R code supplement to the book "Mixed Methods --- quantitativ, qualitativ, explorativ und logisch in Theorie und Anwendung. Objektive Ansichten und subjektive Analysen." written by Guertler & Huber (2023)
The Spanish version of the book is titled "Combinación de Métodos - CUAL, CUAN Y LÓGICA. Puntos de vista objetivos y observaciones subjetivas". Both books can be obtained freely from OSF and AQUAD. A Spanish version of this Readme can be found in README-spa.
The book's main topic covers mixed methodology especially if it comes to data analysis. Although the book is written in German (original language) and translated to Spanish, the R code can be used without any German language skills. The notes and comments in the scripts are written in English. The code itself is not organized as an R package and that's not the intention here. However, some functions may be useful for this or that purpose. Comments here and there in the scripts should help to understand the main aim if the book is neither available or cannot be understood.
The book deals with mixed methodology (quantitative, qualitative, and Boolean logic) and is published in 2023 freely on various open library platforms like OSF or AQUAD. The statistical part covers classical statistics (Fisher, Neyman-Pearson), exploratory data analysis sensu JW Tukey, as well as Bayesian statistics. The Boolean logic is used for implicant analysis of qualitative comparative analysis. The qualitative part covers quantitative as well as qualitative textanalysis. The latter uses coding paradigm and sequential analysis originating from Objective Hermeneutics sensu Oevermann and colleagues and is the only analyses for which R code really does not make any sense. However, copmuter-assisted sequential analysis is available in the free QDA software AQUAD.
Either use a GUI like RStudio, R-Commander, JGR, Emacs with ESS, Deducer, Eclipse StatET, RKWard, Rattle or Tinn-R. Use what suits you. Normally we use RStudio (desktop, server) but never install R libraries via the GUI (see comments below).
The files work in the following manner so that most files are directly related to a chapter of the book and a specific task or data set (see table below).
ptII_quan_Bayes_BayesFactors_dependence-on-N-sim.rptII_quan_Bayes_BayesFactors_dependence-on-N-sim_helpfuncs.r
This means we take the example filenames above and break them down to their parts, e.g.:
| Filename part | Description |
|---|---|
pt II |
part II [possible values: part all, I, II, III, IV, V] |
quan |
quantitative [possible values: generalfuncs, scientifictheory, quantitative, qualitative] |
Bayes |
Bayesian statistics [possible values: classicstats, EDA, code-paradigm, quan-textanalysis, Boole, mixed] |
BayesFactors |
subtopic [ various topics possible...] |
dependence-on-N-sim |
topic discussed, here the dependence of the Bayes Factor from the sample size N by using simulation to demonstrate such a relationship |
helpfuncs [optional] |
general term used for functions used in the scripts (in most cases the non-helpfuncs.r files do not contain R functions) |
ptall_generalfuncs |
collection of functions of general usage which are not (just) data set specific |
Some rare files do not follow that filename pattern:
| Scriptname | Description |
|---|---|
DiM_Bretthorst_PG.r |
R implementation of the paper "Difference in means" from GL Bretthorst 1993 (an analytical Bayesian solution to the Behrens-Fisher problem) modelled after Gregory 2005 |
DiM_Bretthorst_UMS.r |
same as above but an implementation ater UM Studer (1998) |
model.txt |
Lady Bristol BUGS model to reproduce the exact Fisher test based on the original data from the 'Lady tea experiment' (with fixed margin totals for rows and cols) |
For the Bretthorst approach there is a dedicated repo at diffinmeans with updated and enhanced R code.
To facilitate the relationship of R script file and book chapter, the following table contains the match of chapter and R script. It is ordered in accordance to book chapters.
Click here to see the table with a the matching of R script filename, book chapter, and the (German) chapter title
| Scriptname | Chap | SubChap | Chapter title (German) |
|---|---|---|---|
| DiM_Bretthorst_PG.r | - | - | general functions |
| DiM_Bretthorst_PG_calls.r | - | - | example calls |
| DiM_Bretthorst_UMS.r | - | - | general functions |
| DiM_Bretthorst_UMS_calls.r | - | - | example calls |
| External functions | |||
| EXT_bayesian2beta.r | - | - | general functions |
| EXT_DBDA2E-utilities.R | - | - | general functions |
| EXT_Jags-Ymet-XmetMulti-Mrobust.R | - | - | general functions |
| All parts | |||
| ptall_generalfuncs_Bayes_Beta_determine.r | - | - | general functions |
| ptall_generalfuncs_Bayes_binomial-prop-test.r | - | - | general functions |
| ptall_generalfuncs_Bayes_binomial.r | - | - | general functions |
| ptall_generalfuncs_brob-integral.r | - | - | general functions |
| ptall_generalfuncs.r | - | - | general functions |
| Part I - Scientific Theory | |||
| ptI_sciencetheory_logic.r | 2 | 2.2 | Der deduktive Schluss |
| Part II - Classical Statistics | |||
| ptII_quan_classicstats_Fisher_ladyteataste.r | 4 | 4.5.1.1 | Vom Tee trinken und Milch erkennen — ein Beispielexperiment nach Fisher |
| ptII_quan_classicstats_N-P_powerfunc.r | 4 | 4.5.2.1 | Praktische Bedeutsamkeit im Kontext von statistischer Bedeutsamkeit |
| ptII_quan_classicstats_N-P_stat-signif-isNOT-practsignif.r | 4 | 4.5.2.1 | Praktische Bedeutsamkeit im Kontext von statistischer Bedeutsamkeit |
| ptII_quan_classicstats_GandC_type-S-M-error_helpfuncs.r | 4 | 4.5.2.2 | Richtung und Größe — zwei unterschätzte Fehlertypen |
| ptII_quan_classicstats_GandC_type-S-M-error.r | 4 | 4.5.2.2 | Richtung und Größe — zwei unterschätzte Fehlertypen |
| ptII_quan_classicstats_N-P_nulldist-hypotest_helpfuncs.r | 4 | 4.5.2.5 | Die Vollkostenrechnung |
| ptII_quan_classicstats_N-P_nulldist-hypotest.r | 4 | 4.5.2.5 | Die Vollkostenrechnung |
| ptII_quan_classicstats_N-P_confint_bayesboot.r | 4 | 4.5.2.6 | Konfidenzintervalle |
