Difference between revisions of "An initial path towards statistical analysis"
| Line 2: | Line 2: | ||
<imagemap>Image:Statistics Flowchart - First Step.png|center|650px | <imagemap>Image:Statistics Flowchart - First Step.png|center|650px | ||
| − | poly 332 | + | poly 328 376 12 696 332 1008 640 688 [[An_initial_path_towards_statistical_analysis#Multivariate_statistics|Multivariate Statistics]] |
poly 1068 436 744 752 1060 1080 1380 752 [[An_initial_path_towards_statistical_analysis#Univariate_statistics|Univariate Statistics]] | poly 1068 436 744 752 1060 1080 1380 752 [[An_initial_path_towards_statistical_analysis#Univariate_statistics|Univariate Statistics]] | ||
</imagemap> | </imagemap> | ||
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= Univariate statistics = | = Univariate statistics = | ||
<imagemap>Image:Statistics Flowchart - Univariate Statistics.png|650px|center| | <imagemap>Image:Statistics Flowchart - Univariate Statistics.png|650px|center| | ||
| − | poly 328 | + | poly 328 376 12 696 332 1008 640 688 [[An_initial_path_towards_statistical_analysis#Categorical_variables|Not only continuous variables]] |
poly 1056 368 732 696 1064 1000 1372 696 [[An_initial_path_towards_statistical_analysis#Continuous_variables|Only continuous variables]] | poly 1056 368 732 696 1064 1000 1372 696 [[An_initial_path_towards_statistical_analysis#Continuous_variables|Only continuous variables]] | ||
</imagemap> | </imagemap> | ||
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==Categorical and continuous data== | ==Categorical and continuous data== | ||
<imagemap>Image:Statistics flowchart - Categorical factor levels.png|650px|center| | <imagemap>Image:Statistics flowchart - Categorical factor levels.png|650px|center| | ||
| − | poly | + | poly 328 376 12 696 332 1008 640 688 [[An_initial_path_towards_statistical_analysis#One_or_two_factor_levels|One or two factor levels]] |
poly 1064 372 736 696 1076 996 1376 696 [[An_initial_path_towards_statistical_analysis#More_than_two_factor_levels|More than two factor levels]] | poly 1064 372 736 696 1076 996 1376 696 [[An_initial_path_towards_statistical_analysis#More_than_two_factor_levels|More than two factor levels]] | ||
</imagemap> | </imagemap> | ||
Revision as of 08:13, 23 March 2021
Start here with your data! This is your first question.
How do I know?
- Inspect your data with
strorsummary. Are there several variables? - What does the data show? Does the underlying logic of the data suggest dependencies between the variables?
Example: Inspecting the swiss dataset
Contents
Univariate statistics
How do I know?
- Check the entry on Data formats to understand the difference between categorical and numeric variables.
- Investigate your data using
strorsummary. integer and numeric data is not categorical, while factorial and character data is.
Categorical variables
How do I know?
- Investigate your data using
strorsummary. integer and numeric data is not categorical, while factorial and character data is.
LINK LEFT TO CHI SQUARE TEST R EXAMPLE
Categorical and continuous data
How do I know?
- R commands: quantile(), str, summary
- Investigate your categorical dependent variables using...
- ADD MORE
One or two factor levels
More than two factor levels
t-test
R commands: t.test, t_test Relevant figures:
Analysis of Variance
R commands: aov, Anova, ezAnova, var.test(), lm
Relevant figures: boxplot()
Is your dependent variable normally distributed?
R commands: ks.test, shapiro.test, hist
Yes, my dependent variable is normally distributed!
No, my dependent variable is binomial distributed!
No, my dependent variable is Poisson distributed!
Gaussian Anova
R commands: aov, lm
Relevant figures: boxplot
Is your dependent variable binomial or Poisson
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:
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 count data
Dependent variable is 0/1 or proportions
R commands:
Relevant figures:
Multivariate statistics
How do I know?
- In an Ordination, you arrange your data alongside underlying gradients in the variables to see which variables most strongly define the data points. Check the entry on Ordinations (to be added) to learn more.
- In a Cluster Analysis, you group your data points according to how similar they are, resulting in a tree structure. Check the entry on Clustering Methods to learn more.
- In a Network Analysis, you arrange your data in a network structure to understand their connections and the distance between individual data points. Check the entry on Social Network Analysis to learn more.
Data is classified into groups
R commands:
Relevant figures:
Cluster analysis
DIFFERENCE BETWEEN SUPERVISED AND UNSUPERVISED?? DISTINCTION NOT MADE IN THE CLUSTERING ENTRY
How do I know?
TAKE OUT LINK TO CLUSTERING METHODS ENTRY???
Supervised classification
R commands:
Relevant figures:
Unsupervised classification
R commands:
Relevant figures:
Network analysis
How do I know?
- Check your data using the R code ADD CODE
ADD MORE BELOW
Bipartite
If your data has two different kinds of nodes, your network is called a "bipartite" network.
R commands:
- is_bipartite(graph)
- make_bipartite_graph(types, edges, directed = FALSE)
Tripartite
R commands:
Relevant figures:
Ordinations
How do I know?
- Check the entry on Data formats to learn more about the different data formats.
- Investigate your data using
strorsummary. Abundance data is marked as FORMATNAME, and continuous data is marked as FORMATNAME.
MAKE THE STUFF BELOW CLEARER
Linear-based ordinations
Linear-based ordinations are... It uses Euclidean distances, which is...
R commands:
Relevant figures:
Correspondance analysis
A correspondence analysis is... It uses Jaccard distances, which is...
R commands:
Relevant figures:
Is your dependent variable normally distributed?
Is your dependent variable not normally distributed?
Does your independent variable contain only 1 or 2 groups?
Does your independent variable contain more than 2 groups?
Is your dependent variable normally distributed?
Is your dependent variable not normally distributed?
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
Dependent variable is count data
Dependent variable is 0/1 or proportions
