92 hiv_inter.stan hiv.sf2
93 radon_vary_intercept_a.stan radon_vary_intercept_a.sf1
94 radon_vary_intercept_b.stan radon_vary_intercept_b.sf1
95 radon_vary_intercept_nofloor.stan radon_vary_intercept_nofloor.sf1
96 radon_vary_intercept_floor.stan radon_vary_intercept_floor.sf1
97 radon_vary_intercept_floor2.stan radon_vary_intercept_floor2.sf1
98 anova_radon_nopred.stan anova_radon_nopred.sf1
99 electric_1a.stan electric_1a.sf1
100 electric_1b.stan electric_1b.sf1
101 electric_1c.stan electric_1c.sf1
102 electric_one_pred.stan electric_one_pred.sf1
103 electric_multi_preds.stan electric_multi_preds.sf1
104 dogs.stan dogs.sf1
105 earnings.stan earnings.sf1
106 earnings.stan earnings.sf2
107 earnings_pt1.stan earnings_pt1.sf1
108 earnings_pt2.stan earnings_pt2.sf1
109 earnings2.stan earnings2.sf1
model.type
1 One predictor
2 One predictor
3 Multiple predictors with no interaction
4 Multiple predictors with interaction
5 Multiple predictors with no interaction
6 One predictor
7 Multiple predictors with no interaction
8 Multiple predictors with interaction
9 One predictor
10 A simple regression, raw data
11 Multiple predictors with interaction, raw data
12 Centering
13 Centering based on an understandable reference point
14 Standardizing
15 Log transformations
16 Log transformations
17 Log transformations
18 Log transformations
19 Discrete predictor
20 Models for prediction
21 Models for prediction
22 Models for prediction
23 Models for prediction
24 Models for prediction
25 Models for prediction
26 One predictor
27 One predictor
28 One predictor
29 One predictor
30 Multiple predictors with no interaction
31 Multiple predictors with interction
32 Multiple predictors with interction
33 Multiple predictors with interction
34 Multiple predictors with interction
35 Multiple predictors with interction
36 Multiple predictors with interction
37 Multiple predictors with interction
38 Multiple predictors with no interaction
39 Multiple predictors with interction
40 One predictor
41 One predictor
42 Multiple predictors with no interaction
43 Log transformations
44 One predictor
45 Multiple predictors with no interaction
46 One predictor
47 Multiple predictors with no interaction
48 Log transformations
49 One predictor
50 Zero predictors
51 Poisson regression with exposure
52 Poisson overdispersion regression
53 One predictor
54 One predictor
55 Multiple predictors without interaction
56 One predictor
57 Multiple predictors without interaction
58 Multiple predictors with interaction
59 Multiple predictors with interaction
60 Multiple predictors without interaction
61 Multiple predictors without interaction
62 Multiple predictors without interaction
63 Multiple predictors with interaction
64 Multiple predictors without interaction
65 One predictor
66 One predictor
67 One predictor
68 One predictor
69 Multilevel model with varying intercept
70 One predictor
71 Multilevel model with varying intercept
72 Multilevel model with varying intercept
73 Multilevel model with varying intercept
74 Multilevel model with varying intercept
75 Multilevel model with varying slope and intercept
76 One predictor
77 One predictor
78 Multilevel model with varying slope and intercept
79 Multilevel model with varying slope and intercept
80 Multilevel model with varying slope and intercept
81 Multilevel model with several group level predictors
82 Multilevel model with several group level predictors
