Experiments and ANOVA

Outline

Why Do Experiments?

Causal Diagram of the World

In an experiment, we want to isolate effects between pairs of variables.

Manipulation to Determine Causal Relationship

Manipulation to Determine Causal Relationship

Experimental manipulation (done right) severs the link between a driver and its causes. We can now test the causal effect of changing one this driver on a response variable.

Other Sources of Variation are “Noise”

Properly designed experiments will have a distribution of other variables effecting our response variable. We want to reduce BIAS due to biological processes

How can experimental replicates go awry?

How would you place replicates across this “field”?

Stratified or Random Treatment Assignment

• How is your population defined?

• What is the scale of your inference?

• What might influence the inclusion of a environmental variability?

• How important are external factors you know about?

• How important are external factors you cannot assess?

Other Sources of Variation are now “Noise”

AND - this term also includes observer error. We must minimize OBSERVER BIAS as well.

Removing Bias and Confounding Effects

(Hurlbert 1984)

Ensuring that our Signal Comes from our Manipulation

CONTROL

• A treatment against which others are compared

• Separate out causal v. experimental effects

• Techniques to remove spurious effects of time, space, gradients, etc.

Ensuring our Signal is Real

REPLICATION

• How many points to fit a probability distribution?

• Ensure that your effect is not a fluke10

• \(frac{p^{3/2}}{n}\) should approach 0 for Likelihood
  • Portnoy 1988 Annals of Statistics

• i.e.,\(sim\) 5-10 samples per paramter (1 treatment = 1 parameter, but this is total # of samples)

Outline

Analysis of Models with Categorical Predictors

What are our “treatments?”

Treatments can be continuous - or grouped into discrete categories

Why categories for treatments?

• When we think of experiments, we think of manipulating categories
   
• Control, Treatment 1, Treatment 2
  
  
• Models with categorical predictors still reflect an underlying data and error generating processes
  
  
• In many ways, it’s like having many processes generating data, with each present or absent
  
  
• Big advantage: don’t make assumptions of linearity about relationships between treatments

Categorical Predictors Ubiquitous

• Treatments in an Experiment
  
  
• Spatial groups - plots, Sites, States, etc.
  
  
• Individual sampling units
  
  
• Temporal groups - years, seasons, months

Modeling categorical predictors in experiments

Categorical Predictors: Gene Expression and Mental Disorders

The data

The Steps of Statistical Modeling

1. What is your question?
2. What model of the world matches your question?
3. Build a test
4. Evaluate test assumptions
5. Evaluate test results
6. Visualize

Traditional Way to Think About Categories

What is the variance between groups v. within groups?

But What is the Underlying Model ?

But What is the Underlying Model ?

Underlying linear model with control = intercept, dummy variable for bipolar

But What is the Underlying Model ?

Underlying linear model with control = intercept, dummy variable for bipolar

But What is the Underlying Model ?

Underlying linear model with control = intercept, dummy variable for schizo

Different Ways to Write a Categorical Model

\[1) y_{ij} = \bar{y} + (\bar{y}_{i} - \bar{y}) + ({y}_{ij} - \bar{y}_{i})\]
\[2) y_{ij} = \mu + \alpha_{i} + \epsilon_{ij}, \qquad \epsilon_{ij} \sim N(0, \sigma^{2} )\]
\[3) y_{j} = \beta_{0} + \sum \beta_{i}x_{i} + \epsilon_{j}, \qquad x_{i} = 0,1\]

Partioning Model

Means Model

Linear Dummy Variable Model

\[\large y_{ij} = \beta_{0} + \sum \beta_{i}x_{i} + \epsilon_{ij}, \qquad x_{i} = 0,1\]
\[\epsilon_{ij} \sim N(0, \sigma^{2})\]  

• \(x_{i}\) inidicates presence/abscence (1/0) of a category
  
  
• Note similarities to a linear regression
  
  
• Often one category set to $ beta_{0}$ for ease of fitting, and other $ beta$s are different from it

The Steps of Statistical Modeling

1. What is your question?
2. What model of the world matches your question?
3. Build a test
4. Evaluate test assumptions
5. Evaluate test results
6. Visualize

You have Fit a Valid Model. Now…

1. Does your model explain variation in the data?

2. Are your coefficients different from 0?

3. How much variation is retained by the model?

4. How confident can you be in model predictions?

Testing the Model

Ho = The model predicts no variation in the data.



Ha = The model predicts variation in the data.

Introducing ANOVA: Comparing Variation

Hypothesis Testing with a Categorical Model: ANOVA

\[H_{0} = \mu_{1} = \mu{2} = \mu{3} = ...\]

OR

\[\beta_{0} = \mu, \qquad \beta_{i} = 0\]

Linking your Model to Your Question

Variability due to DGP versus EGP

Variability due to DGP versus EGP

Variability due to DGP versus EGP

F-Test to Compare

\(SS_{Total} = SS_{Between} + SS_{Within}\)

(Regression: \(SS_{Total} = SS_{Model} + SS_{Error}\) )

F-Test to Compare

\(SS_{Between} = \sum_{i}\sum_{j}(\bar{Y_{i}} - \bar{Y})^{2}\), df=k-1

\(SS_{Within} = \sum_{i}\sum_{j}(Y_{ij} - \bar{Y_{i}})^2\), df=n-k

To compare them, we need to correct for different DF. This is the Mean Square.

MS = SS/DF, e.g, \(MS_{W} = \frac{SS_{W}}{n-k}\)

F-Test to Compare

\(F = \frac{MS_{B}}{MS_{W}}\) with DF=k-1,n-k

(note similarities to \(SS_{R}\) and \(SS_{E}\) notation of regression)

ANOVA

Is using ANOVA valid?

Assumptions of Linear Models with Categorical Variables

• Independence of data points

• Normality within groups (of residuals)

• No relationship between fitted and residual values

• Homoscedasticity (homogeneity of variance) of groups

Fitted v. Residuals

Residuals!

Leverage

Levene’s Test of Homogeneity of Variance

Levene’s test robust to departures from normality

What do I do if I Violate Assumptions?

• Nonparametric Kruskal-Wallace (uses ranks)

• log(x+1) or otherwise transform

• GLM with ANODEV (two weeks!)

Kruskal Wallace Test