Prof Randi Garcia
February 19, 2018
We assume every observation in a similar condition is affected exactly the same. (Gets the same true score).
animals_sim <- animals %>%
mutate(benchmark = mean(calm)) %>%
group_by(animal) %>%
mutate(animal_mean = mean(calm),
aminal_effect = animal_mean - benchmark)
We add the effects as we go down the assembly line.
The interaction effect captures the possibility that conditions have non-additive effects, but it is also added to everything else.
calm_sim = benchmark
+ aminal_effect
+ cue_effect
+ interaction_effect
+ student_effect
The piece of code for adding error is not dependent on which condition the observation is in.
+ rnorm(64, 0, 0.65)
Takes 64 independent draws from a normal distribution.
+ rnorm(64, 0, 0.65)
It's rnorm()
, and not rbinom()
or rpois()
…
+ rnorm(64, 0, 0.65)
The second argument is the mean.
+ rnorm(64, 0, 0.65)
It is reasonable to assume that the structure of a sugar molecule has something to do with its food value. An experiment was conducted to compare the effects of four sugar diets on the survival of leafhoppers. The four diets were glucose and fructose (6-carbon atoms), sucrose (12-carbon), and a control (2% agar). The experimenter prepared two dishes with each diet, divided the leafhoppers into eight groups of equal size, and then randomly assigned them to dishes. Then she counted the number of days until half the insects had died.
control | sucrose | glucose | fructose |
---|---|---|---|
2.3 | 3.6 | 3.0 | 2.1 |
1.7 | 4.0 | 2.8 | 2.3 |
X. | control | sucrose | glucose | fructose |
---|---|---|---|---|
2.3 | 3.6 | 3.0 | 2.1 | |
1.7 | 4.0 | 2.8 | 2.3 | |
means | 2.0 | 2.2 | 2.9 | 3.8 |
Formal ANOVA starts with the simple idea that we can compare our estimate of treatment effect variability to our estimate of chance error variability to measure how large our treatment effect is.
Variability in treatment effects = True Effect Differences + Error
Variability in residuals = Error
Variability in treatment effects/Variability in residuals
ANOVA measures variability in treatment effects with the sum of squares (SS) divided by the number of units of unique information (df). For the BF[1] design,
\[ {SS}_{Treatments} = n\sum_{i=1}^{a}(\bar{y}_{i.}-\bar{y}_{..})^{2} \]
\[ {SS}_{E} = \sum_{i=1}^{a}\sum_{j=1}^{n}({y}_{ij}-\bar{y}_{i.})^{2} \]
\[ {SS}_{Total} = {SS}_{Treatments} + {SS}_{E} \]
where \( n \) is the group size, and \( a \) is the number of treatments.
The df for a table equals the number of free numbers, the number of slots in the table you can fill in before the pattern of repetitions and adding to zero tell you what the remaining numbers have to be.
\[ {df}_{Treatments}=a-1 \]
\[ {df}_{E}=N-a \]
The ultimate statistic we want to calculate is Variability in treatment effects/Variability in residuals.
Variability in treatment effects: \[ {MS}_{Treatments}=\frac{{SS}_{Treatments}}{{df}_{Treatments}} \]
Variability in residuals \[ {MS}_{E}=\frac{{SS}_{E}}{{df}_{E}} \]
The ratio of these two MS's is called the F ratio. The following quantity is our test statistic for the null hypothesis that there are no treatment effects.
\[ F = \frac{{MS}_{Treatments}}{{MS}_{E}} \]
If the null hypothesis is true, then F is a random variable \( \sim F({df}_{Treatments}, {df}_{E}) \). The F-distribution.
qplot(x = rf(500, 3, 4), geom = "density")
We can find the p-value for our F calculation with the following code
pf(17.67, 3, 4, lower.tail = FALSE)
\[ Effect = Average - Partial Fit \]
student | animal | cute | scary |
---|---|---|---|
2 | cat | 5 | 1 |
5 | cat | 5 | 5 |
1 | dog | 5 | 1 |
3 | dog | 4 | 2 |