| ptII_quan_classicstats_N-P_confint_errorbars.r | 4 | 4.5.2.6 | Konfidenzintervalle |
| ptII_quan_classicstats_N-P_confint_helpfuncs.r | 4 | 4.5.2.6 | Konfidenzintervalle |
| ptII_quan_classicstats_N-P_confint_p-t-value_helpfuncs.r | 4 | 4.5.2.6 | Konfidenzintervalle |
| ptII_quan_classicstats_N-P_confint_p-t-value.r | 4 | 4.5.2.6 | Konfidenzintervalle |
| ptII_quan_classicstats_N-P_confint-errorbars_helpfuncs.r | 4 | 4.5.2.6 | Konfidenzintervalle |
| ptII_quan_classicstats_N-P_confint.r | 4 | 4.5.2.6 | Konfidenzintervalle |
| ptII_quan_classicstats_N-P_simulation.r | 4 | 4.5.4 | Exkurs — Simulationen |
| ptII_quan_classicstats_N-P_example-soccer-sim_helpfuncs.r | 4 | 4.5.4.1 | Fallbeispiel Simulation — Fussball Sammelbilder |
| ptII_quan_classicstats_N-P_example-soccer-sim.r | 4 | 4.5.4.1 | Fallbeispiel Simulation — Fussball Sammelbilder |
| ptII_quan_classicstats_N-P_SE-N-dep.r | 4 | 4.5.5 | Stichprobengröße |
| ptII_quan_classicstats_nullritual.r | 4 | 4.5.7 | Der Ablauf eines statistischen Tests — das Nullritual |
| ptII_quan_classicstats_NHST_nulldist.r | 4 | 4.5.8 | Die Wahrscheinlichkeit von Daten als Basis von Testentscheidungen |
| ptII_quan_classicstats_pvalue-as-base.r | 4 | 4.5.8.1 | Die Berechnung des p-Wertes |
| ptII_quan_classicstats_normal-vs-t.r | 4 | 4.5.8.2 | Zum Verhältnis von Normalverteilung und t-Verteilung |
| ptII_quan_classicstats_centrallimittheorem_helpfuncs.r | 4 | 4.5.8.3 | Exkurs --- zentraler Grenzwertsatz |
| ptII_quan_classicstats_centrallimittheorem.r | 4 | 4.5.8.3 | Exkurs --- zentraler Grenzwertsatz |
| ptII_quan_classicstats_p-t-df-relationship.r | 4 | 4.5.9 | Erkenntnistheorie reloaded — klassische Statistik |
| ptII_quan_classicstats_effectsizes_helpfuncs_sjstats.r | 4 | 4.6.10 | Effektstärken — Größe, Häufigkeit und Bezug zur Originalskala |
| ptII_quan_classicstats_effectsizes_helpfuncs.r | 4 | 4.6.10 | Effektstärken — Größe, Häufigkeit und Bezug zur Originalskala |
| ptII_quan_classicstats_effectsizes.r | 4 | 4.6.10 | Effektstärken — Größe, Häufigkeit und Bezug zur Originalskala |
| ptII_quan_classicstats_Simpsonparadox_helpfuncs.r | 4 | 4.6.11.1 | Simpson Paradox |
| ptII_quan_classicstats_Simpsonparadox.r | 4 | 4.6.11.1 | Simpson Paradox |
| ptII_quan_classicstats_JeffreysLindleyparadox.r | 4 | 4.6.11.2 | Jeffreys-Lindley Paradox |
| ptII_quan_classicstats_p-hacking-sim_helpfuncs.r | 4 | 4.6.2 | Auf der Suche nach Signifikanzen — unbewusste Forschungsintentionen und p-hacking |
| ptII_quan_classicstats_p-hacking-sim.r | 4 | 4.6.2 | Auf der Suche nach Signifikanzen — unbewusste Forschungsintentionen und p-hacking |
| ptII_quan_classicstats_dealingwithpower.r | 4 | 4.6.3 | Der Umgang mit Power |
| ptII_quan_classicstats_R-index_z-curve_helpfuncs.r | 4 | 4.6.4.1 | R-Index |
| ptII_quan_classicstats_R-index_z-curve.r | 4 | 4.6.4.1 | R-Index |
| ptII_quan_classicstats_varianceestimation_helpfuncs.r | 4 | 4.6.5 | (Selbst-)Täuschungen |
| ptII_quan_classicstats_varianceestimation.r | 4 | 4.6.5 | (Selbst-)Täuschungen |
| ptII_quan_classicstats_randomization.r | 4 | 4.6.6 | Randomisierung |
| ptII_quan_classicstats_missingdata.r | 4 | 4.6.7 | Fehlende Daten |
| ptII_quan_classicstats_equivalentmethods_helpfuncs.r | 4 | 4.6.8.1 | Äquivalenz von Messverfahren und -methoden |
| ptII_quan_classicstats_equivalentmethods.r | 4 | 4.6.8.1 | Äquivalenz von Messverfahren und -methoden |
| ptII_quan_classicstats_variancehomogeneity.r | 4 | 4.6.8.2 | Varianzhomogenität (Homoskedastizität) |
| ptII_quan_classicstats_normaldist_residuals.r | 4 | 4.6.9.1 | Normal-Verteilung der Residuen |
| ptII_quan_classicstats_outliers-and-influentialpoints_helpfuncs.r | 4 | 4.6.9.3 | Ausreisser und einflussreiche Datenpunkte |
| ptII_quan_classicstats_outliers-and-influentialpoints.r | 4 | 4.6.9.3 | Ausreisser und einflussreiche Datenpunkte |
| Part II - Exploratory Data Analysis (EDA) sensu Tukey | |||
| ptII_quan_EDA_intro_overviewrobust_helpfuncs.r | 5 | 5.2.-5.3. | Typische Verfahren der EDA in R |
| ptII_quan_EDA_intro_overviewrobust.r | 5 | 5.2.-5.3. | Typische Verfahren der EDA in R |
| ptII_quan_EDA_case_German-states-population.r | 5 | 5.5.1 | population comparison of German states |
| ptII_quan_EDA_case_Suisse-fertility_helpfuncs.r | 5 | 5.5.2 | Fruchtbarkeit und Fertilität |
| ptII_quan_EDA_case_Suisse-fertility.r | 5 | 5.5.2 | Fruchtbarkeit und Fertilität |
| ptII_quan_EDA_case_Anderson_iris-species-in-biology.r | 5 | 5.5.3 | Spezies unterscheiden in der Biologie |
| ptII_quan_EDA_case_Titanic_death-and-dying_helpfuncs.r | 5 | 5.5.4.6 | Leben und Sterben auf der Titanic |
| ptII_quan_EDA_case_Titanic_death-and-dying.r | 5 | 5.5.4.6 | Leben und Sterben auf der Titanic |
| ptII_quan_EDA_case_Spain_leadership-in-education_helpfuncs.r | 5 | 5.5.5 | 5.5.5 Führungsverhalten in Bildungskontexten |
| ptII_quan_EDA_case_Spain_leadership-in-education.r | 5 | 5.5.5 | Führungsverhalten in Bildungskontexten |
| ptII_quan_EDA_case_Chiro_heartrate-variability_helpfuncs.r | 5 | 5.5.6 | Ein Experiment zur Herzratenvariabilität |
| ptII_quan_EDA_case_Chiro_heartrate-variability.r | 5 | 5.5.6 | Ein Experiment zur Herzratenvariabilität |
| Part II - Bayesian Statistics | |||
| ptII_quan_Bayes_Beta-distribution.r | 6 | 6.12 | Die Wahl priorer Verteilungen |
| ptII_quan_Bayes_find-Beta-distribution-shapeparams.r | 6 | 6.12 | Die Wahl priorer Verteilungen |
| ptII_quan_Bayes_Fisher_LadyBristol-Beta-disttribution.r | 6 | 6.12 | Die Wahl priorer Verteilungen |
| ptII_quan_Bayes_Gamma-distribution.r | 6 | 6.12 | Die Wahl priorer Verteilungen |
| ptII_quan_Bayes_regularization.r | 6 | 6.12 | Die Wahl priorer Verteilungen |
| ptII_quan_Bayes_Prior-Likeli-Post_relationship.r | 6 | 6.12.1 | Verhältnis Prior — Likelihood — Posterior |
| ptII_quan_Bayes_MC-simulation_binom-norm.r | 6 | 6.13 | Marko Chain Monte Carlo Simulationen — MCMC |
| ptII_quan_Bayes_RandomWalk_helpfuncs.r | 6 | 6.13 | Marko Chain Monte Carlo Simulationen — MCMC |
| ptII_quan_Bayes_RandomWalk.r | 6 | 6.13 | Marko Chain Monte Carlo Simulationen — MCMC |
