---
title: "What is Analysis of Frequency Data?"
bibliography: "../inst/REFERENCES.bib"
csl: "../inst/apa-6th.csl"
output: 
  rmarkdown::html_vignette
description: >
  This vignette describes what an analysis of frequency data is.
vignette: >
  %\VignetteIndexEntry{What is Analysis of Frequency Data?}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

The _ANalysis Of Frequency datA_ (ANOFA) is a framework for analyzing frequencies (a.k.a. counts) of
classification data. This framework is very similar to the well-known ANOVA and uses the same general
approach. It allows analyzing _main effects_ and _interaction effects_It also allow analyzing _simple effects_ (in case of 
interactions) as well as _orthogonal contrats_. Further, ANOFA makes it easy to generate frequency plots
which includes confidence intervals, and to compute _eta-square_ as a measure of effect size. Finally,
power planning is easy within ANOFA.

```{r, echo = FALSE, message = FALSE, results = 'hide', warning = FALSE}
cat("this will be hidden; use for general initializations.\n")
library(ANOFA)
library(ggplot2)
library(superb)
# generate some random data with no meaning
set.seed(43)
#probs  #alone                 #ingroup               #harass                #shout
pr <- c(0.4/12,1.4/12,0.5/12,  2.3/12,1.0/12,0.5/12,  0.5/12,2.0/12,0.4/12,  0.5/12,2.0/12,0.5/12)
dta <- GRF( list(Gender          = c("boy", "girl", "other"), 
                 TypeOfInterplay = c("alone", "ingroup", "harrass", "shout")
            ),
            300,
            pr
        )
```

## A basic example

As an example, suppose that you observe a class of primary school students, trying to 
ascertain the different sorts of behaviors. You might use an obsrevation grid where, 
for every kid observed, you check various things, such as

| Student Id: A   |                          |
|:----------------|-------------------------|
|**Gender:**        | Boy [**x**] Girl [ ] Other [ ]   |
|**Type of interplay:**  | Play alone [**x**] Play in group [ ] Harrass others [ ] Shout against other [ ] |
|**etc.** |

This grid categorizes the participants according to two factors, `Gender`, and `TypeOfInterplay`. 

From these observations, one may wish to know if gender is more related to one type of interplay. Alternatively, 
genders could be evenly spread across types of interplay. In the second case, there is no _interaction_ between the 
factors.

## Some data

Once collected through observations, the data can be formated in one of many ways (see the vignette 
[Data formats](https://dcousin3.github.io/ANOFA/articles/DataFormatsForFrequencies.html)). The raw format
could look like 

| Id | boy  | girl  | other | alone | in-group | harass | shout |
|----|------|-------|-------|-------|----------|--------|-------|
| A  | 1    | 0     | 0     | 1     | 0        |  0     |  0    |
| B  | 0    | 0     | 1     | 0     | 0        |  1     |  0    |
| C  | 0    | 1     | 0     | 0     | 0        |  0     |  1    |
| D  | 1    | 0     | 0     | 0     | 1        |  0     |  0    |
| ...|      |       |       |       |          |        |       |

For a more compact representation, the data could be _compiled_ into a table
with all the combination of gender $\times$ types of interplay, hence 
resulting in 12 cells. The results (totally ficticious) looks like (assuming that
they are stored in a data.frame named ``dta``):

```{r, message = FALSE, warning = FALSE}
dta
```

for a grand total of `r sum(dta$Freq)` childs observed.

## Analyzing the data

The frequencies can be analyzed using the _Analysis of Frequency Data_ (ANOFA) framework [@lc23b].
This framework only assumes that the population is _multinomial_ (which means that the population
has certain probabilities for each cell). The relevant test statistic is a $G$ statistic, whose
significance is assessed using a chi-square table.

*ANOFA* works pretty much the same as an *ANOFA* except that instead of looking at the means in each
cell, its examines the count of observations in each cell.

To run an analysis of the data frame ``dta``, simply use:

```{r, warning = FALSE}
library(ANOFA)

w <- anofa(Freq ~ Gender * TypeOfInterplay, data = dta) 
```

This is it. The formula indicates that the counts are stored in column ``Freq`` and that 
the factors are ``Gender`` and ``TypeOfInterplay``, each stored in its own column.
(if your data are organized differently, see 
[Data formats](https://dcousin3.github.io/ANOFA/articles/DataFormatsForFrequencies.html)).

At this point, you might want a plot showing the counts on the vertical axis:

```{r, message=FALSE, warning=FALSE, fig.width=4, fig.height=3, fig.cap="**Figure 1**. The frequencies of the ficticious data as a function of Gender and Type of Interplay. Error bars show difference-adjusted 95% confidence intervals."}
anofaPlot(w) 
```

We can note a strong interaction, the `ingroup` activity not being distributed the same as a 
function of ``Gender``. To confirm the interaction, let's look at the ANOFA table:

```{r}
summary(w)
```

Indeed, the interaction (last line) is significant ($G(6) = 65.92$, $p < .001$). The $G$ statistics is
corrected for small sample but the correction is typically small (as seen in the fourth column).

We might want to examine whether the frequencies of interplay are equivalent separately for each ``Gender``, 
even though examination of the plot suggest that it is only the case for the ``other`` gender. This is
achieved with an analysis of the _simple effects_ of `TypeOfInterplay` within each level of `Gender`:

```{r}
e <- emFrequencies(w, Freq ~ TypeOfInterplay | Gender)
summary(e)
```

As seen, for boys and girls, the type of interplay differ significantly (both $p < .002$); for 
``others``, as expected from the plot, this is not the case ($G(3) = 1.57$, $p = 0.46$).

If really, you need to confirm that the major difference is caused by the ``ingroup`` type of
activity (in these ficticious data), you could follow-up with a contrast analysis. We might
compare `alone` to `harass`, both to `shout`, and finally the three of them to `ingroup`.

```{r}
f <- contrastFrequencies(e, list(
    "alone vs. harass                     " = c(-1,    0, +1,    0  ),
    "(alone & harass) vs. shout           " = c(-1/2,  0, -1/2, +1  ),
    "(alone & harass & shout) vs. in-group" = c(-1/3, +1, -1/3, -1/3)
    
))
summary(f)
```

Because the contrast analysis is based on the simple effects within ``Gender`` (variable ``e``),
we get three contrasts for each gender. As seen, for boys, `in-group` is the sole condition
triggering the difference. Same for girls. Finally, there are no difference for the
last group.


## Additivity of the decomposition (optional)

The main advandage of ANOFA is that all the decomposition are entirely additive.

If, for example, you sum the $G$s and degrees of freedom of the contrasts, with e.g., 

```{r, results="hold"}
sum(summary(f)[,1])  # Gs
sum(summary(f)[,2])  # degrees of freedom
```

you get exacly the same as the simple effects:

```{r, results="hold"}
sum(summary(e)[,1])  # Gs
sum(summary(e)[,2])  # degrees of freedom
```

which is also the same as the main analysis done first, adding the main effect of ``TypeOfInterplay`` and its 
interaction with ``Gender`` (lines 3 and 4):

```{r, results="hold"}
sum(summary(w)[c(3,4),1])  # Gs
sum(summary(w)[c(3,4),2])  # degrees of freedom
```

In other words, **the decompositions preserved all the information available**. This is the 
defining characteristic of ANOFA.



# References



