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

From Sustainability Methods
Line 52: Line 52:
 
<imagemap>Image:Statistics Flowchart - Equal variances.png|850px|center|
 
<imagemap>Image:Statistics Flowchart - Equal variances.png|850px|center|
 
poly 332 24 16 328 344 656 644 336 [[F-Test]]
 
poly 332 24 16 328 344 656 644 336 [[F-Test]]
−
poly 884 372 552 708 904 996 1196 688 [[T-Test]]
+
poly 884 372 552 708 904 996 1196 688 [[An_initial_pathway_towards_statistical_analysis#t-test|t-test]]
−
poly 1616 372 1272 716 1600 1000 1924 692 [[T-Test]]
+
poly 1616 372 1272 716 1600 1000 1924 692 [[An_initial_pathway_towards_statistical_analysis#t-test|t-test]]
 
</imagemap>
 
</imagemap>
  
 
'''How do I know?'''
 
'''How do I know?'''
 
* Use an F-Test to check whether the variances of the datasets are equal. LINK LEFT BOX TO F-TEST
 
* 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 ====
 
==== More than two factor levels ====

Revision as of 09:08, 23 March 2021

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

Multivariate Statistics Univariate StatisticsStatistics Flowchart - First Step.png
About this image

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

At least one categorical variable Only continuous variablesStatistics Flowchart - Univariate Statistics.png
About this image

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

Only categorical data Categorical and continuous dataStatistics Flowchart - Data Formats.png
About this image

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

One or two factor levels More than two factor levelsStatistics flowchart - Categorical factor levels.png
About this image

How do I know?

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


One or two factor levels

F-Test t-test t-testStatistics Flowchart - Equal variances.png
About this image

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!


Dependent variable is count data

R commands: glm,
Relevant figures: plot

No, my dependent variable is binomial distributed!


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

No dependencies Clear dependenciesStatistics Flowchart - Continuous - Dependencies.png
About this image

How do I know?

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

No dependencies

Pearson Correlation Spearman CorrelationStatistics Flowchart - Normal Distribution.png
About this image

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

Linear Regression Non-linear distribution of dependent variableStatistics Flowchart - Dependent - Normal Distribution.png
About this image

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

Generalised Linear Model Generalised Linear ModelStatistics Flowchart - Dependent - Distribution type.png
About this image

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

Mixed Effect Models Generalized Linear ModelsStatistics Flowchart - GLM random variables.png
About this image

How do I know?

  • HOW DO I KNOW IF I HAVE RANDOM VARIABLES???


WHAT IS THIS ABOUT?

Dependent variable is count data

Dependent variable is 0/1 or proportions

Multivariate statistics

Statistics Flowchart - Clustering, Networks, Ordination.png
About this image

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

Supervised Classification Unsupervised ClassificationStatistics Flowchart - Cluster Analysis.png
About this image

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

Big problems for later| Big problems for laterStatistics Flowchart - Network Analysis.png
About this image

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

Euclidean distances Jaccard distancesStatistics Flowchart - Ordination.png
About this image

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

[[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

Retrieved from "https://sustainabilitymethods.org/index.php?title=An_initial_path_towards_statistical_analysis&oldid=4580"
Powered by MediaWiki