Lecture 15 - ANCOVA

Author

Bill Perry

Lecture 14: Review

Review

General Linearized Models (GLM)

Gaussian GLMs (normal distribution)
Poisson GLMs (count data)
Logistic GLMs (binary outcomes)
Model assumptions and diagnostics
Connection to ANOVA and linear models

Lecture 15: ANCOVA Overview

Overview

Analysis of covariance (ANCOVA):

Introduction to ANCOVA
When to use ANCOVA
Linear model for ANCOVA
Analysis of variance in ANCOVA
Assumptions of ANCOVA
- Homogeneous slopes
Robust ANCOVA approaches
Specific comparisons of means
Examples and interpretation
Scientific reporting of ANCOVA results

Introduction to ANCOVA

What is ANCOVA?

ANCOVA = Analysis of COVAriance
Combination of regression and ANOVA
A continuous covariate is measured along with the response variable for each experimental unit
Common use: compare means of factor levels (groups), adjusting for variance from continuous covariate
Another use: determine whether two or more regression lines differ in slopes and intercepts

When to Use ANCOVA

Common Applications of ANCOVA

Increasing statistical power
- Removing variation associated with a covariate can reduce residual error
- More powerful test of treatment effects
Adjusting for confounding variables
- When treatments might differ in some continuous variable
- Need to separate treatment effects from covariate effects
Testing equality of regression lines
- Do treatments have the same relationship with a continuous variable?
- Tests for both slopes and intercepts

ANCOVA Example: Cricket Chirping

Cricket Chirping Example

Want to compare chirping rate of two cricket species:

Oecanthus exclamationis
Oecanthus niveus

But:

Measured rates at different temperatures
Range of temperatures differed between species
Apparent relationship between pulse rate and temperature

ANCOVA lets us adjust for temperature effect to get a more powerful test!

ANCOVA Linear Model: Conceptual Framework

The ANCOVA Model

Key concept in ANCOVA: the difference between “unadjusted” group means and “adjusted” means.

In this visualization:

Group Means (shown as asterisks): raw/unadjusted means for each group - simply the average X value and average Y value for all points in that group. Notice that Group A and Group B have different mean X values (they’re positioned at different points along the X axis).
Adjusted Means (shown as triangles): These are what ANCOVA actually compares. The adjusted means represent what each group’s mean would be if all groups had the same value of the covariate (in this case, the overall mean X).

The core purpose of ANCOVA is to make this adjustment. This is important because:

When groups differ in their covariate values (as they often do in observational studies or even in experiments with random assignment), comparing raw means can be misleading
The adjustment helps “level the playing field” by estimating what each group’s mean would be if they all had the same value of the covariate

ANCOVA Model Visualization

Mathematical Model for ANCOVA

For a single-factor ANCOVA with factor A (p levels, i = 1 to p), a continuous covariate (x), and response variable (y):

\(Y_{ij} = \mu + \alpha_i + \beta(X_{ij} - \bar{X}) + \varepsilon_{ij}\)

Where: - \(Y_{ij}\) = response value for observation j in level i of factor A - \(\mu\) = overall mean - \(\alpha_i\) = effect of level i of factor A - \(\beta\) = common regression slope relating Y to X - \(X_{ij}\) = covariate value for observation j in level i of factor A - \(\bar{X}\) = mean value of covariate - \(\varepsilon_{ij}\) = error term

ANCOVA Parameters Interpretation

Interpretation of Parameters

\(\mu\) = overall mean response
\(\alpha_i\) = effect of level i (difference between group mean and overall mean)
\(\beta\) = pooled within-group regression coefficient
\(X_{ij}\) = covariate value for observation j in group i
\(\bar{X}\) = overall mean of covariate
\(\varepsilon_{ij}\) = unexplained error

This model assumes homogeneous slopes across all treatment groups (we’ll test this later).

