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Zusammenfassung/Summary Andy Fields Discovering Statistics Using R $11.29
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  3. Georg-August-Universität Göttingen (Uni Goetti)
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  5. Psychologie
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  7. Multivariate Statistik (M.PSY.205)

Summary

Zusammenfassung/Summary Andy Fields Discovering Statistics Using R

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  • Course
  • Multivariate Statistik (M.PSY.205)
  • Institution
  • Georg-August-Universität Göttingen (Uni Goetti)
  • Book
  • Discovering Statistics Using R

Includes summaries of chapters: Chapter 4: Exploring data with graphs Chapter 6: Correlations Chapter 7: Simple/Multiple Regressions Chapter 8: Logistic regression Chapter 9: Comparing two means Chapter 10: Comparing several means ANOVA Chapter 11: Analysis of Covariance ANCOVA Chapter 12: ...

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  • Kapitelangaben in beschreibung enthalten
  • November 3, 2020
  • 77
  • 2019/2020
  • Summary

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  • statistics r fields summary

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  • Georg-August-Universität Göttingen (Uni Goetti)
  • Psychologie
  • Multivariate Statistik (M.PSY.205)
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SUMMARY – ANDY FIELDS R – MULTIVARIATE STATISTIK


Kapitel 4: Exploring data with graphs S.117-165
1. grundlegendes Name_plot <- ggplot(Datensatz, aes(x= Variable 1, y = Variable2, fill = x-Variable / group =
Objekt erstellen Gruppenvariable / color = Variable))
X-/Y-Achse begrenzen plot(ddf$height, ddf$weight, ylim=c(-5,130), xlim=c(-5,200))
Nullpunkt abline(x=0)
abline(y=0)
Regressionsgerade abline(model)
zum Diagramm
hinzufügen
2. Graphische Ebene Name_plot + geom_x()
hinzufügen  x kann für folgende Graphentypen stehen
o geom_point = fügt Ebene aus Punkten hinzu z.B. für Scatterplots
o geom_line = fügt Ebene mit einer Linie hinzu
o geom_smooth(method = lm, se = FALSE/TRUE)= fügt Regressionsgerade hinzu
 Method kann auch andere als lm sein (z.B. loess, dann ist es keine Gerade
 se = FALSE blenden Standardfehler aus,True zeigt ihn an (als Schatten um die Linie)
o geom_bar = Bardiagramm/Balkendiagramm
o geom_histogram= Histogramm
Scatterplot scatterplot <- ggplot(Datensatz, aes(x = , y = ))
scatterplot + geom_point() + geom_smooth(method = "lm", se = FALSE)




Plot nach Gruppen Scatterplot_neu <- scatterplot + facet_wrap(~ group)
getrennt




bar_plot <- ggplot(Datensatz, aes(x = fct_reorder(hero, injury), y = injury, fill = hero)

1. barplot + stat_summary(fun.y = mean, geom = "bar", color = "black") +
labs(x = "Antrieb",
y = "Miles per Gallon (Stadt)",

1

, SUMMARY – ANDY FIELDS R – MULTIVARIATE STATISTIK


Barplot title = "Verbrauch nach Antrieb",
fill = "Antrieb")

 fill fügt Farbe hinzu
 stat_summary(fun.y = mean) stellt die Mittelwerte der Daten auf der y-Achse dar
 geom = "bar" erzeugt Barplot
 colour = "black" verleiht Säulen einen schwarzen Rand
 labs(x="Test“, y = "Text“, title = "Text“, fill = "Text")beschriftet x- und y-Achse und gibt dem Plot einen
Titel + Beschriftung für die Farben

