An initial path towards statistical analysis

Start here with your data! This is your first question.

Do you have several continuous variables without clear dependencies? (?)
R commands: str, summary, head, tail
Example: Inspecting the swiss dataset

Data inspection example

Univariate statistics

'Yes', there are clear dependencies

Does you data contain at least one categorical variable?
Yes, I have at least one categorical variable! (?) (?)

R commands: str, summary, head, tail, ordered(dataset$variablename, c(levels = “level1”, level2”...)) 

Relevant figures: hist(), boxplot()

Categorical variables

Chi-Square test

R commands: table, chisq.test( x, correct=TRUE)
Relevant figures: (stacked) bar charts : barplot(), pie()

Categorical and continuous data

R commands: quantile(), str, summary,
Relevant figures:

Does your data consist only of categorical variables? R commands: str, summary, table
Relevant figures: bipartite

Does you categorical dependent variables have 1-2 factor levels?

t-test

R commands: t.test, t_test Relevant figures:

Does you categorical dependent variables have more than 2 factor levels?

Analysis of Variance

R commands: aov, Anova, ezAnova, var.test(), lm
Relevant figures: boxplot()

The dependent variable is normally distributed

R commands: ks.test Dependent variable normally distributed

Type II Anova

R commands: aov, lm
Relevant figures: boxplot

Dependent variable not normally distributed

Dependent variable is count data

R commands: glm,
Relevant figures: plot

Dependent variable is 0/1 or proportions

R commands:
Relevant figures:

Type III Anova

R commands: Anova(car)
Relevant figures: boxplot

Dependent variable not normally distributed

Dependent variable is count data

R commands: glm
Relevant figures: plot

Dependent variable is 0/1 or proportions

R commands: glm
Relevant figures:

Are there random factor variables?

Random factors

R commands: glmer, glmmPQL
Relevant figures:

No, I have only continuous variables! (?) (?)

Continuous variables

Non dependent relations?

Correlations

Clear dependent relations

Regression models

Dependent variable normally distributed

Linear Regression

Dependent variable not normally distributed

Generalised linear model

Dependent variable is 0/1 or proportions

R commands:
Relevant figures:

Multivariate statistics

R commands:
Relevant figures:

Data is classified into groups

R commands:
Relevant figures:

CLuster analysis

R commands:
Relevant figures:

Supervised classification

R commands:
Relevant figures:

Unsupervised classification

R commands:
Relevant figures:

Network analysis

R commands:
Relevant figures:

Bipartite

R commands: is_bipartite(graph)

Relevant figures: make_bipartite_graph(types, edges, directed = FALSE)

Tripartite

R commands:
Relevant figures:

Ordinations

R commands:
Relevant figures:

Linear based ordinations

R commands:
Relevant figures:

Correspondance analysis

R commands:
Relevant figures:

More than 2 categorical variables

Is your dependent variable normally distributed?
Is your dependent variable not normally distributed?

My data consists only of categorical variables

Does your independent variable contain only 1 or 2 groups?
Does your independent variable contain more than 2 groups?

Does your independent variable contain more than 2 groups?

Is your dependent variable normally distributed?
Is your dependent variable not normally distributed?

Does your independent variable contain more only 1 or 2 groups?

My data consists only of categorical variables

Multivariate statistics

Resterampe

[[At least one continuous and one categorical variable| More than 2 groups Analysis of Variance
Dependent variable normally distributed
INSERT TYPE II
INSERT RANDOM FACTOR
INSERT LMM

Dependent variable not normally distributed

Type III Anova

Dependent variable is count data

Dependent variable is 0/1 or proportions

Random factors

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