| ptII_quan_Bayes_simulate-pi_helpfuncs.r | 6 | 6.13 | Marko Chain Monte Carlo Simulationen — MCMC |
| ptII_quan_Bayes_simulate-pi.r | 6 | 6.13 | Marko Chain Monte Carlo Simulationen — MCMC |
| ptII_quan_simulate-pi_helpfuncs.r | 6 | 6.13 | Marko Chain Monte Carlo Simulationen — MCMC |
| ptII_quan_simulate-pi.r | 6 | 6.13 | Marko Chain Monte Carlo Simulationen — MCMC |
| ptII_quan_Bayes_problem-local-minima.r | 6 | 6.13.1.4 | Zusammenfassung MCMC-Algorithmus |
| ptII_quan_Bayes_HMC_helpfuncs.r | 6 | 6.13.2.3.1 | Hamilton Monte Carlo im R |
| ptII_quan_Bayes_HMC.r | 6 | 6.13.2.3.1 | Hamilton Monte Carlo im R |
| ptII_quan_Bayes_MetropolisHastings_example-normdist.r | 6 | 6.13.4.1 | Der Metropolis-Hastings Algorithmus im R |
| ptII_quan_Bayes_MH-Gibbs_example_helpfuncs.r | 6 | 6.13.4.1 | Der Metropolis-Hastings Algorithmus im R |
| ptII_quan_Bayes_GibbsSampling_example-normdist.r | 6 | 6.13.4.2 | Der Gibbs Sample im R |
| ptII_quan_Bayes_JAGS_example-norm.r | 6 | 6.13.4.3 | Gibbs-Sampling mit JAGS |
| ptII_quan_Bayes_Fisher_LadyBristol-BUGS_helpfuncs.r | 6 | 6.13.5 | Fisher reloaded — mehr Tee |
| ptII_quan_Bayes_Fisher_LadyBristol-BUGS.r | 6 | 6.13.5 | Fisher reloaded — mehr Tee |
| ptII_quan_Bayes_MaximumEntropy_helpfuncs.r | 6 | 6.14 | Maximum Entropy |
| ptII_quan_Bayes_MaximumEntropy.r | 6 | 6.14 | Maximum Entropy |
| ptII_quan_Bayes_Entropy_KullbackLeibler_helpfuncs.r | 6 | 6.14.1 | „I, we, and nation“ — präsidiale Eigenwerbung |
| ptII_quan_Bayes_Entropy_KullbackLeibler.r | 6 | 6.14.1 | „I, we, and nation“ — präsidiale Eigenwerbung |
| ptII_quan_Bayes_MaximumEntropy_Jaynes-fair-dice_helpfuncs.r | 6 | 6.14.3 | Der Klassiker — ist ein Würfel fair? |
| ptII_quan_Bayes_MaximumEntropy_Jaynes-fair-dice.r | 6 | 6.14.3 | Der Klassiker — ist ein Würfel fair? |
| ptII_quan_Bayes_case_presidential-heights_helpfuncs.r | 6 | 6.15.1 | Präsidiale Höhenflüge |
| ptII_quan_Bayes_case_presidential-heights.r | 6 | 6.15.1 | Präsidiale Höhenflüge |
| ptII_quan_Bayes_case_startagain-successrates.r | 6 | 6.15.2 | Durchlaufquoten in der Drogensuchttherapie |
| ptII_quan_Bayes_case_startagain-successrates-longterm.r | 6 | 6.15.2.1 | Langzeitevaluation (Durchlaufquoten) |
| ptII_quan_Bayes_case_presidential-debates.r | 6 | 6.15.3 | „I, we, and nation“ — präsidiale Eigenwerbung Teil 2 |
| ptII_quan_Bayes_intro-BayesTheorem_tea.r | 6 | 6.2.2.4 | [6.2.2 Fallbeispiel — noch ein Tee-Experiment] |
| ptII_quan_Bayes_intro-BayesTheorem_medicaldiagnosis_helpfuncs.r | 6 | 6.2.3.4 | [6.2.3 Fallbeispiel — Medizindiagnostik] |
| ptII_quan_Bayes_intro-BayesTheorem_medicaldiagnosis.r | 6 | 6.2.3.4 | [6.2.3 Fallbeispiel — Medizindiagnostik] |
| ptII_quan_Bayes_intro-BayesTheorem_covid19-test.r | 6 | 6.2.4 | Fallbeispiel — Zur Zuverlässigkeit eines COVID-19 Tests |
| ptII_quan_Bayes_posterior.r | 6 | 6.5.1 | Intuitives Verständnis von Wahrscheinlichkeit |
| ptII_quan_Bayes_simple-estimation-mean-post_helpfuncs.r | 6 | 6.5.1 | Intuitives Verständnis von Wahrscheinlichkeit |
| ptII_quan_Bayes_simple-estimation-mean-post.r | 6 | 6.5.1 | Intuitives Verständnis von Wahrscheinlichkeit |
| ptII_quan_Bayes_BayesFactors_test-hypos.r | 6 | 6.7.1 | Bayes-Faktoren und Bayes-Hypothesentesten |
| ptII_quan_Bayes_lossfun_startagain.r | 6 | 6.7.1.2 | Verlustfunktionen |
| ptII_quan_Bayes_BayesFactors_dependence-on-N-sim_helpfuncs.r | 6 | 6.7.1.3 | Aktualität von Bayes-Faktoren |
| ptII_quan_Bayes_BayesFactors_dependence-on-N-sim.r | 6 | 6.7.1.3 | Aktualität von Bayes-Faktoren |
| ptII_quan_Bayes_case_exp-extra-sensual-perception.r | 6 | 6.7.1.4 | Hellsehen — in guter Grund für Nullhypothesentesten? |
| ptII_quan_Bayes_Bem-study-aspects.r | 6 | 6.7.1.5 | Designkritik — die Studie von Bem |
| ptII_quan_Bayes_BayesFactors_further-remarks.r | 6 | 6.7.1.6 | Bayes-Faktoren — und nun? |
| ptII_quan_Bayes_information-criteria_helpfuncs.r | 6 | 6.7.2 | Informationskriterien |
| ptII_quan_Bayes_information-criteria.r | 6 | 6.7.2 | Informationskriterien |
| ptII_quan_Bayes_over-and-underfitting.r | 6 | 6.7.3 | Overfitting und Underfitting |
| ptII_quan_Bayes_HDI_helpfuncs.r | 6 | 6.7.4.1 | Intervallschätzung |
| ptII_quan_Bayes_HDI.r | 6 | 6.7.4.1 | Intervallschätzung |
| ptII_quan_Bayes_ROPE-BayesFactor_helpfuncs.r | 6 | 6.7.4.2 | ROPE — region of practical equivalenceg |
| ptII_quan_Bayes_ROPE-BayesFactor.r | 6 | 6.7.4.2 | ROPE — region of practical equivalenceg |
| ptII_quan_Bayes_PPC_model-check_helpfuncs.r | 6 | 6.7.4.4 | Praxis — posterior predictive check |
| ptII_quan_Bayes_PPC_model-check.r | 6 | 6.7.4.4 | Praxis — posterior predictive check |
| ptII_quan_Bayes_PPC_model-check-graph.r | 6 | 6.7.4.5 | Graphische Begutachtung von Modellen im Dienste des Modellfittings |
| ptII_quan_Bayes_case_wordcounts-PPC_helpfuncs.r | 6 | 6.7.4.6 | Forschungsbeispiel — Wortproduktion Humor |
| ptII_quan_Bayes_case_wordcounts-PPC.r | 6 | 6.7.4.6 | Forschungsbeispiel — Wortproduktion Humor |
| Part III - Qualitative Data Analysis | |||
| ptIII_qual_code-paradigm_table-analysis.r | 9 | 9.4 | Tabellenanalysen nach Miles und Huberman |
| ptIII_qual_quan-textanalysis.r | 10 | 10.1 | Fallbeispiel quantitative Textanalyse |
| Part IV - Qualitative Comparative Analysis | |||
| ptIV_qual_Boole_basics.r | 12 | 12.1 | Propädeutikum |
| ptIV_qual_Boole_case_Krook_women-in-parliament.r | 12 | 12.11.1 | Die Repräsentativität von Frauen in Parlamenten |
| ptIV_qual_Boole_case_Titanic_death-and-dying.r | 12 | 12.11.2 | Leben und Sterben auf der Titanic Teil II |
| ptIV_qual_Boole_logical-minimization.r | 12 | 12.3 | Typenbildung als Prinzip des Vergleichs mittels logischer Minimierung |
| ptIV_qual_Boole_case_school-success.r | 12 | 12.6 | Kriteriumsanalyse — positiver und negativer Ausgang |
| ptIV_qual_Boole_fuzzy-logic.r | 12 | 12.7 | Fuzzy Logic |
| Part V - Mixed Methods | |||
| ptV_mixed_prime-numbers.r | 13 | 13.3.1 | QUAL und QUAN in Konversions-Designs |
The following table contains condensed information about each script using some keywords. The files are clustered in accordance ot the topic and do not necessarily represent the order of appearance in the book.
