08_5_rats_sol.Rmd

---
title: "Exercise 8.5: Blocking on the rat diet dataset - solution"
author: "Lieven Clement, Jeroen Gilis and Milan Malfait"
date: "statOmics, Ghent University (https://statomics.github.io)"
---

# Background

Researchers are studying the impact of protein sources and protein levels in
the diet on the weight of rats. They feed the rats with diets of beef, cereal
and pork and use a low and high protein level for each diet type.
The researchers can include 60 rats in the experiment. Prior to the experiment,
the rats were divided in 10 homogeneous groups of 6 rats based on
characteristics such as initial weight, appetite, etc.

Within each group a rat is randomly assigned to a diet. The rats are fed during
a month and the weight gain in grams is recorded for each rat.

The researchers want to assess the effect of the type of diet and the protein
level on the weight of the rats.

In this exercise we will perform the data exploration using all diets, but,
to keep the data analysis simple we will only assess the beef and cereal diets.

# Experimental design

- There are three explanatory variables in the experiment: the factor diet type
with two levels (beef and cereal), factor protein level with levels
(low and high) and a group blocking factor with 10 levels.

- There are 6 treatments: beef-high, cereal-high, pork-high, beef-low,
cereal-low, pork-low protein.

- The rats are the experimental and observational units.

- The weight gain is the response variable.

- The experiment is a randomized complete block (RCB) design

Load libraries

```{r, message=FALSE, warning=FALSE}
library(tidyverse)
```

# Data import

```{r}
diet <- read.table("https://raw.githubusercontent.com/statOmics/PSLSData/main/dietRats.txt",
  header = TRUE
)
head(diet)
```

# Tidy data

```{r}
diet <- diet %>%
  ## Convert categorical levels to factors
  mutate(
    block = as.factor(block),
    protSource = as.factor(protSource),
    protLevel = as.factor(protLevel)
  ) %>%
  ## Recode factors to make them more verbose
  mutate(
    protLevel = fct_recode(protLevel, high = "h", low = "l"),
    protSource = fct_recode(protSource, beef = "b", cereal = "c", pork = "p")
  ) %>%
  ## Set "low" as the reference level for `protLevel`
  mutate(protLevel = fct_relevel(protLevel, "low"))

head(diet)
```

# Data exploration

- Boxplot of the weight gain against protein source, protein level with coloring according to block

```{r}
diet %>%
  ggplot(aes(x = protLevel, y = weightGain)) +
  scale_fill_brewer(palette = "RdGy") +
  theme_bw() +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(aes(color = block)) +
  ggtitle("Weight gain dependence on protein source") +
  labs(x = "Protein level", y = "Weight gain (g)") +
  stat_summary(
    fun = mean, geom="point",
    shape=5, size=3, color="black"
  ) +
  facet_wrap(vars(protSource))
```

- Lineplot of the weight gain against protein source, protein level with coloring and grouping according to block

```{r}
diet %>%
  ggplot(aes(x = protLevel, y = weightGain, color = block)) +
  scale_fill_brewer(palette = "RdGy") +
  theme_bw() +
  geom_line(aes(group = block)) +
  geom_point() +
  ggtitle("Weight gain dependence on protein source") +
  ylab("Weight gain (g)") +
  facet_wrap(vars(protSource))
```

- An increase in the weight of the rats seems to depend on the protein source
received in the diet.

- The increase in the weight of the rats seems to depend on the level of protein
received in the diet

- There also seems to be an interaction effect between the protein level and the
protein source on the gain in weight of the rats. For the beef and the pork
diets the effect of high protein levels in the data seems to be much stronger
than in the cereal diet.

- There is also a strong effect of the block. Blocking implies a randomisation
restriction, hence, we will have to include the block effect anyway.

# Filter the data to only use the beef and cereal diet

```{r}
diet_bc <- diet %>% filter(protSource != "pork")
```

# Multivariate linear regression analysis

## Assumptions

List assumptions:

1. The observations are independent
2. Linearity between the response and predictor variable
3. The residuals of the model must be normally distributed
4. Homoscedasticity of the data

The first assumption is met if we correct for block in the model because the
rats were randomized to the treatment within block. The other three assumptions
can be assessed by fitting the linear model and calling the `plot()` function
as follows.

```{r}
lm1 <- lm(weightGain ~ block + protSource * protLevel, data = diet_bc)
plot(lm1)
```

All assumptions are met for this dataset.

