27  Multi-Factor ANOVA

27.1 Introduction to Factorial Designs

While one-way ANOVA compares groups defined by a single factor, many biological experiments manipulate multiple factors simultaneously. A factorial design examines all combinations of factor levels, allowing us to study not only the independent effect of each factor but also how factors interact with each other.

Consider an experiment testing the effects of both temperature and nutrient concentration on algal growth. A factorial design would include all combinations: high temperature with high nutrients, high temperature with low nutrients, low temperature with high nutrients, and low temperature with low nutrients. This approach is more efficient than conducting separate experiments for each factor, and it reveals interactions that single-factor designs would miss.

Factorial designs are characterized by: - Multiple factors (independent variables) - All combinations of factor levels are tested - Ability to test for interactions between factors - Greater efficiency than separate one-factor experiments

The notation \(a \times b\) factorial design means factor A has \(a\) levels and factor B has \(b\) levels, producing \(a \times b\) treatment combinations. A \(2 \times 3\) design has 6 treatment groups; a \(2 \times 2 \times 2\) design has 8 treatment groups.

27.2 Two-Way ANOVA

Two-way ANOVA analyzes data from a two-factor experimental design. The model partitions variance into:

  1. Main effect of Factor A: The average effect of Factor A across all levels of Factor B
  2. Main effect of Factor B: The average effect of Factor B across all levels of Factor A
  3. Interaction effect (A × B): The extent to which the effect of one factor depends on the level of the other factor
  4. Residual (error): Unexplained variation within treatment groups

The statistical model is:

\[y_{ijk} = \mu + \alpha_i + \beta_j + (\alpha\beta)_{ij} + \epsilon_{ijk}\]

where: - \(\mu\) is the grand mean - \(\alpha_i\) is the effect of level \(i\) of Factor A - \(\beta_j\) is the effect of level \(j\) of Factor B - \((\alpha\beta)_{ij}\) is the interaction effect - \(\epsilon_{ijk}\) is the random error for observation \(k\) in treatment combination \(ij\)

Code 
# Simulated factorial experiment: Temperature × Nutrient effects on growth
set.seed(123)
n <- 15  # replicates per treatment

# Create factorial design data
temperature <- rep(c("Low", "High"), each = 2*n)
nutrient <- rep(rep(c("Low", "High"), each = n), 2)

# Simulate growth with main effects AND interaction
# Low temp & low nutrient: baseline 10
# High nutrient adds 5
# High temp adds 3
# BUT interaction: high temp × high nutrient gives extra boost of 4
growth <- numeric(4*n)
growth[temperature == "Low" & nutrient == "Low"] <- rnorm(n, 10, 1.5)
growth[temperature == "Low" & nutrient == "High"] <- rnorm(n, 15, 1.5)
growth[temperature == "High" & nutrient == "Low"] <- rnorm(n, 13, 1.5)
growth[temperature == "High" & nutrient == "High"] <- rnorm(n, 22, 1.5)  # Synergistic effect

factorial_data <- data.frame(
  temperature = factor(temperature, levels = c("Low", "High")),
  nutrient = factor(nutrient, levels = c("Low", "High")),
  growth = growth
)

# Visualize the design
ggplot(factorial_data, aes(x = temperature, y = growth, fill = nutrient)) +
  geom_boxplot(alpha = 0.7) +
  geom_jitter(width = 0.1, alpha = 0.3, size = 1) +
  labs(title = "Factorial Design: Temperature × Nutrient",
       subtitle = "Each combination of factor levels is tested",
       x = "Temperature",
       y = "Growth Rate",
       fill = "Nutrient\nLevel") +
  scale_fill_manual(values = c("Low" = "#E69F00", "High" = "#56B4E9")) +
  theme_minimal(base_size = 12)
Figure 27.1: Two-way ANOVA factorial design showing main effects and interaction
Code 
# Fit the two-way ANOVA model
factorial_aov <- aov(growth ~ temperature * nutrient, data = factorial_data)
summary(factorial_aov)
                     Df Sum Sq Mean Sq F value   Pr(>F)    
temperature           1  427.4   427.4  228.63  < 2e-16 ***
nutrient              1  638.7   638.7  341.63  < 2e-16 ***
temperature:nutrient  1   67.7    67.7   36.19 1.43e-07 ***
Residuals            56  104.7     1.9                     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

The ANOVA table shows three F-tests: - temperature: Tests whether the main effect of temperature is significant - nutrient: Tests whether the main effect of nutrient is significant - temperature:nutrient: Tests whether the interaction is significant

27.3 Understanding Interactions

An interaction occurs when the effect of one factor depends on the level of another factor. Interactions are one of the most important features of factorial designs—they reveal that factors do not operate independently.

Types of effects:

  1. Additive effects (no interaction): The effect of Factor A is the same regardless of Factor B’s level. Effects simply add together.

  2. Synergistic interaction: The combined effect is greater than the sum of individual effects. For example, two drugs together might be more effective than expected from their individual effects.

  3. Antagonistic interaction: The combined effect is less than expected. One factor might diminish or reverse the effect of another.