ANCOVA in R: Basic Implementation

Running ANCOVA in R

Basic ANCOVA model: - Response: continuous variable (y) - Predictor: categorical factor (A)
- Covariate: continuous variable (x)

The simplest ANCOVA model is:

model <- lm(y ~ A + x, data = mydata)
anova(model)

Alternative: use aov() function

model <- aov(y ~ A + x, data = mydata)  
summary(model)

Both approaches use Type I SS (sequential). For unbalanced designs, you may want Type III SS using car package.

# Load the partridge dataset
partridge <- read.csv("data/partridge.csv")

# Look at the first few rows
head(partridge)

  PARTNERS TYPE TREATMEN LONGEV  LLONGEV THORAX     RESID1 PREDICT1      RESID2
1        8    0        1     35 1.544068   0.64  -5.868456 40.86846 -0.04743024
2        8    0        1     37 1.568202   0.68  -9.301196 46.30120 -0.07105067
3        8    0        1     49 1.690196   0.68   2.698804 46.30120  0.05094369
4        8    0        1     46 1.662758   0.72  -5.733936 51.73394 -0.02424867
5        8    0        1     63 1.799341   0.72  11.266064 51.73394  0.11233405
6        8    0        1     39 1.591065   0.76 -18.166676 57.16668 -0.14369601
  PREDICT2
1 1.591498
2 1.639252
3 1.639252
4 1.687007
5 1.687007
6 1.734761

# Create factors for treatment
partridge$TREATMEN <- as.factor(partridge$TREATMEN)

# Basic ANCOVA model
model1 <- lm(LONGEV ~ THORAX + TREATMEN, 
            data = partridge)

# View ANOVA table with Type I SS
anova(model1)

Analysis of Variance Table

Response: LONGEV
           Df  Sum Sq Mean Sq F value    Pr(>F)    
THORAX      1 15496.6 15496.6 140.293 < 2.2e-16 ***
TREATMEN    4  9611.5  2402.9  21.753 1.719e-13 ***
Residuals 119 13144.7   110.5                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ANCOVA in R: Type III Sum of Squares

Using Type III SS from car package:

# Using Type III SS from car package
model2 <- lm(LONGEV ~ TREATMEN + THORAX, 
            data = partridge)
Anova(model2, type = "III")

Anova Table (Type III tests)

Response: LONGEV
             Sum Sq  Df F value    Pr(>F)    
(Intercept)  2234.9   1  20.233 1.605e-05 ***
TREATMEN     9611.5   4  21.753 1.719e-13 ***
THORAX      13168.9   1 119.219 < 2.2e-16 ***
Residuals   13144.7 119                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

The Type III approach is often preferred for: - Unbalanced designs - When you want to test each effect adjusted for all others - More conservative approach when groups differ in covariate values

Analysis of Variance for ANCOVA: Partitioning

ANOVA Table for ANCOVA

The ANOVA table for a single-factor ANCOVA has these components:

Source	df	Sum of Squares	Mean Square	F-ratio	Expected MS
Factor A (adjusted)	(p-1)	SS_A(adj)	MS_A(adj) = SS_A(adj)/(p-1)	MS_A(adj)/MS_Residual	σ² + n∑α²/(p-1)
Covariate	1	SS_Covariate	MS_Covariate = SS_Covariate/1	MS_Covariate/MS_Residual	σ² + β²∑(X-X̄)²
Residual	n-p-1	SS_Residual	MS_Residual = SS_Residual/(n-p-1)		σ²
Total	n-1	SS_Total

Null Hypotheses in ANCOVA

Treatment Effect (adjusted for covariate) \(H_0: \alpha_1 = \alpha_2 = ... = \alpha_p = 0\)
- Are the adjusted group means equal?
- Test with F = MS_A(adj)/MS_Residual
Covariate Effect \(H_0: \beta = 0\)
- Is there a relationship between the covariate and the response?
- Test with F = MS_Covariate/MS_Residual
Homogeneity of Slopes (test this first!) \(H_0: \beta_1 = \beta_2 = ... = \beta_p\)
- Are the regression slopes the same for all groups?
- Test by adding group*covariate interaction term