## Hypothesis testing

We here fit a linear model with a blocking factor for `block` and main and
interaction effects for protein source and protein level.

```{r}
summary(lm1)
```

## Interpretation of the regression parameters

```{r}
ExploreModelMatrix::VisualizeDesign(diet_bc, ~ block + protSource * protLevel)$plotlist
```

There are 3 levels for protein source, 2 levels for protein level and 10 levels
for the blocking variable. We will have one reference level for each respective
variable: beef, low, block 1. So we need 2, 1 and 9 dummy variables to introduce
the factors protein source, protein level and block in the linear model,
respectively.

Hence, we can write down the linear model as follows:

$y_i=\beta_0+\beta_cx_{i,c} +\beta_hx_{i,h}+\beta_{ch}x_{i,c}x_{i,h}+\beta_{b2}x_{i,b2}+\ldots+\beta_{b10}x_{i,b10}+\epsilon_{i}$

with:
$y_i$ the observed weight gain for rat i,
$x_{i,h}$ a dummy variable which is 1 if rat i receives a high protein diet
and is 0 otherwise,
$x_{i,c}$ a dummy variable which is 1 if rat i receives a cereal diet
and is 0 otherwise,
$x_{i,bk}$ is a dummy variable which is 1 if rat i belongs to block $bk$
and is 0 otherwise, with $k \in 2,\ldots, 10$,
and $\epsilon_i$ an error term which is normally distributed with mean 0 and
variance $\sigma^2$, i.e. $\epsilon_i \sim N(0,\sigma^2)$.

- Rats that are assigned to block $k$ and receive a beef based low protein diet have
	a covariate pattern $x_{i,h}=0$, $x_{i,c}=0$, $x_{i,bm}=0$ with $m\neq k$ and $x_{i,bk}=1$.
	Their mean weight gain is thus equal to $\mu_{l,b,bk}=\beta_0+\beta_{bk}$

- Rats that are assigned to block $k$ and receive a beef based high protein diet have
	a covariate pattern $x_{i,h}=1$, $x_{i,c}=0$, $x_{i,bm}=0$ with $m\neq k$ and $x_{i,bk}=1$.
	Their mean weight gain is thus equal to $\mu_{h,b,bk}=\beta_0+\beta_h+\beta_{bk}$

- Rats that are assigned to block $k$ and receive a cereal based low protein diet have
	a covariate pattern $x_{i,h}=0$, $x_{i,c}=1$, $x_{i,bm}=0$ with $m\neq k$ and $x_{i,bk}=1$.
	Their mean weight gain is thus equal to $\mu_{h,c,bk}=\beta_0+\beta_c+\beta_{bk}$

- Rats that are assigned to block $k$ and receive a cereal based heigh protein diet have
	a covariate pattern $x_{i,h}=1$, $x_{i,c}=1$ $x_{i,bm}=0$ with $m\neq k$ and $x_{i,bk}=1$.
	Their mean weight gain is thus equal to $\mu_{h,c,bk}=\beta_0+\beta_h+\beta_c+\beta_{ch}+\beta_{bk}$

We can now relate this to the output of the `lm` function:

- The intercept $\beta_0$ is thus the average weight gain in the low beef
diet for rats in block 1.

- The parameter $\beta_c$: the average weight gain difference between cereal-low and
beef-low diet is `r lm1$coef["protSourcecereal"]`g.

- The parameter $\beta_h$: the average weight gain difference between beef-high and
beef-Low diet is `r lm1$coef["protLevelhigh"]`g.

- The parameter $\beta_{ch}$ is the difference in the average weight gain difference
due to the high protein level as compared to the low protein level for cereal diets
as compared to the weight gain difference that occurs due to the protein level
in the reference class (here beef diet). Here this is negative,
i.e. `r lm1$coef["protSourcecereal:protLevelhigh"]`g, thus the weight gain
for the cereal protein source increases on average less between high and
low protein diets than in beef based diets.

## Testing the overall (combined) effect of diet

Because there are multiple factors with different levels in the model,
we can first assess the effect of the diet (protein Level, protein source
and the interaction) by using anova. With this test we will assess the null
hypothesis that the average weight gain in each treatment is equal: i.e. $H_0: \mu_{b,l}=\mu_{b,h}=\mu_{c,h}=\mu_{c,l}$ versus
the alternative hyptohesis $H_1:$ that at least two treatment means are
different.

```{r}
lm0 <- lm(weightGain ~ block, data = diet_bc)
anova(lm0, lm1)
```

We can conclude that there is an very significant effect of the diet type
(protein source and/or protein level and/or protein source-protein level
interaction) on the weight gain of rats (p = `r format(anova(lm0, lm1)$"Pr(>F)"[2],digits =1)`).