Click here to see the table with a short description of the R scripts
| Scriptname | Content |
|---|---|
| DiM_Bretthorst_PG.r | Bretthorst (1993) difference in means, analytical solution, with package Brobdingnag for very large numbers, implementation after P Gregory (2005) |
| DiM_Bretthorst_PG_calls.r | example calls |
| DiM_Bretthorst_UMS.r | Bretthorst (1993) difference in means, analytical solution, with package Brobdingnag for very large numbers, implementation after UM Studer (1998) |
| DiM_Bretthorst_UMS_calls.r | example calls |
| External functions | |
| EXT_bayesian2beta.r | exact Bayesian proportion test from Sverdlov, Ryeznik & Wu (2015) |
| EXT_DBDA2E-utilities.R | from Kruschke (2014) DBDA 2nd ed., general functions |
| EXT_Jags-Ymet-XmetMulti-Mrobust.R | from Kruschke (2014) DBDA 2nd ed., used for PPC and heterogenuous variances |
| All parts | |
| ptall_generalfuncs.r | Cohen's d, descriptive statistics of all kinds in one table, Tukey's fivenum with labels, convert Aquad style tables to truth tables and v.v., print prime implicants from QCA objects, full distance matrix, optimal cut through a proximity matrix after Oldenbürger and plot it, plot prototypes in 2d + 3d, plot eigenvalues (MDS), correlation and p-values |
| ptall_generalfuncs_Bayes_Beta_determine.r | determine and plot beta distribution values from three quantile points via optimization, role model: Coghlan (2017) |
| ptall_generalfuncs_Bayes_binomial.r | functions to apply and plot prior probability functions for successes/ failures, calculate summary statistics for posterior, HDI, formula to calculate beta posterior via conjugation, plot prior, likelihood, posterior, updated prior and update therefor posterior, summary statistics |
| ptall_generalfuncs_Bayes_binomial-prop-test.r | Bayesian proportion test (e.g. successes/ failures): calculate, summarize, and plot prior from successes/ failures, convert to beta disztribution values and v.v., update binomial prior with likelihood to beta (posterior) and plot, tweaked bayes.prop.test summary from BayesianFirstAid and plot theta_diff, MCMC plot, simulation from posterior, grid approximation via brute force, exact (binomial difference) tests (Evan Miller, Chris Stucchio, the approach by Sverdlov, Ryeznik, Wu (2015) from bayesian2beta.r in a tweaked form to work with log values or brob objects and plot results, numerical integration (Simpson rule) for brob objects, brute force comparison of rbetas vs. dbetas and plot it |
| ptall_generalfuncs_brob-integral.r | useful functions for brob objects, convert a list to a vector, calculate the scalarproduct, numerical integration (Simpson rule), more numerical integration methods for brob objects can be found at https://github.com/abcnorio/R-largenum-integration |
| Part I - Scientific Theory | |
| ptI_sciencetheory_logic.r | chap. 2.2 - simple true/ false statements |
| Part II - Classical Statistics | |
| ptII_quan_classicstats_Fisher_ladyteataste.r | chap. 4.5.1.1 - Lady Bristol tea taste experiment by Fisher using his methods using hypergeometric distribution, fisher.test, and manual calculation using factorials |
| ptII_quan_classicstats_N-P_powerfunc.r | chap. 4.5.2.1 - calculate and plot power vs. effect sizes in accordance to different hypotheses |
| ptII_quan_classicstats_N-P_stat-signif-isNOT-practsignif.r | chap. 4.5.2.1 - different views on sample differences if population parameters are known |
| ptII_quan_classicstats_GandC_type-S-M-error.r | chap. 4.5.2.2 - reproduce Gelman & Carlin (2014), retrodesign power analysis |
| ptII_quan_classicstats_GandC_type-S-M-error_helpfuncs.r | chap. 4.5.2.2 - plot effect size and power for retrodesign after Gelman & Carlin (2014), plot sample distribution under H_0 and hypothesis of true effect size |
| ptII_quan_classicstats_N-P_nulldist-hypotest.r | chap. 4.5.2.5 - plot H_0, H_1 for one- and two-sided tests |
| ptII_quan_classicstats_N-P_nulldist-hypotest_helpfuncs.r | chap. 4.5.2.5 - functions to plot H_0, H_1 for hypothesis directions: two-sided, less, greater, and plot alpha and beta error rates for t-distributed densities |
| ptII_quan_classicstats_N-P_confint.r | chap. 4.5.2.6 - working and simulating confidence intervals (CI) |
| ptII_quan_classicstats_N-P_confint_bayesboot.r | chap. 4.5.2.6 - bootstrap CIs and other statistics via simulation, simulate difference in means, use Bayesian bootstrap |
| ptII_quan_classicstats_N-P_confint_errorbars.r | chap. 4.5.2.6 - CI evolution in relation to varying sample sizes |
| ptII_quan_classicstats_N-P_confint_helpfuncs.r | chap. 4.5.2.6 - calculate CI for mean, difference in means, and plot them |
| ptII_quan_classicstats_N-P_confint_p-t-value.r | chap. 4.5.2.6 - simulate t-test with p- and t-values |
| ptII_quan_classicstats_N-P_confint_p-t-value_helpfuncs.r | chap. 4.5.2.6 - simulate t-tests, compare with Cohen's d |
| ptII_quan_classicstats_N-P_confint-errorbars_helpfuncs.r | chap. 4.5.2.6 - change of CIs along with error bars, calculate covered CIs |
| ptII_quan_classicstats_N-P_simulation.r | chap. 4.5.4 - simulation 'difference in means' via replicate |
| ptII_quan_classicstats_N-P_example-soccer-sim.r | chap. 4.5.4.1 - simulate an empirical real run of how to get all cards of a soccer card album |
| ptII_quan_classicstats_N-P_example-soccer-sim_helpfuncs.r | chap. 4.5.4.1 - simulate how many cards are still to go using chance and not commone sense like card exchange, etc. |
| ptII_quan_classicstats_N-P_SE-N-dep.r | chap. 4.5.5 - dependence of sample size characteristics via simulated data (N, SE, t-value, Cohen's d, ...) |
| ptII_quan_classicstats_nullritual.r | chap. 4.5.7 - all the non-sense around the Null ritual ie. the NHST procedure |
| ptII_quan_classicstats_NHST_nulldist.r | chap. 4.5.8 - plot NHST decision making using acceptance/ rejection regions, alpha and beta error rates |
| ptII_quan_classicstats_pvalue-as-base.r | chap. 4.5.8.1 - differences of normal vs. mixed normal distribution in accordance to plot and statistics, simulation H_0 |
| ptII_quan_classicstats_normal-vs-t.r | chap. 4.5.8.2 - relationship of normal and t distribution |
| ptII_quan_classicstats_centrallimittheorem.r | chap. 4.5.8.3 - simulate central limit theorem (CLT) und varying conditions |
| ptII_quan_classicstats_centrallimittheorem_helpfuncs.r | chap. 4.5.8.3 - basic functions to simulate CLT |
| ptII_quan_classicstats_p-t-df-relationship.r | chap. 4.5.9 - relationship of N, t-, and p-value, simulate with same seed, growing sample sizes, constant t-value, and post-hoc power simulation |
| ptII_quan_classicstats_effectsizes.r | chap. 4.6.10 - relate N, p-value, Cohens' d, odds ratio, and risk ratio with each other graphically |
| ptII_quan_classicstats_effectsizes_helpfuncs.r | chap. 4.6.10 - function to relate N, p-value, and Cohen's d |