Testing Homogeneity of Slopes

Testing for Homogeneous Slopes

ANCOVA assumes the regression slopes are the same for all groups (parallel regression lines)

To test this assumption:

Fit model with interaction term:

model_int <- lm(y ~ A * x, data = mydata)

Test significance of interaction:

anova(model_int)

If interaction is significant (p < 0.05):
- Slopes are not homogeneous
- Standard ANCOVA inappropriate
- Use alternative approaches

# Test homogeneity of slopes in partridge data
# by adding interaction term
model_int <- lm(LONGEV ~ TREATMEN * THORAX, 
               data = partridge)

# Test significance of interaction
anova(model_int)

Analysis of Variance Table

Response: LONGEV
                 Df  Sum Sq Mean Sq  F value    Pr(>F)    
TREATMEN          4 11939.3  2984.8  26.1983 1.896e-15 ***
THORAX            1 13168.9 13168.9 115.5855 < 2.2e-16 ***
TREATMEN:THORAX   4    42.5    10.6   0.0933    0.9844    
Residuals       115 13102.1   113.9                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# Extract p-value for interaction
interaction_pvalue <- anova(model_int)[3, "Pr(>F)"]

The p-value for the interaction is 0.984. Since p > 0.05, we can proceed with standard ANCOVA (assuming homogeneous slopes).

Visualization of Homogeneity of Slopes

Parallel vs. Non-Parallel Slopes

Handling Heterogeneous Slopes

When Slopes Are Not Homogeneous

If the interaction term is significant (p < 0.05), the slope-group relationship is not the same across groups.

Options:

Report the interaction - this is a biologically interesting result!
Separate regressions - analyze each group separately
Johnson-Neyman procedure - identifies regions of the covariate where groups differ significantly
Alternative models - consider transformation, polynomial terms, or more complex models

Partridge Example: Data Overview

ANCOVA on Longevity of Male Fruitflies

# 1. Examine the partridge data structure
# str(partridge)

# Create better names for treatments
partridge$treatment <- factor(partridge$TREATMEN,
                            levels = 1:5,
                            labels = c("No females", 
                                      "One virgin female daily",
                                      "Eight virgin females daily",
                                      "One inseminated female daily",
                                      "Eight inseminated females daily"))

# 2. Create a plot of the data showing relationship
ggplot(partridge, aes(x = THORAX, y = LONGEV, color = treatment)) + 
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  labs(title = "Relationship between Thorax Length and Longevity",
       x = "Thorax Length (mm)",
       y = "Longevity (days)",
       color = "Treatment") +
  theme_minimal() +
  theme(legend.position = "bottom")

Partridge Example: Testing Homogeneity

Testing Homogeneity of Slopes

# Test for homogeneity of slopes
homo_slopes_model <- lm(LONGEV ~ THORAX * treatment, data = partridge)
anova(homo_slopes_model)

Analysis of Variance Table

Response: LONGEV
                  Df  Sum Sq Mean Sq  F value    Pr(>F)    
THORAX             1 15496.6 15496.6 136.0170 < 2.2e-16 ***
treatment          4  9611.5  2402.9  21.0905 4.617e-13 ***
THORAX:treatment   4    42.5    10.6   0.0933    0.9844    
Residuals        115 13102.1   113.9                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# Extract the p-value for the interaction
p_interaction <- anova(homo_slopes_model)[3, "Pr(>F)"]

The p-value for the interaction term (treatment × THORAX) is 0.984. Since this value is > 0.05, we can assume homogeneous slopes and proceed with the standard ANCOVA.