Interaction Plots

The clearest way to understand interactions is through interaction plots, which show the mean response at each factor combination.

Code 
# Calculate means for each combination
means_data <- factorial_data %>%
  group_by(temperature, nutrient) %>%
  summarize(mean_growth = mean(growth),
            se = sd(growth) / sqrt(n()),
            .groups = "drop")

# Create interaction plot
ggplot(means_data, aes(x = temperature, y = mean_growth,
                       color = nutrient, group = nutrient)) +
  geom_line(linewidth = 1.2) +
  geom_point(size = 4) +
  geom_errorbar(aes(ymin = mean_growth - se, ymax = mean_growth + se),
                width = 0.1, linewidth = 1) +
  labs(title = "Interaction Plot: Temperature × Nutrient",
       subtitle = "Non-parallel lines indicate an interaction effect",
       x = "Temperature",
       y = "Mean Growth Rate",
       color = "Nutrient\nLevel") +
  scale_color_manual(values = c("Low" = "#E69F00", "High" = "#56B4E9")) +
  theme_minimal(base_size = 12) +
  theme(legend.position = "right")

# Alternative: base R interaction.plot
interaction.plot(
  x.factor = factorial_data$temperature,
  trace.factor = factorial_data$nutrient,
  response = factorial_data$growth,
  col = c("#E69F00", "#56B4E9"),
  lwd = 2,
  xlab = "Temperature",
  ylab = "Mean Growth Rate",
  trace.label = "Nutrient"
)
Figure 27.2: Interaction plot showing non-parallel lines indicating interaction between factors
Figure 27.3: Interaction plot showing non-parallel lines indicating interaction between factors

Interpreting interaction plots:

  • Parallel lines suggest no interaction—the effect of one factor is constant across levels of the other
  • Non-parallel lines suggest an interaction—the effect of one factor changes depending on the other factor
  • Crossing lines indicate a strong interaction, potentially with a reversal of effects
Figure 27.4: Illustration of interaction patterns showing parallel versus non-parallel lines

The figure above illustrates common interaction patterns. In panel (a), parallel lines indicate no interaction—the effect of Factor A is the same at both levels of Factor B. In panel (b), non-parallel lines reveal an interaction—the effect of Factor A differs depending on Factor B’s level.

27.4 Interpreting Main Effects vs Interactions

When a significant interaction exists, interpreting main effects requires caution. The main effect represents an average across levels of the other factor, but if there’s an interaction, this average may not meaningfully represent what happens at any particular level.

When Interactions are Present

If the interaction is significant, focus on simple effects—the effect of one factor at each level of the other factor—rather than main effects. The main effect is an average that may obscure important differences.

Simple effects analysis examines the effect of one factor separately at each level of the other factor:

Code 
# Simple effects: effect of nutrient at each temperature level
# Low temperature
low_temp_data <- factorial_data %>% filter(temperature == "Low")
t.test(growth ~ nutrient, data = low_temp_data)

    Welch Two Sample t-test

data:  growth by nutrient
t = -8.2269, df = 26.34, p-value = 9.446e-09
alternative hypothesis: true difference in means between group Low and group High is not equal to 0
95 percent confidence interval:
 -5.500596 -3.302476
sample estimates:
 mean in group Low mean in group High 
          10.22858           14.63011
Code 
# High temperature
high_temp_data <- factorial_data %>% filter(temperature == "High")
t.test(growth ~ nutrient, data = high_temp_data)

    Welch Two Sample t-test

data:  growth by nutrient
t = -18.771, df = 27.893, p-value < 2.2e-16
alternative hypothesis: true difference in means between group Low and group High is not equal to 0
95 percent confidence interval:
 -9.593152 -7.705141
sample estimates:
 mean in group Low mean in group High 
          13.44293           22.09208

In our example, the nutrient effect is significant at both temperatures, but the magnitude differs—the interaction shows that the nutrient boost is stronger at high temperature.

27.5 ANCOVA: Analysis of Covariance

Analysis of Covariance (ANCOVA) combines ANOVA with regression by including both categorical factors and continuous covariates. The covariate is a continuous variable that you want to control for—it’s not a treatment you manipulate, but a source of variation you want to account for.

Common uses of ANCOVA: - Controlling for pre-existing differences (baseline measurements) - Increasing precision by removing variance explained by the covariate - Adjusting for confounding variables - Testing whether regression slopes differ across groups

The ANCOVA Model

The ANCOVA model for one factor and one covariate is:

\[y_{ij} = \mu + \alpha_i + \beta(x_{ij} - \bar{x}) + \epsilon_{ij}\]

where: - \(\mu\) is the overall mean - \(\alpha_i\) is the effect of group \(i\) - \(\beta\) is the regression slope (common to all groups) - \(x_{ij}\) is the covariate value - \(\bar{x}\) is the mean of the covariate - \(\epsilon_{ij}\) is the random error

The key assumption is that the relationship between the covariate and response has the same slope in all groups (homogeneity of regression slopes).

