Research Question &
Response Variable
What is your research question and your response variable? Give a
detailed answer.
Factors, Levels, and
Design Choice
List your factors and their levels. What is your design choice?
Discuss any constraints or restrictions on randomization. Report your
design table which includes run randomization. Discuss how you should
employ randomization and blocking in this experiment.
Analysis Results
Write a comprehensive paragraph on the results of your statistical
analysis. Use APA style. Refer to the interaction plots if
necessary.
Response : response
Df Sum Sq Mean Sq F value Pr(>F)
MODEL 15 36.027 2.4018 8.0018 4.860e-07 ***
POPM 1 12.727 12.7273 42.4018 2.489e-07 ***
PTF 1 3.592 3.5923 11.9680 0.0015537 **
POPM:PTF 1 12.286 12.2862 40.9322 3.448e-07 ***
TM 1 0.167 0.1671 0.5568 0.4610065
POPM:TM 1 0.033 0.0329 0.1097 0.7426348
PTF:TM 1 0.073 0.0730 0.2432 0.6252730
POPM:PTF:TM 1 0.189 0.1890 0.6298 0.4332827
PEP 1 1.056 1.0562 3.5187 0.0698296 .
POPM:PEP 1 0.503 0.5027 1.6748 0.2048826
PTF:PEP 1 0.004 0.0042 0.0140 0.9066870
POPM:PTF:PEP 1 0.224 0.2236 0.7451 0.3944625
TM:PEP 1 0.007 0.0073 0.0243 0.8770860
POPM:TM:PEP 1 4.837 4.8370 16.1148 0.0003364 ***
PTF:TM:PEP 1 0.214 0.2141 0.7133 0.4046114
POPM:PTF:TM:PEP 1 0.114 0.1141 0.3802 0.5418589
RESIDUALS 32 9.605 0.3002
CORRECTED TOTAL 47 45.632
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1



Power
Calculate the post-hoc power for this design. Use the effect size
from your output.
The four-way interaction effect size is eta2=0.109001.
Degrees of freedom for the power calculation is df=1.
Residual Analysis
Are your assumptions met? Use the plots to support your argument.


Factorial
Regularities
Do the results of your factorial experiment display sparsity,
heredity, and hierarchy? Support your answer with your results.
Limitations & What
You’d Do Next
Discuss issues you see with this design. Do you have issues with
Confounding effects? Are there design weaknesses? Give follow up
experiment ideas.
---
title: "STA320 Final Exam Team 2"
author: "Team 4: first names here"
date: "`r Sys.Date()`"
output:
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body { background-color:white; }

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p { background-color:white; }

}
```


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = F, comment=NA, warning=F)

# Setup and Design Definition

# Load required libraries
library(tidyverse)
library(effects)    # For interaction plots
library(sasLM)
library(FrF2)
library(dplyr)
library(effectsize)
library(kableExtra)

set.seed(123) # Reproducibility
```



# Research Question & Response Variable

What is your research question and your response variable? Give a detailed answer.








# Factors, Levels, and Design Choice

List your factors and their levels. What is your design choice? Discuss any constraints or restrictions on randomization. Report your design table which includes run randomization. Discuss how you should employ randomization and blocking in this experiment. 

```{r design, include=F}

############################################################
# Define Factors and Levels
############################################################

# Example: 2^4 factorial with optional blocking

base_design <- expand.grid(
  POPM = c("Low", "High"),
  PTF = c("Low", "High"),
  TM = c("Low", "High"),
  PEP = c("Low", "High")
)

#Create blocking variable on replication

design <- base_design[rep(1:nrow(base_design), times = 3), ]

design$Block <- rep(paste0("Block", 1:3), each = nrow(base_design))

#randomization within block

design <- design %>%
  group_by(Block) %>%
  mutate(RunOrder = sample(1:n())) %>%
  ungroup()

design <- design %>%
  arrange(Block, RunOrder) %>%
  mutate(GlobalRun = row_number())


design %>%
  kbl(caption="2^4 Unreplicated Factorial-Randomization Schedule", align="c") %>%
  kable_classic(full_width=F) %>%
  column_spec(5, width="3cm")
```


``` {r simulation data, include=F}

# Simulate Response Data
# Define true effects
mu = 6.4
effect_A = .5
effect_B = -.3
effect_C = 0
effect_D = -.1
interaction_AB = .6
interaction_ACD = -.2

# Convert factors to indicators
sim_data = design %>%
  mutate(
    A = ifelse(POPM == "High", 1, -1),
    B = ifelse(PTF == "High", 1, -1),
    C = ifelse(TM == "High", 1, -1),
    D = ifelse(PEP == "High", 1, -1)   
  )

# Generate response
sim_data$response = mu +
  effect_A * sim_data$A +
  effect_B * sim_data$B +
  effect_C * sim_data$C +
  effect_D * sim_data$D + 
  interaction_AB * sim_data$A * sim_data$B +
  interaction_ACD * sim_data$A * sim_data$C * sim_data$D +
  rnorm(nrow(sim_data), mean = 0, sd = .5)