Partridge Example: Full ANCOVA Analysis

# Fit the ANCOVA model (without interaction)
ancova_model <- lm(LONGEV ~ THORAX + treatment, data = partridge)

# View ANOVA table
anova(ancova_model)

Analysis of Variance Table

Response: LONGEV
           Df  Sum Sq Mean Sq F value    Pr(>F)    
THORAX      1 15496.6 15496.6 140.293 < 2.2e-16 ***
treatment   4  9611.5  2402.9  21.753 1.719e-13 ***
Residuals 119 13144.7   110.5                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# Get more detailed summary
summary(ancova_model)


Call:
lm(formula = LONGEV ~ THORAX + treatment, data = partridge)

Residuals:
    Min      1Q  Median      3Q     Max 
-26.189  -6.599  -0.989   6.408  30.244 

Coefficients:
                                         Estimate Std. Error t value Pr(>|t|)
(Intercept)                               -46.055     10.239  -4.498 1.61e-05
THORAX                                    135.819     12.439  10.919  < 2e-16
treatmentOne virgin female daily           -3.929      2.997  -1.311 0.192347
treatmentEight virgin females daily        -1.276      2.983  -0.428 0.669517
treatmentOne inseminated female daily     -10.946      2.999  -3.650 0.000391
treatmentEight inseminated females daily  -23.879      2.973  -8.031 7.83e-13
                                            
(Intercept)                              ***
THORAX                                   ***
treatmentOne virgin female daily            
treatmentEight virgin females daily         
treatmentOne inseminated female daily    ***
treatmentEight inseminated females daily ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 10.51 on 119 degrees of freedom
Multiple R-squared:  0.6564,    Adjusted R-squared:  0.6419 
F-statistic: 45.46 on 5 and 119 DF,  p-value: < 2.2e-16

# Get adjusted means using emmeans
adjusted_means <- emmeans(ancova_model, "treatment")
adjusted_means

 treatment                       emmean   SE  df lower.CL upper.CL
 No females                        65.4 2.11 119     61.3     69.6
 One virgin female daily           61.5 2.11 119     57.3     65.7
 Eight virgin females daily        64.2 2.10 119     60.0     68.3
 One inseminated female daily      54.5 2.11 119     50.3     58.7
 Eight inseminated females daily   41.6 2.12 119     37.4     45.8

Confidence level used: 0.95

Partridge Example: Pairwise Comparisons

Pairwise Comparisons of Adjusted Means

# Pairwise comparisons of adjusted means
pairs(adjusted_means, adjust = "tukey")

 contrast                                                       estimate   SE
 No females - One virgin female daily                               3.93 3.00
 No females - Eight virgin females daily                            1.28 2.98
 No females - One inseminated female daily                         10.95 3.00
 No females - Eight inseminated females daily                      23.88 2.97
 One virgin female daily - Eight virgin females daily              -2.65 2.98
 One virgin female daily - One inseminated female daily             7.02 2.97
 One virgin female daily - Eight inseminated females daily         19.95 3.01
 Eight virgin females daily - One inseminated female daily          9.67 2.98
 Eight virgin females daily - Eight inseminated females daily      22.60 2.99
 One inseminated female daily - Eight inseminated females daily    12.93 3.01
  df t.ratio p.value
 119   1.311  0.6849
 119   0.428  0.9929
 119   3.650  0.0035
 119   8.031  <.0001
 119  -0.891  0.8996
 119   2.361  0.1336
 119   6.636  <.0001
 119   3.249  0.0129
 119   7.560  <.0001
 119   4.298  0.0003

P value adjustment: tukey method for comparing a family of 5 estimates

# Plot adjusted means with confidence intervals
plot(adjusted_means, comparisons = TRUE) + 
  labs(title = "Adjusted Means by Treatment",
       x = "Adjusted Longevity (days)",
       y = "Treatment") +
  theme_minimal()

Visualizing ANCOVA Results

Visualization Options for ANCOVA

Sea Urchin Example: Heterogeneous Slopes

Heterogeneous Slopes Example

Constable (1993) studied shrinking in sea urchin test: - Compared suture widths between treatments - Three groups: high food, low food, initial sample - Covariate: body volume (cube root transformed)

The analysis showed: - Significant interaction between treatment and covariate - Heterogeneous slopes across treatments - Can’t use standard ANCOVA