Code 
# Simulated ANCOVA example: Effect of diet on final weight, controlling for initial weight
set.seed(567)
n_per_group <- 20

# Three diet treatments
diet_data <- data.frame(
  diet = factor(rep(c("Control", "Low-fat", "High-protein"), each = n_per_group)),
  initial_weight = c(
    rnorm(n_per_group, 75, 8),   # Control group
    rnorm(n_per_group, 78, 8),   # Low-fat (slightly heavier initially)
    rnorm(n_per_group, 73, 8)    # High-protein (slightly lighter initially)
  )
) %>%
  mutate(
    # Final weight depends on diet AND initial weight
    # Diet effects: Control = 0, Low-fat = -3, High-protein = -5
    diet_effect = case_when(
      diet == "Control" ~ 0,
      diet == "Low-fat" ~ -3,
      diet == "High-protein" ~ -5
    ),
    # Final weight = initial + diet effect + some regression to mean + noise
    final_weight = initial_weight + diet_effect + 0.3 * (initial_weight - 75) + rnorm(n(), 0, 3)
  )

# Visualize the ANCOVA setup
ggplot(diet_data, aes(x = initial_weight, y = final_weight, color = diet)) +
  geom_point(alpha = 0.7, size = 2) +
  geom_smooth(method = "lm", se = FALSE, linewidth = 1) +
  labs(title = "ANCOVA: Diet Effect on Final Weight",
       subtitle = "Parallel regression lines show diet effects while controlling for initial weight",
       x = "Initial Weight (kg)",
       y = "Final Weight (kg)",
       color = "Diet") +
  scale_color_manual(values = c("Control" = "#E69F00", "Low-fat" = "#56B4E9", "High-protein" = "#009E73")) +
  theme_minimal(base_size = 12)
Figure 27.5: ANCOVA example showing treatment effects on final weight while controlling for initial weight

Fitting ANCOVA in R

Code 
# Fit ANCOVA model
ancova_model <- aov(final_weight ~ initial_weight + diet, data = diet_data)
summary(ancova_model)
               Df Sum Sq Mean Sq F value   Pr(>F)    
initial_weight  1   7582    7582  607.91  < 2e-16 ***
diet            2    270     135   10.83 0.000106 ***
Residuals      56    698      12                     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Note the order of terms: the covariate (initial_weight) is entered first to remove its effect before testing the factor (diet).

Code 
# Compare to ANOVA without covariate
anova_only <- aov(final_weight ~ diet, data = diet_data)
summary(anova_only)
            Df Sum Sq Mean Sq F value   Pr(>F)    
diet         2   1951   975.5   8.426 0.000623 ***
Residuals   57   6600   115.8                     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code 
# The ANCOVA has smaller residual SS and more power
cat("\nResidual SS (ANOVA):", round(sum(residuals(anova_only)^2), 1), "\n")

Residual SS (ANOVA): 6599.6
Code 
cat("Residual SS (ANCOVA):", round(sum(residuals(ancova_model)^2), 1), "\n")
Residual SS (ANCOVA): 698.5

Adjusted Means

ANCOVA produces adjusted means—the group means estimated at the overall mean of the covariate. These are the means we would expect if all groups had started with the same covariate value.

Code 
# Get adjusted means
library(emmeans)
ancova_emm <- emmeans(ancova_model, "diet")
ancova_emm
 diet         emmean    SE df lower.CL upper.CL
 Control        73.7 0.802 56     72.1     75.3
 High-protein   68.6 0.818 56     66.9     70.2
 Low-fat        70.3 0.864 56     68.6     72.0

Confidence level used: 0.95
Code 
# Pairwise comparisons of adjusted means
pairs(ancova_emm, adjust = "tukey")
 contrast                   estimate   SE df t.ratio p.value
 Control - (High-protein)       5.11 1.12 56   4.567 <0.0001
 Control - (Low-fat)            3.38 1.22 56   2.774  0.0202
 (High-protein) - (Low-fat)    -1.73 1.25 56  -1.385  0.3554

P value adjustment: tukey method for comparing a family of 3 estimates

Testing Homogeneity of Slopes

A critical assumption is that regression slopes are equal across groups. Test this by including the interaction term:

Code 
# Test for unequal slopes (interaction between covariate and factor)
slope_test <- aov(final_weight ~ initial_weight * diet, data = diet_data)
summary(slope_test)
                    Df Sum Sq Mean Sq F value   Pr(>F)    
initial_weight       1   7582    7582  594.01  < 2e-16 ***
diet                 2    270     135   10.58 0.000133 ***
initial_weight:diet  2      9       5    0.36 0.699390    
Residuals           54    689      13                     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

A significant interaction indicates slopes differ across groups, violating the ANCOVA assumption. In that case, you should: 1. Report separate regressions for each group 2. Use a more complex model (separate slopes ANCOVA) 3. Consider whether the interaction itself is scientifically meaningful