2. bar_plot + stat_summary(fun.y=mean, geom="bar", position=position_dodge())
+stat_summary(fun.data = mean_cl_normal, geom = "errorbar",
position=position_dodge(width=0.9),width=0.2)
+ labs(x = "X", y = "Y", title = "XX", fill = "XX")

 fct_reorder(x-Variable, y-Variable) sortiert die Faktorstufen der Größe nach (i.d.R. aufsteigend)
 position = position_dodge() bestimmt Abstand der Balken
 stat_summary(fun.data = mean_cl_normal, geom = "errorbar",
 position=position_dodge(width=0.9),width=0.2) macht Fehlerbalken und legt ihre Breite fest
Linienplot linien_plot <- ggplot(Datensatz, aes(x = class, y = cty))
linien_plot + stat_summary(fun.y = mean, geom = "point") +
stat_summary(fun.y = mean, geom = "line", aes(group = 1)) +
stat_summary(fun.data = mean_cl_normal, geom = "errorbar", width = 0.2) + labs(x = "X", y = "Y",
title = "XX")

 stat_summary(fun.y = mean, geom = "point"): fügt eine Ebene mit Punkten hinzu, die Punkte stellen die Mittelwerte
dar
 stat_summary(fun.y = mean, geom = "line", aes(group = 1)): fügt eine Ebene mit einer Linie hinzu, die die
Punkte verbindet, Argument aes(group = 1) teilt ggplot mit, dass alle Punkte in einer Gruppe gruppiert werden (also dass es
Mittelwerte sind)
 stat_summary(fun.data = mean_cl_normal, geom = "errorbar", width = 0.2): fügt eine Ebene mit
Fehlerbalken hinzu, die die 95% KIs der Mittelwerte angeben, Argument width stellt Breite der KIs ein




2

, SUMMARY – ANDY FIELDS R – MULTIVARIATE STATISTIK


Linienplot mit Linien_plot <- ggplot(data = Datensatz, aes(x = time, y = grammar, group = Gruppenvariable, color
Gruppierungsvariable = Gruppenvariable))
Linienplot + stat_summary(fun.y = mean, geom = "line") + stat_summary(fun.data = mean_cl_normal,
geom = "errorbar", width = .2)
 durch group = Gruppenvariable wird die Gruppenvariable (Group: Controls & Text Messagers) in den Plot eingefüht
 durch color = Gruppenvariable werden die Bedingungen der Gruppenvariable im Linienplot eingefügt




Liniendiagramm Linienplot <- ggplot(data = gdp, aes(year, gdp, group = country_fac, color = country_fac))
Linien, die keine Linienplot + geom_line() + geom_point() + labs(x = "Year", y = "GDP per capita", color =
"Country")
Mittelwerte
 hier kein stat.summary(fun.y = mean…)vor geom_line & geom_point, weil keine Mittelwerte, sondern nur die einzelnen
verbinden Messwerte pro Messzeitpunkt abgetragen und durch Linien verbunden werden




Histogramm  Häufigkeiten einer Variable; diese wird auf der x-Achse dargestellt - y-Achse: Häufigkeit
histogramm <- ggplot(Datensatz, aes(cty))
histogramm + geom_histogram() + theme_classic()


Pakete  ggplot2




Kapitel 6: Korrelationen S.205 - 243

3

, SUMMARY – ANDY FIELDS R – MULTIVARIATE STATISTIK


Wofür/wann  Wir können die Beziehung zwischen zwei Variablen mit Hilfe von Korrelationskoeffizienten messen.
benutzen?  Diese Koeffizienten liegen zwischen -1 und +1 (stärke des Zusammenhangs)
Welche gibt es?




name type assumptions

Pearson parametric  data are interval
 Sampling distribution has to be normally distributed
 Both variables have to be normally distributed
(one of the variables can be a categorical variable
provided there are only two categories)
 Needs to be standardized (Fishers z) when sampling
distribution not normal
Spearman non-parametric  can be used when the data have violated parametric
assumptions such as non-normally distributed data
 requires only ordinal data for both variables.
Kendall’s Tau non-parametric  should be used rather than Spearman’s coefficient when
you have a small data set with a large number of tied
ranks
Bootstrapping non-parametric

point-biserial correlation coefficient  quantifies the relationship between a continuous
variable and a variable that is a continuous dichotomy
(e.g., there is no continuum underlying the two
categories, such as passing or failing an exam)
biserial correlation coefficient  quantifies the relationship between a continuous
variable and a variable that is a continuous dichotomy
(e.g., there is a continuum underlying the two
categories, such as passing or failing an exam)
Allgemein  Correlation coefficients are effect sizes!
o caveat when using non-parametric correlation coefficients as effect sizes


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