## Assessing the interaction effect between protein source and protein level

```{r, message=FALSE, warning=FALSE}
library(car)
summary(lm1)
Anova(lm1, type = "III")
```

There is a very significant interaction between the protein source and
the protein level. This indicates that the average weight increase due to
the protein level differs according to the protein source.
Hence, we cannot assess the effect of the protein source and/or protein level
independently because there effects of the protein source vary according to
the protein level.

## Assessing specific contrasts

Imagine that we are interested in assessing if there is an effect of

1. protein source in the low protein diets
	- $\mu_{c,l}-\mu_{b,l} = \beta_c$


2. protein source in high protein diets
	- $\mu_{c,h}-\mu_{b,h}=\beta_c+\beta_{ch}$

3. protein level for beef diets ($\mu_{b,h}-\mu_{b,l}=\beta_h$), and
cereal diets  ($\mu_{c,h}-\mu_{c,l}=\beta_h+\beta_{ch}$)

4. if the effect of the protein level differs between
	- beef and cereal  $(\mu_{c,h}-\mu_{c,l}) - (\mu_{b,h}-\mu_{b,l})=\beta_{ch}$

These effects of interest are so-called
**contrasts, i.e. linear combinations of the parameters**.

We can define the contrasts and assess the significance of the contrasts with
the code below. The contrasts are given as input in the form of symbolic
descriptions to the `linfct` argument of the `glht` function.

```{r,message=FALSE, warning=FALSE}
library(multcomp)
set.seed(75468) # to get reproducible results (small effect if removed)
lm1MultComp <- glht(
  model = lm1,
  linfct = c(
    "protSourcecereal = 0",
    "protSourcecereal + protSourcecereal:protLevelhigh = 0",
    "protLevelhigh = 0",
    "protLevelhigh +  protSourcecereal:protLevelhigh = 0",
    "protSourcecereal:protLevelhigh = 0"
  )
)
```

```{r}
summary(lm1MultComp)
```

```{r}
confint(lm1MultComp)
plot(lm1MultComp)
```

Note that the p-values and the confidence intervals are automatically corrected
for multiple testing.

# Conclusion

- There is an extremely significant effect of the type of protein diet on the
weight gain of rats  (p<<1e-3).

- The average weight gain does not vary significantly according to protein
source in the diets with low protein levels (p = `r format(summary(lm1MultComp)$test$pvalues[names(summary(lm1MultComp)$test$tstat)=="protSourcecereal"],digits=2)`).

- The weight gain in the cereal diet at high protein concentration is on
average `r confint(lm1MultComp)$confint["protSourcecereal + protSourcecereal:protLevelhigh",1] %>% abs %>% round(.,1)`g lower than in the high protein beef diet (95% CI [`r confint(lm1MultComp)$confint["protSourcecereal + protSourcecereal:protLevelhigh",-1] %>% abs %>% round(.,1) %>% sort`]) and the difference is
very significant (p = format(summary(lm1MultComp)$test$pvalues[names(summary(lm1MultComp)$test$tstat)=="protSourcecereal + protSourcecereal:protLevelhigh"],digits=1)).

- We also discovered an extremely significant difference in weight gain
according to the protein level for beef based diets (p<<0.001).
The weight gain on average increases with `r round(confint(lm1MultComp)$confint["protLevelhigh",1],1)`g in the high protein
level as compared to the low protein beef diet
(95%CI [`r round(confint(lm1MultComp)$confint["protLevelhigh",-1],1)`]).
The protein level effect is not significant
for the cereal diet (`r format(summary(lm1MultComp)$test$pvalues[names(summary(lm1MultComp)$test$tstat)=="protLevelhigh + protSourcecereal:protLevelhigh"],digits=2)`).

- Finally there is a significant interaction between protein level and protein source (p = `r format(summary(lm1MultComp)$test$pvalues[names(summary(lm1MultComp)$test$tstat)=="protSourcecereal:protLevelhigh"],digits=2)`), i.e. the increase in weight gain due to protein level in beef
based diets was `r confint(lm1MultComp)$confint["protSourcecereal:protLevelhigh",1] %>% abs %>% round(.,1)`g than that in the cerial diet (95% CI [`r confint(lm1MultComp)$confint["protSourcecereal:protLevelhigh",-1] %>% abs %>% round(.,1) %>% sort`]).

All reported p-values and confidence intervals were corrected for multiple
testing