Code 
# Visualize slopes by group
ggplot(diet_data, aes(x = initial_weight, y = final_weight, color = diet)) +
  geom_point(alpha = 0.6) +
  geom_smooth(method = "lm", se = TRUE, alpha = 0.2) +
  labs(title = "Testing Homogeneity of Regression Slopes",
       subtitle = "Slopes should be approximately parallel for valid ANCOVA",
       x = "Initial Weight (kg)",
       y = "Final Weight (kg)",
       color = "Diet") +
  scale_color_manual(values = c("Control" = "#E69F00", "Low-fat" = "#56B4E9", "High-protein" = "#009E73")) +
  theme_minimal(base_size = 12)
Figure 27.6: Testing homogeneity of slopes: parallel lines indicate the assumption is met
ANCOVA Assumptions
  1. Independence of observations
  2. Normality of residuals
  3. Homogeneity of variance across groups
  4. Linear relationship between covariate and response
  5. Homogeneity of regression slopes (equal slopes across groups)
  6. Covariate measured without error (or with negligible error)
  7. Covariate independent of treatment (especially important in observational studies)

The last assumption is critical: if the treatment affects the covariate, ANCOVA can remove real treatment effects. The covariate should ideally be measured before treatment is applied.

When to Use ANCOVA

Use ANCOVA when: - You have a continuous covariate measured before treatment - You want to increase precision by accounting for baseline variation - Groups differ on the covariate (but not due to treatment) - The covariate-response relationship is linear with equal slopes

Avoid ANCOVA when: - The covariate is affected by treatment - Slopes clearly differ across groups - The covariate is categorical (use factorial ANOVA instead) - Sample sizes are very small

27.6 Nested Designs

In nested designs, levels of one factor exist only within levels of another factor. This differs from a factorial design where all combinations of factors are crossed.

Common examples in biology: - Students nested within classrooms (each student is in only one classroom) - Samples nested within sites (each sample comes from one site) - Technicians nested within labs (each technician works in one lab) - Subsamples nested within experimental units

In a nested design, we cannot estimate an interaction because not all factor combinations exist. The nested factor is typically a random effect representing sampling variability.

Notation: Factor B is nested within Factor A, written as B(A) or B/A.

Code 
# Simulated nested design: Technicians (nested within Labs)
set.seed(456)
n_labs <- 3
n_techs_per_lab <- 3
n_measurements <- 5

lab <- rep(paste0("Lab", 1:n_labs), each = n_techs_per_lab * n_measurements)
tech <- rep(paste0("Tech", 1:(n_labs * n_techs_per_lab)), each = n_measurements)

# Lab effects (fixed)
lab_effects <- c(100, 105, 110)
# Technician effects (random, nested within labs)
tech_effects <- rnorm(n_labs * n_techs_per_lab, 0, 3)

measurement <- numeric(length(lab))
for (i in 1:length(lab)) {
  lab_idx <- as.numeric(substr(lab[i], 4, 4))
  tech_idx <- as.numeric(substr(tech[i], 5, 5))
  measurement[i] <- lab_effects[lab_idx] + tech_effects[tech_idx] + rnorm(1, 0, 2)
}

nested_data <- data.frame(
  lab = factor(lab),
  tech = factor(tech),
  measurement = measurement
)

# Visualize nested structure
ggplot(nested_data, aes(x = tech, y = measurement, color = lab)) +
  geom_point(alpha = 0.6, size = 2) +
  stat_summary(fun = mean, geom = "point", size = 4, shape = 18) +
  facet_wrap(~ lab, scales = "free_x") +
  labs(title = "Nested Design: Technicians within Labs",
       subtitle = "Each technician works in only one lab",
       x = "Technician",
       y = "Measurement") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        legend.position = "none")
Figure 27.7: Nested design example: technicians nested within labs
Code 
# Analysis of nested design
# Technician is nested within lab: tech %in% lab or tech/lab
nested_aov <- aov(measurement ~ lab + Error(tech %in% lab), data = nested_data)
summary(nested_aov)

Error: tech:lab
          Df Sum Sq Mean Sq F value  Pr(>F)   
lab        2 1157.6   578.8   24.02 0.00137 **
Residuals  6  144.6    24.1                   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Error: Within
          Df Sum Sq Mean Sq F value Pr(>F)
Residuals 36  150.8   4.189
Code 
# Alternative using lme4 for mixed effects
library(lme4)
nested_lmer <- lmer(measurement ~ lab + (1|lab:tech), data = nested_data)
summary(nested_lmer)
Linear mixed model fit by REML ['lmerMod']
Formula: measurement ~ lab + (1 | lab:tech)
   Data: nested_data

REML criterion at convergence: 198

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.12446 -0.44723 -0.04123  0.60879  1.92716 

Random effects:
 Groups   Name        Variance Std.Dev.
 lab:tech (Intercept) 3.982    1.996   
 Residual             4.189    2.047   
Number of obs: 45, groups:  lab:tech, 9

Fixed effects:
            Estimate Std. Error t value
(Intercept)  100.966      1.268  79.653
labLab2        1.425      1.793   0.795
labLab3       11.401      1.793   6.360

Correlation of Fixed Effects:
        (Intr) labLb2
labLab2 -0.707       
labLab3 -0.707  0.500

The nested design partitions variance into: - Between labs (fixed effect) - Between technicians within labs (random effect) - Within technicians (residual error)

27.7 Repeated Measures ANOVA

When the same subjects are measured multiple times under different conditions, observations are not independent. Repeated measures ANOVA accounts for the correlation structure in the data by treating subjects as a blocking factor or random effect.

