Revision as of 09:13, 23 March 2021

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

How do I know?

Inspect your data with str or summary. 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

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 str or summary. integer and numeric data is not categorical, while factorial and character data is.

At least one categorical variable

How do I know?

Investigate your data using str or summary. integer and numeric data is not categorical, while factorial and character data is.

Only categorical data

If you have only categorical variables, you should do a Chi Square Test. LINK TO CHI SQUARE TEST R EXAMPLE

Categorical and continuous data

Statistics flowchart - Categorical factor levels.png

How do I know?

R commands: quantile(), str, summary
Investigate your categorical dependent variables using...
ADD MORE

One or two factor levels

How do I know?

Use an F-Test to check whether the variances of the datasets are equal. LINK LEFT BOX TO F-TEST

t-test

No matter the variances, you arrive at a t-test. A t-test ADD. Check the entry on the T-Test to learn more. ADAPT THE T-TEST ENTRY SO THAT DIFFERENCE BETWEEN STUDENT AND WELCH IS CLEAR

More than two factor levels

MISSING - COMPLICATED FIGURE

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!

Yes, my dependent variable is normally distributed!

Gaussian Anova R commands: aov, lm
Relevant figures: boxplot

No, my dependent variable is not normally distributed!

Is your dependent variable binomial or Poisson

No, my dependent variable is Poisson distributed!

Poisson GLM|Dependent variable is count data R commands: glm,
Relevant figures: plot

No, my dependent variable is binomial distributed!

Binomial GLM|Dependent variable is 0/1 or proportion R commands:
Relevant figures:

      Type III Anova

R commands: Anova(car)
Relevant figures: boxplot

Data_distribution#Non-normal_distributions|Dependent variable not normally distributed]

Poisson GLM|Dependent variable is count data R commands: glm
Relevant figures: plot

Binomial GLM|Dependent variable is 0/1 or proportions] R commands: glm
Relevant figures:

Continuous variables

How do I know?

ADD INFO - HOW DO I KNOW IF THEY ARE DEPENDENT?

No dependencies

Statistics Flowchart - Normal Distribution.png

LINK TO CORRELATION R EXAMPLES INSTEAD? How do I know?

ADD INFO - HOW DO I KNOW IF THE DATA IS NORMALLY DISTRIBUTED?
Check the entry on Normal distributions to learn more.

Correlation

You are dealing with a Correlation. A correlation ...

ADD INFO ON PEARSON AND SPEARMAN CORRELATIONS; WITH R CODE
Check the entry on Correlations to learn more.

Clear dependencies

Statistics Flowchart - Dependent - Normal Distribution.png

How do I know?

ADD INFO - HOW DO I KNOW IF THE DATA IS NORMALLY DISTRIBUTED?
Check the entry on Normal distributions to learn more.

Linear Regression

Non-linear distribution of dependent variable

Statistics Flowchart - Dependent - Distribution type.png

How do I know?

ADD INFO - HOW DO I KNOW THE DISTRIBUTION TYPE?
Check the entry on Non-normal distributions to learn more.
For both types of distribution, your next step is the Generalised Linear Model. However, it is important that you select the proper distribution type in the GLM ADD MORE INFO

Generalised Linear Models

Generalised Linear Models are... ADD

Depending on the existence of random variables, there is a distinction between Mixed Effect Models and Generalised Linear Models based on regressions.

Statistics Flowchart - GLM random variables.png

How do I know?

HOW DO I KNOW IF I HAVE RANDOM VARIABLES???
R commands: glmer, glmmPQL

Relevant figures:

WHAT IS THIS ABOUT?

Dependent variable is count data

Dependent variable is 0/1 or proportions

Multivariate statistics

Statistics Flowchart - Clustering, Networks, Ordination.png

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.

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 str or summary. 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:

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

Analysis of Variance
INSERT TYPE II
INSERT RANDOM FACTOR
INSERT LMM

Type III Anova

Dependent variable is count data

Dependent variable is 0/1 or proportions

Random factors

@@ Line 66: / Line 66: @@
 MISSING - COMPLICATED FIGURE
-===[[Experiments#Analysis_of_Variance| Analysis of Variance]]<br>===
+Analysis of Variance
 R commands: aov, Anova, ezAnova, var.test(), lm  <br>
 Relevant figures:  boxplot()  <br>
@@ Line 77: / Line 77: @@
 <div id="Yes, my dependent variable is normally distributed!">Yes, my dependent variable is normally distributed!</div><br>
-====[[Gaussian Anova| Gaussian Anova]]<br>====
+Gaussian Anova
 R commands: aov, lm  <br>
 Relevant figures: boxplot  <br>
@@ Line 87: / Line 87: @@
 <div id="No, my dependent variable is Poisson distributed!">No, my dependent variable is Poisson distributed!</div><br>
-=====[[Poisson GLM|Dependent variable is count data]]=====
+Poisson GLM|Dependent variable is count data
 R commands: glm,   <br>
 Relevant figures: plot  <br>
 <div id="No, my dependent variable is binomial distributed!">No, my dependent variable is binomial distributed!</div><br>
-=====[[Binomial GLM|Dependent variable is 0/1 or proportions]]=====
+Binomial GLM|Dependent variable is 0/1 or proportion
 R commands:   <br>
 Relevant figures:   <br>
+        Type III Anova
-====[[Type III Anova|Type III Anova]]====
 R commands: Anova(car)  <br>
 Relevant figures: boxplot  <br>
-[[Data_distribution#Non-normal_distributions|Dependent variable not normally distributed]]
+Data_distribution#Non-normal_distributions|Dependent variable not normally distributed]
-=====[[Poisson GLM|Dependent variable is count data]]=====
+Poisson GLM|Dependent variable is count data
 R commands: glm  <br>
 Relevant figures: plot  <br>
-=====[[Binomial GLM|Dependent variable is 0/1 or proportions]]=====
+Binomial GLM|Dependent variable is 0/1 or proportions]
 R commands: glm  <br>
 Relevant figures:   <br>
-''Are there random factor variables?''
-=====[[Generalised linear mixed effect models|Random factors]]=====
-R commands: glmer, glmmPQL  <br>
-Relevant figures:   <br>
 == Continuous variables ==
@@ Line 167: / Line 163: @@
 ==== Generalised Linear Models ====
+Generalised Linear Models are... ADD
+Depending on the existence of random variables, there is a distinction between Mixed Effect Models and Generalised Linear Models based on regressions.
 <imagemap>Image:Statistics Flowchart - GLM random variables.png|650px|center|
 poly 328 376 12 696 332 1008 640 688 [[Mixed Effect Models]]
@@ Line 174: / Line 174: @@
 '''How do I know?'''
 * HOW DO I KNOW IF I HAVE RANDOM VARIABLES???
+* R commands: glmer, glmmPQL  <br>
+Relevant figures:   <br>
@@ Line 195: / Line 197: @@
-==Data is classified into groups==
-R commands:   <br>
-Relevant figures:   <br>
 == Cluster analysis ==
@@ Line 208: / Line 207: @@
 '''How do I know?'''
 TAKE OUT LINK TO CLUSTERING METHODS ENTRY???
-====Supervised classification====
+==== Supervised classification ====
 R commands:   <br>
 Relevant figures:   <br>
-====Unsupervised classification====
+==== Unsupervised classification ====
 R commands:   <br>
 Relevant figures:   <br>
@@ Line 237: / Line 235: @@
 * make_bipartite_graph(types, edges, directed = FALSE)
-====Tripartite====
+==== Tripartite ====
 R commands:   <br>
 Relevant figures:   <br>
@@ Line 302: / Line 300: @@
 Resterampe
-[[At least one continuous and one categorical variable|
-More than 2 groups
 [[Experiments#Analysis_of_Variance| Analysis of Variance]]<br>
-[[Data_distribution#The_normal_distribution|Dependent variable normally distributed]]<br>
 [[Type II Anova|INSERT TYPE II]]<br>
 [[Random factors|INSERT RANDOM FACTOR]]<br>
 [[Linear mixed effect model|INSERT LMM]]<br>
-[[Data_distribution#Non-normal_distributions|Dependent variable not normally distributed]]
 [[Type III Anova|Type III Anova]]

Difference between revisions of "An initial path towards statistical analysis"

Revision as of 09:13, 23 March 2021

Contents

Univariate statistics

At least one categorical variable

Only categorical data

Categorical and continuous data

One or two factor levels

t-test

More than two factor levels

Continuous variables

No dependencies

Correlation

Clear dependencies

Linear Regression

Non-linear distribution of dependent variable

Generalised Linear Models

Dependent variable is count data

Dependent variable is 0/1 or proportions

Multivariate statistics

Cluster analysis

Supervised classification

Unsupervised classification

Network analysis

Bipartite

Tripartite

Ordinations

Linear-based ordinations

Correspondance analysis