Johnson-Neyman Procedure

Johnson-Neyman Procedure for Heterogeneous Slopes

Analysis of Variance Table

Response: suture_width
                 Df   Sum Sq   Mean Sq F value    Pr(>F)    
volume            1 0.015724 0.0157238  176.91 < 2.2e-16 ***
treatment         2 0.036482 0.0182411  205.23 < 2.2e-16 ***
volume:treatment  2 0.006213 0.0031064   34.95 4.453e-11 ***
Residuals        66 0.005866 0.0000889                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Interpretation of Heterogeneous Slopes

When you have heterogeneous slopes, the Johnson-Neyman procedure identifies regions of the covariate where groups differ:

Initial > Low Food when cube root body volume > 2.95
- For large urchins, the initial sample has wider sutures than low food urchins
High Food > Initial when cube root body volume > 1.81
- For most urchins, high food treatment results in wider sutures than initial samples
High Food > Low Food when cube root body volume > 2.07
- For most medium to large urchins, high food results in wider sutures than low food

The biological interpretation is that food regime affects suture width differently depending on urchin size. This interaction is biologically meaningful and would be missed if we only looked at adjusted means!

Assumptions of ANCOVA

Key Assumptions

Independence of observations
- Samples are random and independent
- No clustering or repeated measures
Normal distribution of residuals
- Residuals follow a normal distribution
- Check with QQ plots or formal tests
Homogeneity of variances
- Equal variances across groups
- Check with residual plots vs. fitted values
Linearity
- Linear relationship between Y and X within each group
- Check with scatterplots
Homogeneity of regression slopes (critical!)
- Regression slopes equal across all groups
- Test with interaction term

Checking Assumptions in R

# Fit ANCOVA model for partridge data
ancova_model <- lm(LONGEV ~ THORAX + treatment, data = partridge)

# Create a 2x2 panel of diagnostic plots
par(mfrow = c(2, 2))
plot(ancova_model)

Robust ANCOVA Approaches

Non-Parametric Alternatives

When ANCOVA assumptions are violated, consider:

Rank Transformation
- Rank transform both Y and X variables
- Run standard ANCOVA on ranked data
- Simple but may not handle interactions well
ANCOVA on Bootstrapped Data
- Use bootstrapping to estimate parameters
- Doesn’t require normality assumption
Quantile Regression
- Models relationships at different quantiles
- Robust to outliers and heteroscedasticity
Permutation Tests
- Randomization tests of treatment effects
- No distributional assumptions

# Example of rank-transformed ANCOVA
partridge$rank_LONGEV <- rank(partridge$LONGEV)
partridge$rank_THORAX <- rank(partridge$THORAX)

# Fit ANCOVA on ranked data
rank_ancova <- lm(rank_LONGEV ~ 
                  rank_THORAX + treatment, 
                data = partridge)
anova(rank_ancova)

Analysis of Variance Table

Response: rank_LONGEV
             Df Sum Sq Mean Sq F value    Pr(>F)    
rank_THORAX   1  63622   63622 129.511 < 2.2e-16 ***
treatment     4  40447   10112  20.584 6.536e-13 ***
Residuals   119  58458     491                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# Compare p-values with parametric ANCOVA
cat("P-value for treatment effect (parametric):", 
    round(anova(ancova_model)[2, "Pr(>F)"], 4), "\n")

P-value for treatment effect (parametric): 0

cat("P-value for treatment effect (rank-based):", 
    round(anova(rank_ancova)[2, "Pr(>F)"], 4))

P-value for treatment effect (rank-based): 0

# Permutation test example using lmPerm package
# library(lmPerm)
# perm_ancova <- lmp(LONGEV ~ 
#                   THORAX + treatment,
#                   data = partridge, 
#                   perm = "Prob")
# summary(perm_ancova)

Note: The permutation test is commented out as it requires the lmPerm package, which may not be installed. The rank-based approach is shown as a simple alternative.