83 Multilevel model with varying intercept
84 Multilevel model with several group level predictors
85 Multilevel model with varying intercept
86 Multilevel model with redundant parameterization
87 Multilevel model with several group level predictors
88 Multilevel model with varying intercept
89 Multilevel model with parameter expansion
90 Multilevel model with parameter expansion
91 Multilevel model with varying intercept and slope
92 Multilevel model with group level predictors and interaction
93 Multilevel model with varying intercept
94 Multilevel model with varying intercept
95 Multilevel model with varying intercept
96 Multilevel model with varying intercept
97 Multilevel model with varying intercept
98 Multilevel model with varying intercept
99 Multilevel model with varying slope and intercept
100 Multilevel model with varying slope and intercept
101 Multilevel model with varying slope and intercept
102 Linear model with one predictor
103 Linear model with multiple predictors and without interaction
104 Multilevel model
105 Single level model with multiple predictors
106 Single level model with multiple predictors
107 Single level model with multiple predictors
108 Single level model with multiple predictors
109 Single level model with multiple predictors
model.eq
1 lm(kid_score ~ mom_hs)
2 lm(kid_score ~ mom_iq)
3 lm(kid_score ~ mom_hs + mom_iq)
4 lm(kid_score ~ mom_hs + mom_iq + mom_hs
5 lm(kid_score ~ mom_hs + mom_iq)
6 lm(kid_score ~ mom_iq)
7 lm(kid_score ~ mom_hs + mom_iq)
8 lm(kid_score ~ mom_hs + mom_iq + mom_hs
9 lm(kid_score ~ mom_iq)
10 lm(earn ~ height)
11 lm(kid_score ~ mom_hs + mom_iq + mom_hs
12 lm(kid_score ~ c_mom_hs + c_mom_iq
13 lm(kid_score ~ c2_mom_hs + c2_mom_iq
14 lm(kid_score ~ z_mom_hs + z_mom_iq
15 lm(log(earn) ~ height)
16 lm(log10(earn) ~ height)
17 lm(log(earn) ~ height + male)
18 lm(log(earn) ~ log(height) + male)
19 lm(kid_score ~ as.factor(mom_work))
20 lm(weight ~ diam1 + diam2 + canopy_height + total_height
21 lm(log(weight) ~ log(diam1) + log(diam2)
22 lm(log(weight) ~ log(canopy_volume))
23 lm(log(weight) ~ log(canopy_volume) + log(canopy_area)
24 lm(log(weight) ~ log(canopy_volume)
25 lm(log(weight) ~ log(canopy_volume) + log(canopy_area)
26 glm(vote ~ income, family=binomial(link="logit"))
27 glm(vote ~ income, family=binomial(link="logit"))
28 glm(switched ~ dist, family=binomial(link="logit"))
29 glm(switched ~ dist/100, family=binomial(link="logit"))
30 glm(switched ~ dist/100 + arsenic, family=binomial(link="logit"))
31 glm(switched ~ dist/100 + arsenic + dist/100
32 glm(switched ~ c_dist100 + c_arsenic
33 glm(switched ~ c_dist100 + c_arsenic + c_dist100
34 glm(switched ~ c_dist100 + c_arsenic + c_dist100
35 glm(switched ~ c_dist100 + c_arsenic + c_educ4
36 glm(switched ~ c_dist100 + c_arsenic + c_educ4
37 glm(switched ~ c_dist100 + c_log_arsenic + c_educ4
38 glm(switched ~ dist/100 + arsenic + educ/4,
39 glm(switched ~ dist/100 + arsenic + educ/4
40 glm(y ~ x, family=binomial(link="logit"))
41 glm(switc ~ dist100, family=binomial(link="probit"))
42 glm(earn_pos ~ height + male, family=binomial(link="logit"))
43 lm(log(earnings) ~ height + male)
44 glm(switc ~ dist100, family=binomial(link="logit"))
45 lm(vote_88 ~ vote_86 + incumbency_88)
46 glm(switc ~ dist, family=binomial(link="logit"))
47 glm(earn_pos ~ height + male, family=binomial(link="logit"))
48 lm(log(earnings) ~ height + male)
49 lm(final ~ midterm)
