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https://github.com/dcousin3/anofa

Analysis of Frequency Data with ANOFA
https://github.com/dcousin3/anofa

frequencies r statistics

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Analysis of Frequency Data with ANOFA

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README 

        
---

output: github_document

bibliography: "inst/REFERENCES.bib"

csl: "inst/apa-6th.csl"

---



# ANOFA: Analyses of Frequency Data



[![CRAN Status](https://www.r-pkg.org/badges/version/ANOFA)](https://cran.r-project.org/package=ANOFA)



```{r, echo = FALSE, message = FALSE, results = 'hide', warning = FALSE}

cat("this will be hidden; used for general initializations.\n")

library(ANOFA)

options("ANOFA.feedback" = "none") # shut down all information

```



The library `ANOFA` provides easy-to-use tools to analyze frequency data. 

It does so using the _Analysis of Frequency datA_ (ANOFA) framework 

[the full reference @lc23b]. With this set of tools, you can examined

if classification factors are non-equal (_have an effect_) and if their

interactions (in case you have more than 1 factor) are significant. You

can also examine simple effects (a.k.a. _expected marginal_ analyses). 

Finally, you can assess differences based on orthogonal contrasts.

ANOFA also comes with tools to make a plot of the frequencies along

with 95% confidence intervals [these intervals are adjusted for pair-

wise comparisons @cgh21]; with tools to compute statistical power given

some _a priori_ expected frequencies or sample size to reach a certain

statistical power. In sum, eveything you need to analyse frequencies!



The main function is `anofa()` which provide an omnibus analysis of the 

frequencies for the factors given. For example, @lm71 explore frequencies

for attending a certain type of higher education as a function of gender:



```{r, message=FALSE, warning=FALSE, echo=TRUE, eval=TRUE}

w <- anofa( obsfreq ~ vocation * gender, LightMargolin1971)

summary(w)

```



A plot of the frequencies can be obtained easily with 



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

anofaPlot(w) 

```



Owing to the interaction, simple effects can be analyzed from the _expected marginal

frequencies_ with



```{r, message=FALSE, warning=FALSE, echo=TRUE, eval=TRUE}

e <- emFrequencies(w, ~ gender | vocation )

summary(e)

```



Follow-up functions includes contrasts examinations with `contrastFrequencies()'.



Power planning can be performed on frequencies using ``anofaPower2N()`` or

``anofaN2Power`` if you can determine theoretical frequencies.



Finally, `toRaw()`, `toCompiled()`, `toTabulated()`, `toLong()` and `toWide()` 

can be used to present the frequency data in other formats.



# Installation



Note that the package is named using UPPERCASE letters whereas the main function is in lowercase letters.



The official **CRAN** version can be installed with 



```{r, echo = TRUE, eval = FALSE}

install.packages("ANOFA")

library(ANOFA)

```



The development version `r packageVersion("ANOFA")` can be accessed through GitHub:



```{r, echo = TRUE, eval = FALSE}

devtools::install_github("dcousin3/ANOFA")

library(ANOFA)

```



The library is loaded with 



```{r, echo = TRUE, eval = FALSE, results = FALSE}

library(ANOFA)

```



# For more



As seen, the library `ANOFA` makes it easy to analyze frequency data.

Its general philosophy is that of ANOFAs.



The complete documentation is available on this 

[site](https://dcousin3.github.io/ANOFA/).



A general introduction to the `ANOFA` framework underlying this 

library can be found at *the Quantitative Methods for Psychology* @lc23b.



# References



\insertAllCited{}