Advantages: - Increased statistical power (controls for individual differences) - Requires fewer subjects - Each subject serves as their own control

Assumptions: - Sphericity: The variance of differences between all pairs of repeated measures should be equal - Violations of sphericity can be corrected using Greenhouse-Geisser or Huynh-Feldt adjustments

Code 
# Simulated repeated measures: Drug effect over time
set.seed(789)
n_subjects <- 12
timepoints <- c("Baseline", "Week_1", "Week_2", "Week_4")

# Each subject has a baseline level
baseline_values <- rnorm(n_subjects, 50, 10)

# Treatment effect increases over time
time_effects <- c(0, -3, -6, -8)

# Create data
rm_data <- expand.grid(
  subject = factor(1:n_subjects),
  time = factor(timepoints, levels = timepoints)
) %>%
  arrange(subject, time) %>%
  mutate(
    baseline = rep(baseline_values, each = length(timepoints)),
    time_num = as.numeric(time),
    response = baseline + time_effects[time_num] + rnorm(n(), 0, 2)
  )

# Visualize repeated measures
ggplot(rm_data, aes(x = time, y = response, group = subject)) +
  geom_line(alpha = 0.4, color = "gray40") +
  geom_point(alpha = 0.4, size = 2) +
  stat_summary(aes(group = 1), fun = mean, geom = "line",
               color = "red", linewidth = 1.5) +
  stat_summary(aes(group = 1), fun = mean, geom = "point",
               color = "red", size = 4) +
  labs(title = "Repeated Measures: Drug Response Over Time",
       subtitle = "Gray lines show individual subjects; red line shows mean response",
       x = "Time Point",
       y = "Response Variable") +
  theme_minimal(base_size = 12)
Figure 27.8: Repeated measures design showing within-subject changes over time
Code 
# Traditional repeated measures ANOVA
# Error term specifies within-subject factor
rm_aov <- aov(response ~ time + Error(subject/time), data = rm_data)
summary(rm_aov)

Error: subject
          Df Sum Sq Mean Sq F value Pr(>F)
Residuals 11   2802   254.7               

Error: subject:time
          Df Sum Sq Mean Sq F value   Pr(>F)    
time       3  525.4  175.13   45.13 8.86e-12 ***
Residuals 33  128.0    3.88                     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Code 
# Check sphericity assumption
library(car)
# Reshape to wide format for Mauchly's test
rm_wide <- rm_data %>%
  select(subject, time, response) %>%
  pivot_wider(names_from = time, values_from = response)

# Alternative: Use mixed effects model (handles violations better)
library(lme4)
rm_lmer <- lmer(response ~ time + (1|subject), data = rm_data)
summary(rm_lmer)
Linear mixed model fit by REML ['lmerMod']
Formula: response ~ time + (1 | subject)
   Data: rm_data

REML criterion at convergence: 240.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.7487 -0.5256  0.1230  0.6037  1.3658 

Random effects:
 Groups   Name        Variance Std.Dev.
 subject  (Intercept) 62.71    7.919   
 Residual              3.88    1.970   
Number of obs: 48, groups:  subject, 12

Fixed effects:
            Estimate Std. Error t value
(Intercept)  46.1978     2.3557  19.611
timeWeek_1   -3.0335     0.8042  -3.772
timeWeek_2   -6.7738     0.8042  -8.423
timeWeek_4   -8.5300     0.8042 -10.607

Correlation of Fixed Effects:
           (Intr) tmWk_1 tmWk_2
timeWeek_1 -0.171              
timeWeek_2 -0.171  0.500       
timeWeek_4 -0.171  0.500  0.500
Code 
anova(rm_lmer)
Analysis of Variance Table
     npar Sum Sq Mean Sq F value
time    3  525.4  175.13  45.134

Post-hoc tests for repeated measures:

Code 
# Pairwise comparisons with correction for multiple testing
library(emmeans)
rm_emm <- emmeans(rm_lmer, "time")
pairs(rm_emm, adjust = "bonferroni")
 contrast          estimate    SE df t.ratio p.value
 Baseline - Week_1     3.03 0.804 33   3.772  0.0038
 Baseline - Week_2     6.77 0.804 33   8.423 <0.0001
 Baseline - Week_4     8.53 0.804 33  10.607 <0.0001
 Week_1 - Week_2       3.74 0.804 33   4.651  0.0003
 Week_1 - Week_4       5.50 0.804 33   6.835 <0.0001
 Week_2 - Week_4       1.76 0.804 33   2.184  0.2171

Degrees-of-freedom method: kenward-roger 
P value adjustment: bonferroni method for 6 tests

27.8 Mixed Effects Models

Mixed effects models (also called multilevel models or hierarchical models) include both fixed effects (factors of primary interest) and random effects (sources of random variation).