50 lm(y ~ 1)
51 glm (y ~ roach1 + treatment + senior, family=poisson,
52 glm(y ~ roach1 + treatment + senior,
53 lm(y ~ y_lag)
54 lm(post_test ~ treatment)
55 lm(post_test ~ treatment + pre_test)
56 lm(post_test ~ treatment)
57 lm(post_test ~ treatment + pre_test)
58 lm(post_test ~ treatment + pre_test + treatment
59 lm(post_test ~ treatment + pre_test + treatment
60 lm(post_test ~ supp + pre_test)
61 lm(score1 ~ party + x)
62 lm(score1 ~ party + x)
63 lm(score1 ~ party + x + party
64 lm(score1 ~ party + z1 + z2)
65 lm(watched ~ encouraged)
66 lm(watched ~ encouraged)
67 lm(watched ~ encouraged)
68 lm(watched ~ encouraged)
69 lmer(y ~ 1 + (1 | county))
70 lm(y ~ x)
71 lmer(y ~ x + (1 | county))
72 lmer(y ~ 1 + (1 | county))
73 lmer(y ~ x + (1 | county))
74 lmer(y ~ x + u + (1 | county))
75 lmer(y ~ 1 + (1 + x | county))
76 lm(y ~ x)
77 lm(y ~ x)
78 lmer(y ~ u + u
79 lmer(log(earn) ~ 1 + (1 + height | eth))
80 lmer(log(earn) ~ 1 + (1 + height | eth))
81 lmer(y ~ 1 + (1 | group) + (1 | scenario))
82 lmer(y ~ 1 + (1 + x | eth) + (1 + x | age)
83 glmer(y ~ black + female + (1 | state), family=binomial(link="logit"))
84 glmer(y ~ black + female + v_prev_full + (1 | age) + (1 | age_edu)
85 lmer(y ~ 1 + (1 | county))
86 lmer(y ~ 1 + (1 | county))
87 lmer(y ~1 + (1 | treatment) + (1 | airport))
88 glmer(y ~ black + female + female
89 lmer(y ~ 1 + (1 | treatment) + (1 | airport))
90 glmer(y ~ black + female + female
91 lmer(y ~ 1 + (1 + time | person))
92 lmer(y ~ time
93 lmer(y ~ x + (1 | county))
94 lmer(y ~ x + (1 | county))
95 lmer(y ~ u + (1 | county))
96 lmer(y ~ u + x + (1 | county))
97 lmer(y ~ u + x + x_mean + (1 | county))
98 lmer(y ~ 1 + (1 | county))
99 lmer(y ~ 1 + (1 | pair) + (treatment | grade))
100 lmer(y ~ treatment + pre_test + (1 | pair))
101 lmer(y ~ 1 + (1 | pair) + (treatment + pre_test | grade))
102 lm(post_test ~ treatment)
103 lm(post_test ~ treatment + pre_test)
104 glmer(y ~ n_avoid + n_shock, family=binomial(link="logit"))
105 lm(earnings ~ male + over65 + white + immig + educ_r + workmos
106 lm(earnings ~ male + over65 + white + immig + educ_r + workmos
107 glm(earnings ~ male + over65 + white + immig + educ_r + any_ssi
108 lm(earnings ~ male + over65 + white + immig + educ_r + any_ssi
109 lm(earnings ~ interest + male + over65 + white + immig + educ_r
reg.type
1 lm
2 lm
3 lm
4 lm
5 lm
6 lm
7 lm
8 lm
9 lm
10 lm
11 lm
12 lm
13 lm
14 lm
15 lm
16 lm
17 lm
18 lm
19 lm
20 lm
21 lm
22 lm
23 lm
24 lm
25 lm
26 glm
27 glm
28 glm
29 glm
30 glm
31 glm
32 glm
33 glm
34 glm
35 glm
36 glm
37 glm
38 glm
39 glm
40 glm
41 glm
42 glm
43 lm
44 glm
45 lm
46 glm
47 glm
48 lm
49 lm
50 lm
51 glm
52 glm
53 lm
54 lm
55 lm
56 lm
57 lm
58 lm
59 lm
60 lm
61 lm
62 lm
63 lm
64 lm
65 lm
66 lm
67 lm
68 lm
69 lmer
70 lm
71 lmer
72 lmer
73 lmer
74 lmer
75 lmer
76 lm
77 lm
78 lmer
79 lmer
80 lmer
81 lmer
82 lmer
83 glmer
84 glmer
85 lmer
86 lmer
87 lmer
88 glmer
89 lmer
90 glmer
91 lmer
92 lmer
93 lmer
94 lmer
95 lmer
96 lmer
97 lmer
98 lmer
99 lmer
100 lmer
101 lmer
102 lm
103 lm
104 glmer
105 lm
106 lm
107 glm
108 lm
109 lm
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Description Usage Format Examples
Description
data.frame containing the structure of the github repository https://github.com/stan-dev/example-models that contains examples to run STAN models in R from the book by Gelman and Hill 'Data Analysis Using Regression Analysis and Multilevel/Hierarchical Models'.