| ptII_quan_classicstats_effectsizes_helpfuncs_sjstats.r | chap. 4.6.10 - taken and twekaed from package sjtats as it disappeared from the package, convert odds ratio to risk ratio |
| ptII_quan_classicstats_Simpsonparadox.r | chap. 4.6.11.1 - investigate Simpson Paradox with classical UCB admission data set using plots, tables, corrected base rates, and glmer model, simulate Simpson Paradox data and detect sub-groups with Simpsons from package Simpsons |
| ptII_quan_classicstats_Simpsonparadox_helpfuncs.r | chap. 4.6.11.1 - function to plot two groups to detect Simpson Paradox |
| ptII_quan_classicstats_JeffreysLindleyparadox.r | chap. 4.6.11.2 - Jeffrey's-Lindley's paradox with data examples |
| ptII_quan_classicstats_p-hacking-sim.r | chap. 4.6.2 - simulate p-hacking with varying sample sizes for standard alpha error rates, comparison with a priori power analysis, and plot results |
| ptII_quan_classicstats_p-hacking-sim_helpfuncs.r | chap. 4.6.2 - function to simulate linear model with sim from package arm as well as p-hacking methods |
| ptII_quan_classicstats_dealingwithpower.r | chap. 4.6.3 - calculate, simulate, and plot power under varying models |
| ptII_quan_classicstats_R-index_z-curve.r | chap. 4.6.4.1 - apply R-Index and TIVA after Schimmack and colleagues, e.g. on Bem's clairvoyance study and calculate z-curve |
| ptII_quan_classicstats_R-index_z-curve_helpfuncs.r | chap. 4.6.4.1 - function to calculate R-Index, p-values for R-Index, Test of Insufficient Variance (TIVA) after Schimmack and colleagues |
| ptII_quan_classicstats_varianceestimation.r | chap. 4.6.5 - apply function to calculate variance estimation |
| ptII_quan_classicstats_varianceestimation_helpfuncs.r | chap. 4.6.5 - function to calculate variance estimation if sigma is known or unknown and plot it |
| ptII_quan_classicstats_randomization.r | chap. 4.6.6 - apply different randomization strategies to create samples |
| ptII_quan_classicstats_missingdata.r | chap. 4.6.7 - handling missing data and associated changes with and without imputation methods, comparison of methods of imputation with each other and with brms (Bayesian linear model) |
| ptII_quan_classicstats_equivalentmethods.r | chap. 4.6.8.1 - comparison with Bland-Altman stats and plot (mean-diff plot), TOST from package TOSTER, Pitman-Morgan Test, tests from packages BayesFactor, BEST, usage of implementation of GL Bretthorst (1993) 'On the difference in mean', using e.g. original data from Bland-Altman |
| ptII_quan_classicstats_equivalentmethods_helpfuncs.r | chap. 4.6.8.1 - mean-difference plot after Tukey (equals Bland-Altman plot) |
| ptII_quan_classicstats_variancehomogeneity.r | chap. 4.6.8.2 - determine variance homogeneity via various tests (Levene, Breusch-Pagan, non-constant error variance, Cook and Weisberg, Koenker's studentized version of test statistic) |
| ptII_quan_classicstats_normaldist_residuals.r | chap. 4.6.9.1 - investigation of residuals from linear models for various characteristics (normality, skewness, kurtosis, various sample sizes |
| ptII_quan_classicstats_outliers-and-influentialpoints.r | chap. 4.6.9.3 - impact and consequences of influential points and outliers, calculate linear model, t-test, outlier test, correlation test, leverage plots |
| ptII_quan_classicstats_outliers-and-influentialpoints_helpfuncs.r | chap. 4.6.9.3 - function to plot outlier (with, without) via regression lines |
| Part II - Exploratory Data Analysis | |
| ptII_quan_EDA_intro_overviewrobust.r | chap. 5.2.-5.3. - compare median vs. mean, show robust plots of data, apply lm vs. rlm on empirical data |
| ptII_quan_EDA_intro_overviewrobust_helpfuncs.r | chap. 5.2.-5.3. - function to plot residuals (lm vs. rlm), simulate and plot median/ mean from normal distribution |
| ptII_quan_EDA_case_German-states-population.r | chap. 5.5.1 - conversion to log values lead to straight lines using a real life example from German states population characteristics, compare to classical linear model |
| ptII_quan_EDA_case_Suisse-fertility.r | chap. 5.5.2 - analyzing fertility in Swuisse between Catholics (yes, no, something else) using only descriptive plots and correlations, use subgroups to understand data |
| ptII_quan_EDA_case_Suisse-fertility_helpfuncs.r | chap. 5.5.2 - function to plot two variables with a continuous index |
| ptII_quan_EDA_case_Anderson_iris-species-in-biology.r | chap. 5.5.3 - investigate the famous iris data with pairs, lda, and tables |
| ptII_quan_EDA_case_Titanic_death-and-dying.r | chap. 5.5.4.6 - use only descriptive statistics, tables, and plots to investigate the survivors' characteristics of the Titanic and how to survive and why, answer certain questions about the conditions of surviving or dying during the Titanic catastrophe |
| ptII_quan_EDA_case_Titanic_death-and-dying_helpfuncs.r | chap. 5.5.4.6 - function to plot densities for various subgroups |
| ptII_quan_EDA_case_Spain_leadership-in-education.r | chap. 5.5.5 - analyze data on leadership in Spain (educational context) with distance methods (HCA, MDS), prototype analysis, heatmap, corrgram, levelplot, and simple descriptive plots |
| ptII_quan_EDA_case_Spain_leadership-in-education_helpfuncs.r | chap. 5.5.5 - tweaked version of heatmap from package heatmap.plus (not available in R v.4) |
| ptII_quan_EDA_case_Chiro_heartrate-variability.r | chap. 5.5.6 - experiment in chiropractice about heart rate variability using linear models without any significance test, learning from sample size characteristics, use histograms and interaction plots |
| ptII_quan_EDA_case_Chiro_heartrate-variability_helpfuncs.r | chap. 5.5.6 - function to plot interactions |
| Part II - Bayesian Statistics | |
| ptII_quan_Bayes_Beta-distribution.r | chap. 6.12 - calculate beta posterior from prior and likelihood (conjugation, grid approximation), influence of priors, various shapes of beta |
| ptII_quan_Bayes_find-Beta-distribution-shapeparams.r | chap. 6.12 - determine beta distribution from three quantile points with/ without optimization |
| ptII_quan_Bayes_Fisher_LadyBristol-Beta-disttribution.r | chap. 6.12 - plot prior, likelihood, and posterior of the empirical data |
| ptII_quan_Bayes_Gamma-distribution.r | chap. 6.12 - display Gamma function for different parameters |
| ptII_quan_Bayes_regularization.r | chap. 6.12 - different priors (normal with different sigmas, Cauchy, uniform) |
| ptII_quan_Bayes_Prior-Likeli-Post_relationship.r | chap. 6.12.1 - display how prior and likelihood shape the posterior, see different influences on posterior |
| ptII_quan_Bayes_MC-simulation_binom-norm.r | chap. 6.13 - simulate binormial and normal distributions |
| ptII_quan_Bayes_RandomWalk.r | chap. 6.13 - calculate and plot a random walk as 2d + 3d |
| ptII_quan_Bayes_RandomWalk_helpfuncs.r | chap. 6.13 - function to calculate a simple random walk |
| ptII_quan_Bayes_simulate-pi.r | chap. 6.13 - apply pi simulation for varying sample sizes |