When to use mixed effects models: - Repeated measures or longitudinal data - Nested or hierarchical data structures - Multiple sources of random variation - Unbalanced designs or missing data - Continuous covariates combined with grouping factors

Fixed vs Random Effects:

  • Fixed effects: Specific levels you chose (e.g., specific drug treatments, particular temperatures)
  • Random effects: Levels sampled from a larger population (e.g., subjects, batches, field sites)

The lme4 package in R provides powerful tools for mixed effects models:

Code 
# Example: Growth curve with random intercepts and slopes
set.seed(321)
n_individuals <- 15
n_timepoints <- 6

growth_data <- expand.grid(
  individual = factor(1:n_individuals),
  time = 0:(n_timepoints-1)
) %>%
  mutate(
    # Random intercept for each individual
    intercept = rep(rnorm(n_individuals, 5, 1), each = n_timepoints),
    # Random slope for each individual
    slope = rep(rnorm(n_individuals, 0.8, 0.15), each = n_timepoints),
    # Generate size with individual variation
    size = intercept + slope * time + rnorm(n(), 0, 0.3)
  )

# Fit mixed effects model with random intercepts and slopes
mixed_model <- lmer(size ~ time + (time|individual), data = growth_data)
summary(mixed_model)
Linear mixed model fit by REML ['lmerMod']
Formula: size ~ time + (time | individual)
   Data: growth_data

REML criterion at convergence: 233.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.8644 -0.6987 -0.1743  0.6597  2.0678 

Random effects:
 Groups     Name        Variance Std.Dev. Corr 
 individual (Intercept) 0.10232  0.3199        
            time        0.01272  0.1128   -0.13
 Residual               0.61532  0.7844        
Number of obs: 90, groups:  individual, 15

Fixed effects:
            Estimate Std. Error t value
(Intercept)   5.2214     0.1683   31.03
time          0.7760     0.0565   13.73

Correlation of Fixed Effects:
     (Intr)
time -0.650
Code 
# Compare to model with only random intercepts
mixed_model_int <- lmer(size ~ time + (1|individual), data = growth_data)

# Likelihood ratio test
anova(mixed_model_int, mixed_model)
Data: growth_data
Models:
mixed_model_int: size ~ time + (1 | individual)
mixed_model: size ~ time + (time | individual)
                npar    AIC    BIC  logLik -2*log(L)  Chisq Df Pr(>Chisq)
mixed_model_int    4 236.62 246.62 -114.31    228.62                     
mixed_model        6 239.35 254.35 -113.68    227.35 1.2682  2     0.5304
Code 
# Visualize random effects
ggplot(growth_data, aes(x = time, y = size, group = individual)) +
  geom_line(alpha = 0.3, color = "gray40") +
  geom_point(alpha = 0.3, size = 1.5) +
  geom_abline(intercept = fixef(mixed_model)[1],
              slope = fixef(mixed_model)[2],
              color = "red", linewidth = 1.5) +
  labs(title = "Mixed Effects Model: Growth Curves",
       subtitle = "Gray lines show individuals; red line shows population average (fixed effect)",
       x = "Time",
       y = "Size") +
  theme_minimal(base_size = 12)
Figure 27.9: Mixed effects model showing fixed effect (population trend) and random effects (individual variation)

27.9 Split-Plot Designs

Split-plot designs arise when different factors are applied at different scales or when complete randomization is impractical. Originally developed for agricultural field trials, they’re common in biological experiments.

Structure: - Whole plots: Larger experimental units receiving one factor (hard-to-change factor) - Split plots (or subplots): Smaller units within whole plots receiving another factor (easy-to-change factor)

Example: Testing the effect of irrigation (whole plot factor) and fertilizer (split-plot factor) on crop yield. Irrigation is applied to large field plots, while different fertilizers can be applied to smaller areas within each irrigation plot.

In laboratory settings: - Whole plot: Temperature chambers (hard to randomize, limited number) - Split plot: Different nutrient treatments within each chamber (easy to randomize)

Code 
# Simulated split-plot design
# Whole plot: Temperature (2 levels, 4 replicates each = 8 chambers)
# Split plot: Nutrient (3 levels per chamber)
set.seed(654)

n_chambers_per_temp <- 4
n_nutrients <- 3
n_reps <- 2  # technical replicates within each split plot

split_plot_data <- expand.grid(
  temp = factor(rep(c("Low", "High"), each = n_chambers_per_temp)),
  chamber = factor(1:(2 * n_chambers_per_temp)),
  nutrient = factor(paste0("N", 1:n_nutrients)),
  rep = 1:n_reps
) %>%
  mutate(
    temp_effect = ifelse(temp == "Low", 0, 5),
    # Chambers vary even at same temperature (whole plot error)
    chamber_effect = rep(rnorm(2 * n_chambers_per_temp, 0, 1.5),
                        each = n_nutrients * n_reps),
    nutrient_effect = rep(c(0, 3, 6), times = 2 * n_chambers_per_temp * n_reps),
    # Interaction between temp and nutrient
    interaction = ifelse(temp == "High" & nutrient == "N3", 3, 0),
    yield = 10 + temp_effect + chamber_effect + nutrient_effect +
            interaction + rnorm(n(), 0, 1)
  )
Error in `mutate()`:
ℹ In argument: `chamber_effect = rep(...)`.
Caused by error:
! `chamber_effect` must be size 384 or 1, not 48.
Code 
# Visualize split-plot structure
ggplot(split_plot_data, aes(x = nutrient, y = yield, color = temp)) +
  geom_point(alpha = 0.4, position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.5)) +
  stat_summary(fun = mean, geom = "point", size = 4,
               position = position_dodge(width = 0.5)) +
  stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.2,
               position = position_dodge(width = 0.5)) +
  facet_wrap(~ temp) +
  labs(title = "Split-Plot Design: Temperature (whole plot) × Nutrient (split plot)",
       subtitle = "Temperature applied to whole chambers; nutrients varied within chambers",
       x = "Nutrient Level (Split-Plot Factor)",
       y = "Yield",
       color = "Temperature\n(Whole-Plot)") +
  theme_minimal(base_size = 11)
Error:
! object 'split_plot_data' not found
Code 
# Analysis of split-plot design
# Whole plot factor: temperature (tested against chamber error)
# Split plot factor: nutrient (tested against split-plot error)
# Use mixed model with chamber as random effect