Usage
1 |
Format
An object of class "data.frame"
Examples
1 2 |
Example output
Warning message:
In data(stan.models) : data set 'stan.models' not found
chapter r.files
1 3 3.1_OnePredictor.R
2 3 3.1_OnePredictor.R
3 3 3.2_MultiplePredictors.R
4 3 3.3_Interactions.R
5 3 3.4_StatInference.R
6 3 3.5_GraphDisplays.R
7 3 3.5_GraphDisplays.R
8 3 3.5_GraphDisplays.R
9 3 3.6_Diagnostics.R
10 4 4.1_LinearTransformations.R
11 4 4.2_Centering&Standardizing.R
12 4 4.2_Centering&Standardizing.R
13 4 4.2_Centering&Standardizing.R
14 4 4.2_Centering&Standardizing.R
15 4 4.4_LogTransformations.R
16 4 4.4_LogTransformations.R
17 4 4.4_LogTransformations.R
18 4 4.4_LogTransformations.R
19 4 4.5_OtherTransformations.R
20 4 4.6_RegressionModelsForPrediction.R
21 4 4.6_RegressionModelsForPrediction.R
22 4 4.6_RegressionModelsForPrediction.R
23 4 4.6_RegressionModelsForPrediction.R
24 4 4.6_RegressionModelsForPrediction.R
25 4 4.6_RegressionModelsForPrediction.R
26 5 5.1_LogisticRegressionWithOnePredictor.R
27 5 5.2_InterpretingLogisticRegressionCoef.R
28 5 5.4_LogisticRegressionWellsinBangladesh.R
29 5 5.4_LogisticRegressionWellsinBangladesh.R
30 5 5.4_LogisticRegressionWellsinBangladesh.R
31 5 5.5_LogisticRegressionWithInteractions.R
32 5 5.5_LogisticRegressionWithInteractions.R
33 5 5.5_LogisticRegressionWithInteractions.R
34 5 5.5_LogisticRegressionWithInteractions.R
35 5 5.5_LogisticRegressionWithInteractions.R
36 5 5.6_EvaluatingCheckingComparing.R
37 5 5.6_EvaluatingCheckingComparing.R
38 5 5.7_AveragePredictiveComparisons.R
39 5 5.7_AveragePredictiveComparisons.R
40 5 5.8_IdentifiabilityAndSeparation.R
41 6 6.4_ProbitRegression.R
42 6 6.7_MoreComplexGLM.R
43 6 6.7_MoreComplexGLM.R
44 6 6.8_ConstructiveChoiceModels.R
45 7 7.3_SimulationForNonLinearPredictions.R
46 7 7.4_PredictiveSimulationForGLM.R
47 7 7.4_PredictiveSimulationForGLM.R
48 7 7.4_PredictiveSimulationForGLM.R
49 8 8.2_FakeDataSimulationToUnderstandResidualPlots.R
50 8 8.3_SimulatingFromTheFittedModel.R
51 8 8.3_SimulatingFromTheFittedModel.R
52 8 8.3_SimulatingFromTheFittedModel.R
53 8 8.4_PredictiveSimulationToCheckFitOfTimeSeriesModels.R
54 9 9.3_RandomizedExperiments.R
55 9 9.3_RandomizedExperiments.R
56 9 9.4_TreatmentInteractionsAndPoststratification.R
57 9 9.4_TreatmentInteractionsAndPoststratification.R
58 9 9.4_TreatmentInteractionsAndPoststratification.R
59 9 9.4_TreatmentInteractionsAndPoststratification.R
60 9 9.5_ObservationalStudies.R
61 10 10.4_LackOfOverlapWhenTreat.AssignmentIsUnknown.R
62 10 10.4_LackOfOverlapWhenTreat.AssignmentIsUnknown.R
63 10 10.4_LackOfOverlapWhenTreat.AssignmentIsUnknown.R
64 10 10.4_LackOfOverlapWhenTreat.AssignmentIsUnknown.R
65 10 10.5_CasualEffectsUsingIV.R
66 10 10.5_CasualEffectsUsingIV.R
67 10 10.6_IVinaRegressionFramework.R
68 10 10.6_IVinaRegressionFramework.R
69 12 12.2_PartialPoolingWithNoPredictors.R
70 12 12.3_PartialPoolingWithPredictors.R
71 12 12.3_PartialPoolingWithPredictors.R
72 12 12.4_FittingMLMinR.R
73 12 12.4_FittingMLMinR.R
74 12 12.8_Prediction.R
75 13 13.1_VaryingIntercepts&Slopes.R
76 13 13.1_VaryingIntercepts&Slopes.R
77 13 13.1_VaryingIntercepts&Slopes.R
78 13 13.1_VaryingIntercepts&Slopes.R
79 13 13.4_UnderstandingCorrelationsBetweenIntercepts&Slopes.R
80 13 13.4_UnderstandingCorrelationsBetweenIntercepts&Slopes.R
81 13 13.5_Non-NestedModels.R
82 13 13.5_Non-NestedModels.R
83 14 14.1_State-LevelOpinionsFromNationalPolls.R
84 14 14.1_State-LevelOpinionsFromNationalPolls.R
85 19 19.4_RedundantParameters&IntentionallyNonidentifiableModels.R
86 19 19.4_RedundantParameters&IntentionallyNonidentifiableModels.R
87 19 19.4_RedundantParameters&IntentionallyNonidentifiableModels.R