| ptII_quan_Bayes_simulate-pi_helpfuncs.r | chap. 6.13 - simple function to calculate pi and plot it (inefficient, but works) |
| ptII_quan_Bayes_problem-local-minima.r | chap. 6.13.1.4 - local minima using Himmelblau's function, plot in 3d |
| ptII_quan_Bayes_HMC.r | chap. 6.13.2.3.1 - apply Hamilton Monte Carlo (HMC) algorithm, plot and investigate (bivariate normal distribution), use packages hmclearn, rethinking, and bayesplot as well as the basic HMC algorithm (Neal, 2011, chap. 5), analyze MCMC chains via plots and typical diagnostics (e.g. Gelman, Heidelberger-Welch, effective sample size, remove burn-in, ... ), compare with MH algorithm |
| ptII_quan_Bayes_HMC_helpfuncs.r | chap. 6.13.2.3.1 - functions to simulate bivariate normal distribution (rnorm, mvrnorm), calculate gradient for HMC algorithm, simulate bivariate normal distribution via HMC after McElreath (2015), describe MCMC chains: Heidelberger-Welche diagnostics, describe development of mean and covariance, adjust limits to let a comparison value appear on a posterior plot, and simulate bivariate normal distribution via MH algorithm |
| ptII_quan_Bayes_MetropolisHastings_example-normdist.r | chap. 6.13.4.1 - simulate mean of normal distribution with Metropolis-Hastings (MH) algorithm, investigate MCMC chains using package coda, apply on empirical data, investigate acceptance/ rejection rates, compare MCMC chains, posterior predictive distribution and plots |
| ptII_quan_Bayes_MH-Gibbs_example_helpfuncs.r | chap. 6.13.4.1 - functions to perform MH algorithm to simulate normal distribution based on empirical data, priors, Gibbs sampler, plot MCMC and its parts, normal posterior predictive distribution |
| ptII_quan_Bayes_GibbsSampling_example-normdist.r | chap. 6.13.4.2 - simulate mean posterior via Gibbs sampling, analyze posterior |
| ptII_quan_Bayes_JAGS_example-norm.r | chap. 6.13.4.3 - simulate mean of a normal distribution using JAGS, investigate MCMC, compare with package Bolstad |
| ptII_quan_Bayes_Fisher_LadyBristol-BUGS.r | chap. 6.13.5 - analyze Lady Bristol's tea data with classical and Bayesian methods (analytical solution and MCMC with JAGS, package BEST, BUGS) |
| ptII_quan_Bayes_Fisher_LadyBristol-BUGS_helpfuncs.r | chap. 6.13.5 - function to analyze Lady Bristol experiment with Bayes Theorem, plot successes/ failures, HDI, MAP, and run BUGs model with package RBugs from R |
| ptII_quan_Bayes_MaximumEntropy.r | chap. 6.14 - calculate and plot Boltzmann/ Shannon entropy using a (not-so-fair/ fair) coin and dice |
| ptII_quan_Bayes_MaximumEntropy_helpfuncs.r | chap. 6.14 - functions to reproduce Jaynes' (1962) analyses, entropy simulation after McElreath (2015, p.277) |
| ptII_quan_Bayes_Entropy_KullbackLeibler.r | chap. 6.14.1 - apply entropy functions on word counts |
| ptII_quan_Bayes_Entropy_KullbackLeibler_helpfuncs.r | chap. 6.14.1 - functions to calculate H (Shannon entropy) from counts and priors or Kullback-Leibler distance |
| ptII_quan_Bayes_MaximumEntropy_Jaynes-fair-dice.r | chap. 6.14.3 - perform and plot Jaynes' (1962) analysis on a (fair?) dice |
| ptII_quan_Bayes_MaximumEntropy_Jaynes-fair-dice_helpfuncs.r | chap. 6.14.3 - functions to calculate entropy of a fair or not-so-fair coin and simulate a dice |
| ptII_quan_Bayes_case_presidential-heights.r | chap. 6.15.1 - prepare dataset, remove NAs, EDA plots, classical vs. Bayesian statistics with Bayesian binomial test, MCMC diagnostics, test difference in means via classical and Bayesian solution incl. HDI, ROPE, bayesboot, plot results |
| ptII_quan_Bayes_case_presidential-heights_helpfuncs.r | chap. 6.15.1 - tweaked function of bayes.binom.test from package BayesianFirstAid, function to calculate posterior OR/RR |
| ptII_quan_Bayes_case_startagain-successrates.r | chap. 6.15.2 - compare developments and extreme cases of successes/ failures and resulting posterior probs |
| ptII_quan_Bayes_case_startagain-successrates-longterm.r | chap. 6.15.2.1 - show that long-term analysis over all years vs. sequential analysis per year (prior becomes posterior becomes prior, etc.) leads exactly to the same concluding results within the Bayesian approach |
| ptII_quan_Bayes_case_presidential-debates.r | chap. 6.15.3 - frequentist, chi-square, power, JAGS, posterior odds, plots, MCMC brute force variants, test and plot hypotheses, grid approximation, exact tests, MAP, 2d + 3d plots of theta values over intgral, comparison of methods, using tweaked functions from appell, tolerance, bayesian2beta.r, comparison with package BayesFactor and Bretthorst approach |
| ptII_quan_Bayes_intro-BayesTheorem_tea.r | chap. 6.2.2.4 - simple application of Bayes Theorem, using discrete values, and shift from prior knowledge to posterior to prior, etc. ie. update Bayes Theorem with new information |
| ptII_quan_Bayes_intro-BayesTheorem_medicaldiagnosis.r | chap. 6.2.3.4 - apply Bayes Theorem on medical tests using prevalence (of a disease in population), sensitivity (1-false_positives), and specifity (1-false_negatives) |
| ptII_quan_Bayes_intro-BayesTheorem_medicaldiagnosis_helpfuncs.r | chap. 6.2.3.4 - function to apply Bayes Theorem for medical tests using prevalence p(A), sensitivity p(B |
| ptII_quan_Bayes_intro-BayesTheorem_covid19-test.r | chap. 6.2.4 - calculate probability of a covid-19 test in relation to population characteristics |
| ptII_quan_Bayes_posterior.r | chap. 6.5.1 - analyse posterior and MCMC chains (convergence), apply on empirical data set |
| ptII_quan_Bayes_simple-estimation-mean-post.r | chap. 6.5.1 - posterior distribution of a mean with different informed priors using package Bolstad |
| ptII_quan_Bayes_simple-estimation-mean-post_helpfuncs.r | chap. 6.5.1 - function to plot mean posterior with prior and likelihood, tweaked normgcp from package Bolstadt (bug fix) |
| ptII_quan_Bayes_BayesFactors_test-hypos.r | chap. 6.7.1 - empirical example from Kruschke's remarks (BFs are not enough to interpret results), better is to use priors and posterior probs, example: huge BF but roughly same parameters of the linear model, BF only caused by different priors |
| ptII_quan_Bayes_lossfun_startagain.r | chap. 6.7.1.2 - show loss function in the context of an empirical example (success rates in drug therapy) to draw conclusions for actual work |
| ptII_quan_Bayes_BayesFactors_dependence-on-N-sim.r | chap. 6.7.1.3 - compare and simulate Bayes Factors (BFs) for various sample sizes, t-values, determine minimal BF with different approaches |
| ptII_quan_Bayes_BayesFactors_dependence-on-N-sim_helpfuncs.r | chap. 6.7.1.3 - function to simulate BFs in relation to N, convert p-values to BFs, relate p-values and sample sizes |
| ptII_quan_Bayes_case_exp-extra-sensual-perception.r | chap. 6.7.1.4 - function to calculate t-test with BF, then prepare, describe, analyze empirical data using methods for binomial data and difference in means (classical and Bayesian solution), anova with repeated measurement vs. brms |