library(lme4)
split_plot_model <- lmer(yield ~ temp * nutrient + (1|chamber),
                         data = split_plot_data)
Error:
! bad 'data': object 'split_plot_data' not found
Code 
summary(split_plot_model)
Error in `h()`:
! error in evaluating the argument 'object' in selecting a method for function 'summary': object 'split_plot_model' not found
Code 
anova(split_plot_model)
Error:
! object 'split_plot_model' not found
Code 
# Note: The whole-plot factor (temp) has fewer degrees of freedom
# because it varies only among chambers, not within them

Key features of split-plot analysis: - Different error terms for whole-plot and split-plot factors - Whole-plot tests have fewer degrees of freedom (less powerful) - Split-plot tests are more powerful (more replication) - Interactions use split-plot error term

27.10 Practical Considerations for Complex Designs

Sample Size and Power

Complex designs require careful power analysis. The power for detecting: - Main effects depends on the number of replicates and effect size - Interactions is typically lower than for main effects—interactions require larger samples

Use simulation-based power analysis for complex designs where analytical solutions are difficult.

Balanced vs Unbalanced Designs

Balanced designs (equal sample sizes in all cells) are preferable: - Simpler interpretation - Greater statistical power - Robust to assumption violations - Easier to test for interactions

Unbalanced designs complicate analysis: - Type I, II, and III sums of squares give different results - Main effects and interactions are correlated - Reduced power for some effects

When unavoidable, use Type III sums of squares with caution and consider mixed effects models.

Effect Sizes in Multi-Factor ANOVA

Report effect sizes for all significant effects:

Partial eta-squared (\(\eta^2_p\)): Proportion of variance explained by each effect, removing variance from other effects:

\[\eta^2_p = \frac{SS_{effect}}{SS_{effect} + SS_{error}}\]

Code 
# Calculate effect sizes from factorial ANOVA
library(effectsize)
eta_squared(factorial_aov, partial = TRUE)
# Effect Size for ANOVA (Type I)

Parameter            | Eta2 (partial) |       95% CI
----------------------------------------------------
temperature          |           0.80 | [0.73, 1.00]
nutrient             |           0.86 | [0.80, 1.00]
temperature:nutrient |           0.39 | [0.23, 1.00]

- One-sided CIs: upper bound fixed at [1.00].

Multiple Comparisons

With many factor levels and combinations, multiple comparison corrections become crucial:

Code 
# Post-hoc comparisons for factorial design
library(emmeans)
factorial_emm <- emmeans(factorial_aov, ~ temperature * nutrient)

# All pairwise comparisons
pairs(factorial_emm, adjust = "tukey")
 contrast             estimate    SE df t.ratio p.value
 Low Low - High Low      -3.21 0.499 56  -6.438 <0.0001
 Low Low - Low High      -4.40 0.499 56  -8.816 <0.0001
 Low Low - High High    -11.86 0.499 56 -23.761 <0.0001
 High Low - Low High     -1.19 0.499 56  -2.378  0.0932
 High Low - High High    -8.65 0.499 56 -17.323 <0.0001
 Low High - High High    -7.46 0.499 56 -14.946 <0.0001

P value adjustment: tukey method for comparing a family of 4 estimates
Code 
# Simple effects: nutrient effect at each temperature
nutrient_simple <- emmeans(factorial_aov, ~ nutrient | temperature)
pairs(nutrient_simple, adjust = "bonferroni")
temperature = Low:
 contrast   estimate    SE df t.ratio p.value
 Low - High    -4.40 0.499 56  -8.816 <0.0001

temperature = High:
 contrast   estimate    SE df t.ratio p.value
 Low - High    -8.65 0.499 56 -17.323 <0.0001

Model Assumptions

Check assumptions for all error terms in complex designs:

Code 
# Diagnostic plots
par(mfrow = c(2, 2))
plot(factorial_aov)
par(mfrow = c(1, 1))

# Test homogeneity of variance
library(car)
leveneTest(growth ~ temperature * nutrient, data = factorial_data)
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  3  0.8227 0.4869
      56
Code 
# Test normality of residuals
shapiro.test(residuals(factorial_aov))