88 19 19.4_RedundantParameters&IntentionallyNonidentifiableModels.R
89 19 19.5_ParameterExpansion.R
90 19 19.5_ParameterExpansion.R
91 20 20.5_MultilevelPowerCalculationUsingFake-DataSimulation.R
92 20 20.5_MultilevelPowerCalculationUsingFake-DataSimulation.R
93 21 21.6_SummarizingtheAmmountofPartialPooling.R
94 21 21.6_SummarizingtheAmmountofPartialPooling.R
95 21 21.7_AddingAPredictorCanIncreaseResidualVariance.R
96 21 21.7_AddingAPredictorCanIncreaseResidualVariance.R
97 21 21.7_AddingAPredictorCanIncreaseResidualVariance.R
98 22 22.4_DoingANOVUsingMLM.R
99 23 23.1_MultilevelAspectsofDataCollection.R
100 23 23.1_MultilevelAspectsofDataCollection.R
101 23 23.1_MultilevelAspectsofDataCollection.R
102 23 23.1_MultilevelAspectsofDataCollection.R
103 23 23.1_MultilevelAspectsofDataCollection.R
104 24 24.2_BehavioralLearningExperiment.R
105 25 25.4_RadomImputationofaSingleVariable.R
106 25 25.4_RadomImputationofaSingleVariable.R
107 25 25.4_RadomImputationofaSingleVariable.R
108 25 25.4_RadomImputationofaSingleVariable.R
109 25 25.5_ImputationofSeveralMissingVariables.R
stan.files stan.obj.output
1 kidscore_momhs.stan kidscore_momhs
2 kidscore_momiq.stan kidscore_momiq
3 kidiq_multi_preds.stan kidiq_multi_preds
4 kidiq_interaction.stan kidiq_interaction
5 kidiq_multi_preds.stan kidiq_multi_preds
6 kidscore_momiq.stan stanfit.2
7 kidiq_multi_preds.stan stanfit.3
8 kidiq_interaction.stan stanfit.4
9 kidscore_momiq.stan kidscore_momiq.sf
10 earn_height.stan earn_height
11 kidiq_interaction.stan kidiq_interaction
12 kidiq_interaction_c.stan kidiq_interaction_c
13 kidiq_interaction_c2.stan kidiq_interaction_c2
14 kidiq_interaction_z.stan kidiq_interaction_z
15 logearn_height.stan logearn_height.sf
16 log10earn_height.stan log10earn_height.sf
17 logearn_height_male.stan logearn_height_male.sf
18 logearn_logheight.stan logearn_logheight.sf
19 kidscore_momwork.stan kidscore_momwork.sf
20 mesquite.stan mesquite.sf
21 mesquite_log.stan mesquite_log.sf
22 mesquite_volume.stan mesquite_volume.sf
23 mesquite_vas.stan mesquite_vas.sf
24 mesquite_va.stan mesquite_va.sf
25 mesquite_vash.stan mesquite_vash.sf
26 nes_logit.stan nes_logit.sf
27 nes_logit.stan sf
28 wells_dist.stan wells_dist.sf
29 wells_dist100.stan wells_dist100.sf
30 wells_d100ars.stan wells_d100ars.sf
31 wells_interaction.stan wells_interaction.sf
32 wells_interaction_c.stan wells_interaction_c.sf
33 wells_daae_c.stan wells_daae_c.sf
34 wells_dae_c.stan wells_dae_c.sf
35 wells_dae_inter_c.stan wells_dae_inter_c.sf
36 wells_predicted.stan wells_predicted.sf
37 wells_predicted_log.stan wells_predicted_log.sf
38 wells_dae.stan wells_dae.sf
39 wells_dae_inter.stan wells_dae_inter.sf
40 separation.stan separation.sf
41 wells_probit.stan wells_probit.sf1
42 earnings1.stan earnings1.sf1
43 earnings2.stan earnings2.sf1
44 wells_logit.stan wells_logit.sf1
45 congress.stan congress.sf1
46 wells.stan wells.sf1
47 earnings1.stan earnings1.sf1
48 earnings2.stan earnings2.sf1
49 grades.stan grades.sf1
50 lightspeed.stan lightspeed.sf1
51 roaches.stan roaches.sf1
52 roaches_overdispersion.stan roaches_overdispersion.sf1
53 unemployment.stan unemployment.sf1
54 electric_tr.stan sf.1
55 electric_trpre.stan sf.2
56 electric_tr.stan electric_tr.sf
57 electric_trpre.stan electric_trpre.sf
58 electric_inter.stan electric_inter.sf
59 electric_inter.stan sf
60 electric_supp.stan sf
61 ideo_two_pred.stan ideo_two_pred.sf1
62 ideo_two_pred.stan ideo_two_pred.sf2
63 ideo_interactions.stan ideo_interactions.sf1
64 ideo_reparam.stan ideo_reparam.sf1
65 sesame_one_pred_a.stan sesame_one_pred_a.sf1
66 sesame_one_pred_a.stan sesame_one_pred_b.sf1