| ptII_quan_Bayes_Bem-study-aspects.r | chap. 6.7.1.5 - some artificial but meaningful calculations along with a very critical discussion of the theoretical background and design of the Bem study on clairvoyanca |
| ptII_quan_Bayes_BayesFactors_further-remarks.r | chap. 6.7.1.6 - apply BFs in the context of EDA plots, t-tests, Cohen's d, linear models, and a real full Bayesian approach with brms, and other criteria (Bayes R^2, loo, waic, kfold), different results if a categorial variable is made continuous (ie. converted back to its natural original state like categorial age) |
| ptII_quan_Bayes_information-criteria.r | chap. 6.7.2 - apply function to get information criteria from a linear model (empirical example) |
| ptII_quan_Bayes_information-criteria_helpfuncs.r | chap. 6.7.2 - function to calculate and extract information criteria from linear models |
| ptII_quan_Bayes_over-and-underfitting.r | chap. 6.7.3 - examples to show under- and overfitting incl. polynomials, use Kullback-Leibler (KL) divergence between probs |
| ptII_quan_Bayes_HDI.r | chap. 6.7.4.1 - plot HDI and CI for various data sets to demonstrate the qualitative difference of those interval types |
| ptII_quan_Bayes_HDI_helpfuncs.r | chap. 6.7.4.1 - function to plot HDI vs. symmetric CI |
| ptII_quan_Bayes_ROPE-BayesFactor.r | chap. 6.7.4.2 - show importance of ROPE vs. simple clear-cut for decision-making (difference in means). plot with HDI, Bayesian binomial test for various values, priors, and hypotheses, priors and their influence on BFs, apply on empirical data, apply and plot with package BEST, analyze MCMC and posterior probs |
| ptII_quan_Bayes_ROPE-BayesFactor_helpfuncs.r | chap. 6.7.4.2 - function to demonstrate change in BF due to prior believes |
| ptII_quan_Bayes_PPC_model-check.r | chap. 6.7.4.4 - simulate PPC via JAGS using successes/ failures and plot it |
| ptII_quan_Bayes_PPC_model-check_helpfuncs.r | chap. 6.7.4.4 - function to calulate one-/two-sided test (PPC) |
| ptII_quan_Bayes_PPC_model-check-graph.r | chap. 6.7.4.5 - demonstrate PPC for homogenous variances (yes/no), plot and test specific hypotheses (classical, Bayesian statistics with brms), treatment/ control group design, compare linear models |
| ptII_quan_Bayes_case_wordcounts-PPC.r | chap. 6.7.4.6 - perform analysis (e.g. with JAGS) and plots along with posterior predictive checks (PPC) incl. diagnostic MCMC plots, bootstrap using full cases or real values, exercise Kruschke (2014, chap 18.3) regarding heteroscedasticity (influence of un-/equal variances between groups) |
| ptII_quan_Bayes_case_wordcounts-PPC_helpfuncs.r | chap. 6.7.4.6 - functions to prepare and summarize MCMC chains and posteriors from Kruschke (2014) scripts to match requirements here for PPC |
| Part III - Qualitative Data Analysis | |
| ptIII_qual_code-paradigm_table-analysis.r | chap. 9.4 - example for usage of expand.grid |
| ptIII_qual_quan-textanalysis.r | chap. 10.1 - prepare (e.g. removal of redundant parts, special characters, white space, punctuations, stop words and convert to lower cases, split text, word cloud, plot frequencies, KWIC, collocation, ...) and analyze text using packages stringi, SnowbalC, tm, magrittr, quanteda, corps2, etc., use wordstems, corpus inspection, ... |
| Part IV - Qualitative Comparative Analysis | |
| ptIV_qual_Boole_basics.r | chap. 12.1 - simple true/ false statements |
| ptIV_qual_Boole_case_Krook_women-in-parliament.r | chap. 12.11.1 - analyse data set from Krook (2010) using QCA, extract prime implicants, check for consistencies, prepare positive and negative outcome for general discussion |
| ptIV_qual_Boole_case_Titanic_death-and-dying.r | chap. 12.11.2 - apply QCA on Titanic data set to investigate a minimal set for survival (yes/ no) |
| ptIV_qual_Boole_logical-minimization.r | chap. 12.3 - single case demonstration how Boolean minimization actually works |
| ptIV_qual_Boole_case_school-success.r | chap. 12.6 - artificial data set to demonstrate QCA for negative and positive outcome |
| ptIV_qual_Boole_fuzzy-logic.r | chap. 12.7 - simple plot to demonstrate what fuzzy logic is |
| Part V | Mixed Methods |
| ptV_mixed_prime-numbers.r | chap. 13.3.1 - one-liner to get prime numbers using Euclidean mod division |
It is impossible to write any R code without external role models and code taken, borrowed, learned, etc. from other people. To give respect to that fact and the associated authors and to allow to deepen this or that understanding of R or any other topic of the script, at some selected points the scripts contain URLs to external webpages that may be interesting for the reader or for the practice of R. We cannot guarantee that those links still exist, because the internet changes too fast. They did when the scripts were written.
Due to legal and licence issues not all data sets discussed in the book can be published here as well. Data sets should be located in the same folder as the R scripts to load them properly. Quite some data sets are taken directly from R and therefor are already included in R. The following data sets are added externally and located in the following folders below 'data' of this repository:
| Dataset | Description |
|---|---|
AAH |
data from a research study about collaborative learning (Huber 2007) |
LG |
data from a research study about the usage of word counts (Guertler 2005) and a small experiment about clairvoyance to demonstrate something that does not show any kind of empirical effect (unpublished) |
school_success |
logical table about school success (fictional data created just for demonstration) |
Spain-edu |
data from a Spanish research study on leadership in education (Huber, Guertler & Gento 2018) |
startagain_appl-letter-addiction |
application letter for a treatment place in drug therapy rehabilitation written from detox in psychiatry (originally sent via fax, names and places are fully anonymized, s.a. Studer 1998) |
startagain_successrates |
success rates to pass through a drug rehabilitation program in the Suisse drug therapy center start again between 1992-2017 |
Titanic_survivors |
well-known data of the passangers of the Titanic along with some of their characteristics |
wikipedia_presidential-heights |
data from wikipedia about the relationship of body heights of US presidential candidates and later winners (presidents) |
As mentioned above some data sets used are taken directly from R like the bupa data set or the one from Dobson (1990) about plant weight data, the famous iris data set, etc. (see book for further references). Other sets like crime data are from external sources (e.g. UCLA) and others are not published due to a missing license required for public access (e.g. data about women in parliament by Krook 2010).