    Shapiro-Wilk normality test

data:  residuals(factorial_aov)
W = 0.98365, p-value = 0.6001
Figure 27.10: Diagnostic plots for checking ANOVA assumptions

Reporting Results

When reporting multi-factor ANOVA results, include:

  1. Descriptive statistics for each cell (means, SDs, sample sizes)
  2. ANOVA table with F-values, degrees of freedom, and p-values for all effects
  3. Effect sizes (partial eta-squared or omega-squared)
  4. Post-hoc tests or simple effects when interactions are significant
  5. Diagnostic plots confirming assumptions (in supplementary materials)
  6. Graphical display of means with error bars (interaction plots for two-way designs)

Example reporting:

We conducted a 2 × 2 factorial ANOVA to examine the effects of temperature (low vs. high) and nutrient level (low vs. high) on algal growth rate. There was a significant main effect of temperature, F(1, 56) = 45.2, p < .001, \(\eta^2_p\) = 0.45, and a significant main effect of nutrient level, F(1, 56) = 89.7, p < .001, \(\eta^2_p\) = 0.62. Critically, there was a significant temperature × nutrient interaction, F(1, 56) = 12.3, p < .001, \(\eta^2_p\) = 0.18. Simple effects analysis revealed that the nutrient effect was significant at both temperatures (both p < .001), but the magnitude was greater at high temperature (Δ = 9.1 units) than at low temperature (Δ = 5.2 units).

27.11 Summary

Multi-factor ANOVA extends the basic ANOVA framework to designs with multiple factors:

  • Factorial designs test all combinations of factor levels, allowing estimation of main effects and interactions
  • Interactions reveal that factors do not operate independently—the effect of one factor depends on another
  • Interaction plots with non-parallel lines indicate interactions; interpret simple effects rather than main effects when interactions are significant
  • ANCOVA combines categorical factors with continuous covariates, controlling for baseline variation and increasing statistical power
  • Nested designs have hierarchical structure where levels of one factor exist only within another; common in hierarchical sampling
  • Repeated measures ANOVA accounts for correlation when the same subjects are measured multiple times
  • Mixed effects models include both fixed and random effects, providing flexibility for complex data structures
  • Split-plot designs arise when factors are applied at different scales or complete randomization is impractical
  • Always report effect sizes, check assumptions, and use appropriate corrections for multiple comparisons
  • Balanced designs are preferable but mixed effects models can handle unbalanced data

Understanding when and how to use multi-factor ANOVA is essential for designing efficient experiments and extracting maximum information from biological data. The ability to test for interactions is particularly valuable—discovering that factors interact often leads to deeper biological insights than finding main effects alone.

27.12 Practice Exercises

  1. Design Question: You want to test the effect of three fertilizer types (A, B, C) and two watering regimes (low, high) on plant growth.

    • How many treatment combinations are in this design?
    • Draw a diagram showing all treatment groups
    • Is this a balanced design if you have 10 plants per treatment?

  2. Interaction Interpretation: An experiment tests the effect of drug (present vs. absent) and exercise (yes vs. no) on weight loss. The means are:

    • No drug, no exercise: 2 kg lost

    • No drug, exercise: 5 kg lost

    • Drug, no exercise: 6 kg lost

    • Drug + exercise: 8 kg lost

    • Sketch an interaction plot

    • Is there an interaction? How do you know?

    • Interpret the results in practical terms

  3. Data Analysis: Using the built-in ToothGrowth dataset in R, which examines the effect of Vitamin C dose (0.5, 1.0, 2.0 mg) and delivery method (orange juice vs. ascorbic acid) on tooth length in guinea pigs:

    data(ToothGrowth)
    • Conduct a two-way ANOVA
    • Create an interaction plot
    • Is there a significant interaction?
    • Calculate effect sizes
    • Write a brief results paragraph

  4. Nested vs. Crossed: For each scenario, identify whether the design is nested or crossed:

      1. Students within schools, testing two teaching methods across all schools
      1. Patients within clinics, with each clinic using all three drug treatments
      1. Leaves within trees, where trees are sampled from three different forests
      1. Two temperature conditions and three light conditions, all combinations tested

  5. Repeated Measures: Design a repeated measures experiment to test whether a new drug reduces anxiety over time.

    • Specify your factors and levels
    • How many participants would you need for 80% power?
    • What would your repeated measure be?
    • What assumptions must you check?

  6. Sample Size: In a 3 × 2 factorial design:

    • How many treatment combinations exist?
    • If you want 20 observations per cell, how many total observations do you need?
    • Which effect (main or interaction) typically requires larger sample sizes to detect?

  7. Mixed Effects Application: You measure bacterial growth in 20 Petri dishes, with 5 measurements per dish across four timepoints.

    • What is the fixed effect?
    • What is the random effect?
    • Write the lmer() model formula
    • Why is a mixed effects model appropriate here?

  8. Practical Design: You have access to 4 growth chambers and want to test 3 light conditions and 2 CO₂ levels.

    • Why might a split-plot design be necessary?
    • Which factor should be the whole-plot factor? Why?
    • Sketch the design layout
    • What are the implications for statistical power?