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

From Sustainability Methods
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'''Start here with your data! This is your first question.'''
 
'''Start here with your data! This is your first question.'''
  
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= Univariate statistics =
 
= Univariate statistics =
 
+
<imagemap>Statistics Flowchart - Univariate Statistics.png|650px|center|
 +
poly 328 368 8 684 328 1000 636 688 [[An_initial_path_towards_statistical_analysis#Continuous_variables|Only continuous variables]]
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poly 1056 368 732 696 1064 1000 1372 696 [[An_initial_path_towards_statistical_analysis#Categorical_variables|Not only continuous variables]]
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</imagemap>
  
  
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<div id="Yes, my data contains at least one categorical variable?">'''Yes, my data contains at least one categorical variable?'''</div><br>
 
<div id="Yes, my data contains at least one categorical variable?">'''Yes, my data contains at least one categorical variable?'''</div><br>
  
==Categorical variables==
+
== Categorical variables ==
 +
 
 
'''Does you data contain only [[Data_formats#Categorical_data|categorical]] variables?'''<br>
 
'''Does you data contain only [[Data_formats#Categorical_data|categorical]] variables?'''<br>
 
[[#Yes, my data contain only categorical variables?|Yes, my data contain only categorical variables?]]<br>
 
[[#Yes, my data contain only categorical variables?|Yes, my data contain only categorical variables?]]<br>
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<div id="No, my data does contains continuous and categorical variables?">'''No, my data does contains continuous and categorical variables?'''</div><br>
 
<div id="No, my data does contains continuous and categorical variables?">'''No, my data does contains continuous and categorical variables?'''</div><br>
 +
 
==Categorical and continuous data==
 
==Categorical and continuous data==
 
  R commands: quantile(), str, summary,  <br>
 
  R commands: quantile(), str, summary,  <br>
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 +
== Continuous variables ==
  
<div id="No, my data contains only continuous variables?">'''No, my data contains only continuous variables?'''</div><br>
 
  
==Continuous variables==
 
  
 
Non dependent relations?
 
Non dependent relations?
 +
 
===[[Correlations|Correlations]]===
 
===[[Correlations|Correlations]]===
  
 
Clear dependent relations
 
Clear dependent relations
 +
 
===[[Causality|Regression models]]<br>===
 
===[[Causality|Regression models]]<br>===
 
[[Data_distribution#The_normal_distribution|Dependent variable normally distributed]]<br>
 
[[Data_distribution#The_normal_distribution|Dependent variable normally distributed]]<br>

Revision as of 09:15, 10 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?


Data inspection example

Do you have several continuous variables without clear dependencies? (?)
Yes, I have several continuous variables without clear dependencies!
No, my variables have clear dependencies!
R commands: str, summary, head, tail
Example: Inspecting the swiss dataset



Univariate statistics

Error: Must specify an image in the first line.


My variables have clear dependencies!


Does you data contain at least one categorical variable?
Yes, my data contains at least one categorical variable?
No, my data contains only continuous variables?

No, my variables have clear dependencies!


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

Relevant figures: hist(), boxplot()

 
Yes, my data contains at least one categorical variable?


Categorical variables

Does you data contain only categorical variables?
Yes, my data contain only categorical variables?
No, my data does contains continuous and categorical variables?

Yes, my data contain only categorical variables?


Chi-Square test

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

No, my data does contains continuous and categorical variables?


Categorical and continuous data

R commands: quantile(), str, summary,  

Does you categorical dependent variables have 1-2 factor levels?
Yes, my categorical dependent variables has 1-2 factor levels!
No, my categorical dependent variables more than 2 factor levels!

Yes, my categorical dependent variables has 1-2 factor levels!


t-test

R commands: t.test, t_test Relevant figures:

No, my categorical dependent variables more than 2 factor levels!


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

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:

Yes, I have several continuous variables without clear dependencies!


Multivariate statistics

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


R commands:
Relevant figures:

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

R commands:
Relevant figures:


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

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

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

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