67 sesame_one_pred_a.stan sesame_one_pred_2a.sf1
68 sesame_one_pred_a.stan sesame_one_pred_2b.sf1
69 radon_intercept.stan radon_intercept.sf1
70 radon_complete_pool.stan radon_complete_pool.sf1
71 radon_no_pool.stan radon_no_pool.sf1
72 radon_intercept.stan radon_intercept.sf1
73 radon_no_pool.stan radon_no_pool.sf1
74 radon_group.stan #radon_group.sf1
75 radon_vary_si.stan radon_vary_si.sf1
76 y_x.stan radon_no_pool.sf1
77 y_x.stan radon_complete_pool.sf1
78 radon_inter_vary.stan radon_inter_vary.sf1
79 earnings_vary_si.stan earnings_vary_si.sf1
80 earnings_vary_si.stan earnings_vary_si.sf2
81 pilots.stan pilots.sf1
82 earnings_latin_square.stan earnings_latin_square.sf1
83 election88.stan election88.sf1
84 election88_full.stan election88_full.sf1
85 radon.stan radon.sf1
86 radon_redundant.stan radon_redundant.sf1
87 pilots.stan pilots.sf1
88 election88.stan election88.sf1
89 pilots_expansion.stan pilots_expansion.sf1
90 election88_expansion.stan election88_expansion.sf1
91 hiv.stan hiv.sf1
92 hiv_inter.stan hiv.sf2
93 radon_vary_intercept_a.stan radon_vary_intercept_a.sf1
94 radon_vary_intercept_b.stan radon_vary_intercept_b.sf1
95 radon_vary_intercept_nofloor.stan radon_vary_intercept_nofloor.sf1
96 radon_vary_intercept_floor.stan radon_vary_intercept_floor.sf1
97 radon_vary_intercept_floor2.stan radon_vary_intercept_floor2.sf1
98 anova_radon_nopred.stan anova_radon_nopred.sf1
99 electric_1a.stan electric_1a.sf1
100 electric_1b.stan electric_1b.sf1
101 electric_1c.stan electric_1c.sf1
102 electric_one_pred.stan electric_one_pred.sf1
103 electric_multi_preds.stan electric_multi_preds.sf1
104 dogs.stan dogs.sf1
105 earnings.stan earnings.sf1
106 earnings.stan earnings.sf2
107 earnings_pt1.stan earnings_pt1.sf1
108 earnings_pt2.stan earnings_pt2.sf1
109 earnings2.stan earnings2.sf1
model.type
1 One predictor
2 One predictor
3 Multiple predictors with no interaction
4 Multiple predictors with interaction
5 Multiple predictors with no interaction
6 One predictor
7 Multiple predictors with no interaction
8 Multiple predictors with interaction
9 One predictor
10 A simple regression, raw data
11 Multiple predictors with interaction, raw data
12 Centering
13 Centering based on an understandable reference point
14 Standardizing
15 Log transformations
16 Log transformations
17 Log transformations
18 Log transformations
19 Discrete predictor
20 Models for prediction
21 Models for prediction
22 Models for prediction
23 Models for prediction
24 Models for prediction
25 Models for prediction
26 One predictor
27 One predictor
28 One predictor
29 One predictor
30 Multiple predictors with no interaction
31 Multiple predictors with interction
32 Multiple predictors with interction
33 Multiple predictors with interction
34 Multiple predictors with interction
35 Multiple predictors with interction
36 Multiple predictors with interction
37 Multiple predictors with interction
38 Multiple predictors with no interaction
39 Multiple predictors with interction
40 One predictor
41 One predictor
42 Multiple predictors with no interaction
43 Log transformations
44 One predictor
45 Multiple predictors with no interaction
46 One predictor
47 Multiple predictors with no interaction
48 Log transformations
49 One predictor
50 Zero predictors
51 Poisson regression with exposure
52 Poisson overdispersion regression
53 One predictor
54 One predictor
55 Multiple predictors without interaction
56 One predictor
57 Multiple predictors without interaction
58 Multiple predictors with interaction
59 Multiple predictors with interaction
60 Multiple predictors without interaction
61 Multiple predictors without interaction
62 Multiple predictors without interaction