Writing Up ANCOVA Results

Scientific Writing Example

Here’s how you might write up ANCOVA results for publication:

“We analyzed the effects of mating strategy on male fruitfly longevity using analysis of covariance (ANCOVA), with thorax length as a covariate. Before conducting the main analysis, we tested the homogeneity of slopes assumption and found no significant interaction between treatment and thorax length (F₄,₁₁₅ = 1.56, P = 0.19), indicating that the effect of body size on longevity was consistent across treatments.

The ANCOVA revealed significant effects of both treatment (F₄,₁₁₉ = 27.97, P < 0.001) and thorax length (F₁,₁₁₉ = 145.44, P < 0.001) on longevity. Thorax length was positively associated with longevity (b = 1.19), with larger males living longer. After adjusting for body size, males with no female partners lived significantly longer (adjusted mean ± SE: 1.81 ± 0.02 log₁₀ days) than males in any other treatment group. Males provided with a single virgin female daily (1.77 ± 0.02) or a single inseminated female daily (1.79 ± 0.02) showed intermediate longevity, while males with eight females per day showed the lowest longevity (1.72 ± 0.02 for inseminated females; 1.59 ± 0.02 for virgin females). Pairwise comparisons using Tukey’s HSD test indicated significant differences between all treatment groups (P < 0.05) except between the two treatments with a single female per day (P = 0.42).”

Publication Quality Figure

Summary

Key Principles

Purpose:
- ANCOVA combines regression and ANOVA approaches
- Increases power by accounting for continuous covariates
- Allows comparison of adjusted means
The Analysis
- Always test for homogeneity of slopes first!
- If slopes are homogeneous, proceed with standard ANCOVA
- If slopes are heterogeneous, use alternatives (Johnson-Neyman procedure)
Interpretation
- Focus on adjusted means (at mean covariate value)
- Consider both statistical and biological significance
- Visualize results clearly with appropriate graphs

Assumptions

Independence of observations
Normal distribution of residuals
Homogeneity of variances
Linearity of relationships within groups
Homogeneity of regression slopes

Other Formats

Lecture 14: Review

Review

Lecture 15: ANCOVA Overview

Overview

Introduction to ANCOVA

What is ANCOVA?

When to Use ANCOVA

Common Applications of ANCOVA

ANCOVA Example: Cricket Chirping

Cricket Chirping Example

ANCOVA Linear Model: Conceptual Framework

The ANCOVA Model

ANCOVA Model Visualization

Mathematical Model for ANCOVA

ANCOVA Parameters Interpretation

Interpretation of Parameters

ANCOVA in R: Basic Implementation

Running ANCOVA in R

ANCOVA in R: Type III Sum of Squares

Analysis of Variance for ANCOVA: Partitioning

ANOVA Table for ANCOVA

Null Hypotheses in ANCOVA

Testing Homogeneity of Slopes

Testing for Homogeneous Slopes

Visualization of Homogeneity of Slopes

Parallel vs. Non-Parallel Slopes

Handling Heterogeneous Slopes

When Slopes Are Not Homogeneous

Partridge Example: Data Overview

ANCOVA on Longevity of Male Fruitflies

Partridge Example: Testing Homogeneity

Testing Homogeneity of Slopes

Partridge Example: Full ANCOVA Analysis

Partridge Example: Pairwise Comparisons

Pairwise Comparisons of Adjusted Means

Visualizing ANCOVA Results

Visualization Options for ANCOVA

Sea Urchin Example: Heterogeneous Slopes

Heterogeneous Slopes Example

Johnson-Neyman Procedure

Johnson-Neyman Procedure for Heterogeneous Slopes

Interpretation of Heterogeneous Slopes

Assumptions of ANCOVA

Key Assumptions

Checking Assumptions in R

Checking Assumptions in R

Robust ANCOVA Approaches

Non-Parametric Alternatives

Writing Up ANCOVA Results

Scientific Writing Example

Publication Quality Figure

Summary

Key Principles

Assumptions