```


# Analysis Results

Write a comprehensive paragraph on the results of your statistical analysis. Use APA style. Refer to the interaction plots if necessary.

```{r ANOVA, include=T}

# Factorial ANOVA 
aov1(response ~ POPM * PTF * TM * PEP, sim_data)

model=lm(response ~ POPM * PTF * TM * PEP, data=sim_data)
aov=aov(model)

```


```{r plots, include=T}

# Interaction Plots

par(mfrow=c(1,2))

# Base R interaction plot
#AB
interaction.plot(
  x.factor = sim_data$POPM,
  trace.factor = sim_data$PTF,
  response = sim_data$response,
  main = "A × B",
  type = "b",
  col = c("blue", "red"),
  pch = c(19, 17)
)
#AC
interaction.plot(
  x.factor = sim_data$POPM,
  trace.factor = sim_data$TM,
  response = sim_data$response,
    main = "A × C",
  type = "b",
  col = c("blue", "red"),
  pch = c(19, 17)
)
#AD
interaction.plot(
  x.factor = sim_data$POPM,
  trace.factor = sim_data$PEP,
  response = sim_data$response,
    main = "A × D",
  type = "b",
  col = c("blue", "red"),
  pch = c(19, 17)
)

#BC
interaction.plot(
  x.factor = sim_data$PTF,
  trace.factor = sim_data$TM,
  response = sim_data$response,
    main = "B × C",
  type = "b",
  col = c("blue", "red"),
  pch = c(19, 17)
)

#BD
interaction.plot(
  x.factor = sim_data$PTF,
  trace.factor = sim_data$PEP,
  response = sim_data$response,
    main = "B × D",
  type = "b",
  col = c("blue", "red"),
  pch = c(19, 17)
)

#CD
interaction.plot(
  x.factor = sim_data$TM,
  trace.factor = sim_data$PEP,
  response = sim_data$response,
    main = "C × D",
  type = "b",
  col = c("blue", "red"),
  pch = c(19, 17)
)

```




``` {r power}
#Effect size from ANOVA needed for power
effsize=eta_squared(aov)
#four.way.interaction.effect=effsize[15,2]
#Convert eta squared to Cohen's f for post hoc power calc
E=eta2_to_f(effsize[15,2])

```
# Power 

Calculate the post-hoc power for this design. Use the effect size from your output. 

The four-way interaction effect size is eta^2^=`r E`. Degrees of freedom for the power calculation is df=1. 




# Residual Analysis

Are your assumptions met? Use the plots to support your argument. 

```{r assumptions}

hist(model$residuals, main="Residual Histogram")

design$Block <- factor(design$Block)
plot(
  design$GlobalRun,
  model$residuals,
  col = as.numeric(design$Block),
  pch = 19,
  xlab = "Run Order",
  ylab = "Residuals",
  main = "Residuals vs Run Order (Colored by Block)"
)
abline(h = 0, lty = 2)

```



# Factorial Regularities

Do the results of your factorial experiment display sparsity, heredity, and hierarchy? Support your answer with your results.

# Limitations & What You’d Do Next

Discuss issues you see with this design. Do you have issues with Confounding effects? Are there design weaknesses? Give follow up experiment ideas.