63 Multiple predictors with interaction
64 Multiple predictors without interaction
65 One predictor
66 One predictor
67 One predictor
68 One predictor
69 Multilevel model with varying intercept
70 One predictor
71 Multilevel model with varying intercept
72 Multilevel model with varying intercept
73 Multilevel model with varying intercept
74 Multilevel model with varying intercept
75 Multilevel model with varying slope and intercept
76 One predictor
77 One predictor
78 Multilevel model with varying slope and intercept
79 Multilevel model with varying slope and intercept
80 Multilevel model with varying slope and intercept
81 Multilevel model with several group level predictors
82 Multilevel model with several group level predictors
83 Multilevel model with varying intercept
84 Multilevel model with several group level predictors
85 Multilevel model with varying intercept
86 Multilevel model with redundant parameterization
87 Multilevel model with several group level predictors
88 Multilevel model with varying intercept
89 Multilevel model with parameter expansion
90 Multilevel model with parameter expansion
91 Multilevel model with varying intercept and slope
92 Multilevel model with group level predictors and interaction
93 Multilevel model with varying intercept
94 Multilevel model with varying intercept
95 Multilevel model with varying intercept
96 Multilevel model with varying intercept
97 Multilevel model with varying intercept
98 Multilevel model with varying intercept
99 Multilevel model with varying slope and intercept
100 Multilevel model with varying slope and intercept
101 Multilevel model with varying slope and intercept
102 Linear model with one predictor
103 Linear model with multiple predictors and without interaction
104 Multilevel model
105 Single level model with multiple predictors
106 Single level model with multiple predictors
107 Single level model with multiple predictors
108 Single level model with multiple predictors
109 Single level model with multiple predictors
model.eq
1 lm(kid_score ~ mom_hs)
2 lm(kid_score ~ mom_iq)
3 lm(kid_score ~ mom_hs + mom_iq)
4 lm(kid_score ~ mom_hs + mom_iq + mom_hs
5 lm(kid_score ~ mom_hs + mom_iq)
6 lm(kid_score ~ mom_iq)
7 lm(kid_score ~ mom_hs + mom_iq)
8 lm(kid_score ~ mom_hs + mom_iq + mom_hs
9 lm(kid_score ~ mom_iq)
10 lm(earn ~ height)
11 lm(kid_score ~ mom_hs + mom_iq + mom_hs
12 lm(kid_score ~ c_mom_hs + c_mom_iq
13 lm(kid_score ~ c2_mom_hs + c2_mom_iq
14 lm(kid_score ~ z_mom_hs + z_mom_iq
15 lm(log(earn) ~ height)
16 lm(log10(earn) ~ height)
17 lm(log(earn) ~ height + male)
18 lm(log(earn) ~ log(height) + male)
19 lm(kid_score ~ as.factor(mom_work))
20 lm(weight ~ diam1 + diam2 + canopy_height + total_height
21 lm(log(weight) ~ log(diam1) + log(diam2)
22 lm(log(weight) ~ log(canopy_volume))
23 lm(log(weight) ~ log(canopy_volume) + log(canopy_area)
24 lm(log(weight) ~ log(canopy_volume)
25 lm(log(weight) ~ log(canopy_volume) + log(canopy_area)
26 glm(vote ~ income, family=binomial(link="logit"))
27 glm(vote ~ income, family=binomial(link="logit"))
28 glm(switched ~ dist, family=binomial(link="logit"))
29 glm(switched ~ dist/100, family=binomial(link="logit"))
30 glm(switched ~ dist/100 + arsenic, family=binomial(link="logit"))
31 glm(switched ~ dist/100 + arsenic + dist/100
32 glm(switched ~ c_dist100 + c_arsenic
33 glm(switched ~ c_dist100 + c_arsenic + c_dist100
34 glm(switched ~ c_dist100 + c_arsenic + c_dist100
35 glm(switched ~ c_dist100 + c_arsenic + c_educ4
36 glm(switched ~ c_dist100 + c_arsenic + c_educ4
37 glm(switched ~ c_dist100 + c_log_arsenic + c_educ4
38 glm(switched ~ dist/100 + arsenic + educ/4,