Some R code was not taken from R packages but various locations on the net. From that selection some scripts were also tweaked to fit to our needs here. Such incidents are noted in the R scripts at each place where an external script was used or tweaked. Mostly, those scripts originate from:
bayesian2beta.r(Sverdlov O, Ryeznik Y & Wu S. (2015). Exact Bayesian Inference Comparing Binomial Proportions, With Application to Proof-of-Concept Clinical Trials. Therapeutic Innovation & Regulatory Science, 49(1), p.163-174.) It can downloaded for free as supplementary materialDBDA2E-utilities.RandJags-Ymet-XmetMulti-Mrobust.R(Kruschke, J (2014). Doing Bayesian Data Analysis. 2nd ed. Academic Press.) The scripts can be downloaded for free on the author's book page- package
BayesianFirstAid(Bååth)
The corresponding *.r file contains the URL from where you can download the external R script(s). Download and just place the file(s) in the main folder. Do not change the filename to avoid any error while loading it and if the download creates a different filename, use the one outlined above. External R code is used in the following way as (parts of) R functions:
- R packages and R functions that are modified to meet our needs here (e.g. some functions went missing from one R version to the next one like code from heatmap.plus, sjstats, rhmc, ...).
- Some R code (e.g. from Bolstad) was slightly changed due to bugs in the original code at that time. That may be different now ie. the bug is fixed, but it was not at the time when the R scripts were written.
- Some other R code just follows papers and can be seen as an implementation of formula and algorithms (e.g. about p-hacking, z-curves, etc.). This is referenced en detail in the book itself.
From time to time some R code is put in sections like
### not run
...
### end of not run
Such R code is optional or sometimes not fully related to the book or just gives another (maybe even redundant) perspective. This happens e.g. while investigating the Titanic survival characteristics with a lot of plots and tables. Too many perspectives, so one has to focus on a few spots.
All R scripts were written and tested under R v3.4/ v3.6. They should run with later versions of R >= v4 as well. However, sometimes packages are not maintained anymore and then they are dropped from the official R repository or something changes so heavily in a package that previous functions either do not exist anymore or at least not in the way they should and are used here. This happens when object names, internal structures, etc. become obsolete. Therefor, one can create a virtual machine or a docker engine, install R v3.4/ v3.6 and everything should run fine independent from using Linux or Windows. All scripts were developed under Linux, but also tested under win7 und should run with win10 or win11. In future we will provide a docker file + image that can be used to run the code via the free RStudio server. Those who are interested can send a note to get the most recent version of the docker file which is still under development. The book contains at the end a list of all the R libraries and their versions. However, most things should just work out of the box, the notes above refer to special cases of certain libraries required and later removed from the R repo because the package maintainers of those packages did not update to more recent R versions.
Many scripts require external R packages and external libraries installed on the operation system (Linux, ...), espicially if it comes to compilation in the context of Bayesian linear models with the Bayes package brms. Sometimes they require especially under Linux the compilation of libraries. Such compilations under Linux should be done directly by running R from the commandline and not (never!) via using some GUI like RStudio (esp. this one!), because a lot of experiences showed that the compilation tends to break and fails out of unknown reasons if such a GUI was used. Compilation directly via R on the commandline works pretty well as long as the necessary (development) libraries are installed on the system. If a library on the OS is missing, R tells you normally what is missing and gives a hint how to install it (e.g. using apt). Afterwards, the GUI can be used again without any hazzle. Under windows, most libraries do not require any compilation. Compiled packages or packages installed from the commandline and not via the OS package system (using debs/ rpms/ etc.) can be made available for all local users. To accomplish that start R as root and install all required packages manually via the commandline.
If one finds a bug please contact us with a short and clear example so we can try to reproduce it and fix the error.
Important is to understand that sometimes to demonstrate or explorate a topic the R code is not perfect and is revised a few lines later or it is done differently. Not every break of the script is a bug, sometimes it is a feature of the book. Those incidents are therefor no (serious) bugs but intentionally made for educational purposes to show that things evolve and do not happen accidentially. Statistical work means (ideally!) slow and steady progress towards a goal that sometimes even changes. In general, the R code is adjusted to the tasks (e.g. data sets) and the specific topics of the book and its chapters. It's not some kind general R package and does not try to be so. The R code does not fulfill the requirements of traditional R packages like being useful as a universal library. Nevertheless, some of the functions may be helpful in various changing contexts and can be used independently from the book, the datasets, etc.
If you find something useful, change it according to your needs. We do the same.
One data set (application letter for a treatment place in a drug addiction rehabilitation center) is taken from qualitative data analysis along with the windows binary from AQUAD 7, a free and open source QDA software written an maintained by GL Huber. The folder AQUAD7 contains all working files along with the older AQUAD7 binary version that was originally used for the analysis. AQUAD is now on v8.
The R code is licensed under GNU GPL v3. Please feel free to use, modify or share the code if it is helpful for you.
The functions taken from other packages have their own licenses. Details can be found in the license file of cited and referenced packages and scripts.
The AQUAD7 binary aquad7_170117.exe has a (C) by GL Huber (2017). The more recent version of AQUAD8 can be found freely on the AQUAD 8 webpage.
- Bretthorst, GL (1993). On the difference in means. In: WT Grandy and PW Milonni (Eds.), Physics & Probability Essays in honor of ET Jaynes. Cambridge University Press, England.
- Coghlan, A (2017-11-07). A little book of R for Bayesian Statistics. Release 0.1
- Dobson, A (1990). An Introduction to Generalized Linear Models. Chapman & Hall/ CRC Texts in Sstatistical Science.
- Gregory, P. (2005). Bayesian logical data analysis for the physical sciences. A comparative approach with Mathematica support. Cambridge University Press. Mathematica code
- Guertler, L (2005). Die Rekonstruktion von Innensicht und Aussensicht humorvollen Handelns in Schule und Erwachsenenbildung. Die Bewältigung der Katastrophe —Vipassanā-Meditation und Humor. Berlin: Logos. Book page
- Huber, AA (2007). Wechselseitiges Lehren und Lernen (WELL) als spezielle Formen Kooperativen Lernens. Berlin: Logos. Book page
- Huber, GL, Guertler, L & Gento, S (2018). La aportación de la estadística exploratoria al análisis de datos cualitativos. In: Perspectiva Educacional. Formación de Profesores, 57(1), S. 50–69.
- Krook, ML (2010). Women's Representation in Parliament: A Qualitative Comparative Analysis. Political Studies, 58, p.886-908.
- Kruschke, J (2014). Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. 2nd ed. Academic Press. Book page
- McElreath, R (2015). Statistical Rethinking. A Bayesian Course with Examples in R and Stan. 1st ed. Chapman & Hall/ CRC Texts in Sstatistical Science. Book page
- Neal, RM (2011). Handbook of Markov Chain Monte Carlo._ Edited by S Brooks, A Gelman, G Jones, and X-L Meng. Chapman & Hall/ CRC Press.
- Studer, U.M. (1998). Verlangen, Süchtigkeit und Tiefensystemik. Fallstudie des Suchttherapiezentrums für Drogensüchtige start again in Männedorf und Zürich von 1992 bis 1998. Bericht an das Bundesamt für Justiz (BAJ). Zürich.
If you ever refer to any part of the R code, please cite it as:
Guertler, L (2023). R code supplement to Guertler & Huber (2023). Mixed Methods --- quantitativ, qualitativ, explorativ und logisch in Theorie und Anwendung. Objektive Ansichten und subjektive Analysen. R code published on Github and OSDN.
Although all R scripts were tested heavily under varying conditions, we cannot rule out any possible errors. So we do not guarantee anything but to advice that users should use their common sense along with their intelligence and experience whether a result makes sense and is done properly or not. Some R code and examples make only sense in combination with the book, because the few notes in the code are not sufficient. However, it is provided "as is". Thus - use common sense to compare results with expectations. NO WARRANTY of any kind is involved here. There is no guarantee that the software is free of error or consistent with any standards or even meets your requirements. Do not use the software or rely on it to solve problems if incorrect results may lead to hurting or injurying living beings of any kind or if it can lead to loss of property or any other possible damage to the world, living beings, non-living material or society as such. If you use the software in such a manner, you are on your own and it is your own risk.