39 glm(switched ~ dist/100 + arsenic + educ/4
40 glm(y ~ x, family=binomial(link="logit"))
41 glm(switc ~ dist100, family=binomial(link="probit"))
42 glm(earn_pos ~ height + male, family=binomial(link="logit"))
43 lm(log(earnings) ~ height + male)
44 glm(switc ~ dist100, family=binomial(link="logit"))
45 lm(vote_88 ~ vote_86 + incumbency_88)
46 glm(switc ~ dist, family=binomial(link="logit"))
47 glm(earn_pos ~ height + male, family=binomial(link="logit"))
48 lm(log(earnings) ~ height + male)
49 lm(final ~ midterm)
50 lm(y ~ 1)
51 glm (y ~ roach1 + treatment + senior, family=poisson,
52 glm(y ~ roach1 + treatment + senior,
53 lm(y ~ y_lag)
54 lm(post_test ~ treatment)
55 lm(post_test ~ treatment + pre_test)
56 lm(post_test ~ treatment)
57 lm(post_test ~ treatment + pre_test)
58 lm(post_test ~ treatment + pre_test + treatment
59 lm(post_test ~ treatment + pre_test + treatment
60 lm(post_test ~ supp + pre_test)
61 lm(score1 ~ party + x)
62 lm(score1 ~ party + x)
63 lm(score1 ~ party + x + party
64 lm(score1 ~ party + z1 + z2)
65 lm(watched ~ encouraged)
66 lm(watched ~ encouraged)
67 lm(watched ~ encouraged)
68 lm(watched ~ encouraged)
69 lmer(y ~ 1 + (1 | county))
70 lm(y ~ x)
71 lmer(y ~ x + (1 | county))
72 lmer(y ~ 1 + (1 | county))
73 lmer(y ~ x + (1 | county))
74 lmer(y ~ x + u + (1 | county))
75 lmer(y ~ 1 + (1 + x | county))
76 lm(y ~ x)
77 lm(y ~ x)
78 lmer(y ~ u + u
79 lmer(log(earn) ~ 1 + (1 + height | eth))
80 lmer(log(earn) ~ 1 + (1 + height | eth))
81 lmer(y ~ 1 + (1 | group) + (1 | scenario))
82 lmer(y ~ 1 + (1 + x | eth) + (1 + x | age)
83 glmer(y ~ black + female + (1 | state), family=binomial(link="logit"))
84 glmer(y ~ black + female + v_prev_full + (1 | age) + (1 | age_edu)
85 lmer(y ~ 1 + (1 | county))
86 lmer(y ~ 1 + (1 | county))
87 lmer(y ~1 + (1 | treatment) + (1 | airport))
88 glmer(y ~ black + female + female
89 lmer(y ~ 1 + (1 | treatment) + (1 | airport))
90 glmer(y ~ black + female + female
91 lmer(y ~ 1 + (1 + time | person))
92 lmer(y ~ time
93 lmer(y ~ x + (1 | county))
94 lmer(y ~ x + (1 | county))
95 lmer(y ~ u + (1 | county))
96 lmer(y ~ u + x + (1 | county))
97 lmer(y ~ u + x + x_mean + (1 | county))
98 lmer(y ~ 1 + (1 | county))
99 lmer(y ~ 1 + (1 | pair) + (treatment | grade))
100 lmer(y ~ treatment + pre_test + (1 | pair))
101 lmer(y ~ 1 + (1 | pair) + (treatment + pre_test | grade))
102 lm(post_test ~ treatment)
103 lm(post_test ~ treatment + pre_test)
104 glmer(y ~ n_avoid + n_shock, family=binomial(link="logit"))
105 lm(earnings ~ male + over65 + white + immig + educ_r + workmos
106 lm(earnings ~ male + over65 + white + immig + educ_r + workmos
107 glm(earnings ~ male + over65 + white + immig + educ_r + any_ssi
108 lm(earnings ~ male + over65 + white + immig + educ_r + any_ssi
109 lm(earnings ~ interest + male + over65 + white + immig + educ_r
reg.type
1 lm
2 lm
3 lm
4 lm
5 lm
6 lm
7 lm
8 lm
9 lm
10 lm
11 lm
12 lm
13 lm
14 lm
15 lm
16 lm
17 lm
18 lm
19 lm
20 lm
21 lm
22 lm
23 lm
24 lm
25 lm
26 glm
27 glm
28 glm
29 glm
30 glm
31 glm
32 glm
33 glm
34 glm
35 glm
36 glm
37 glm
38 glm
39 glm
40 glm
41 glm
42 glm
43 lm
44 glm
45 lm
46 glm
47 glm
48 lm
49 lm
50 lm
51 glm
52 glm
53 lm
54 lm
55 lm
56 lm
57 lm
58 lm
59 lm
60 lm
61 lm
62 lm
63 lm
64 lm
65 lm
66 lm
67 lm
68 lm
69 lmer
70 lm
71 lmer
72 lmer
73 lmer
74 lmer
75 lmer
76 lm
77 lm
78 lmer
79 lmer
80 lmer
81 lmer
82 lmer
83 glmer
84 glmer
85 lmer
86 lmer
87 lmer
88 glmer
89 lmer
90 glmer
91 lmer
92 lmer
93 lmer
94 lmer
95 lmer
96 lmer
97 lmer
98 lmer
99 lmer
100 lmer
101 lmer
102 lm
103 lm
104 glmer
105 lm
106 lm
107 glm
108 lm
109 lm
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