fanova: Flexible anova
In userfriendlyscience: Quantitative Analysis Made Accessible

Description Usage Arguments Details Value Author(s) See Also Examples

This function is meant as a userfriendly wrapper to approximate the way analysis of variance is done in SPSS.

fanova(data,
       y,
       between = NULL,
       covar = NULL,
       plot = FALSE,
       levene = FALSE,
       digits = 2,
       contrast = NULL)

`data`	The dataset containing the variables to analyse.
`y`	The dependent variable. For oneway anova, factorial anova, or ancova, this is the name of a variable in dataframe `data`. For repeated measures anova, this is a vector with the names of all variable names in dataframe `data`, e.g. `c('t0_value', 't1_value', 't2_value')`.
`between`	A vector with the variables name(s) of the between subjects factor(s).
`covar`	A vector with the variables name(s) of the covariate(s).
`plot`	Whether to produce a plot. Note that a plot is only produced for oneway and twoway anova and oneway repeated measures designs: if covariates or more than two between-subjects factors are specified, not plot is produced. For twoway anova designs, the second predictor is plotted as moderator (and the first predictor is plotted on the x axis).
`levene`	Whether to show Levene's test for equality of variances (using `car`'s `leveneTest` function but specifying `mean` as function to compute the center of each group).
`digits`	Number of digits (actually: decimals) to use when printing results. The p-value is printed with one extra digit.
`contrast`	This functionality has not been implemented yet.

This wrapper uses oneway and lm and lmer in combination with car's Anova function to conduct the analysis of variance.

Mainly, this function prints its results, but it also returns them in an object containing three lists:

`input`	The arguments specified when calling the function
`intermediate`	Intermediat objects and values
`output`	The results such as the plot.

Gjalt-Jorn Peters

Maintainer: Gjalt-Jorn Peters <gjalt-jorn@userfriendlyscience.com>

regr and logRegr for similar functions for linear and logistic regression and oneway, lm, lmer and Anova for the functions used behind the scenes.

### Oneway anova with a plot
fanova(dat=mtcars, y='mpg', between='cyl', plot=TRUE);

### Factorial anova
fanova(dat=mtcars, y='mpg', between=c('vs', 'am'), plot=TRUE);

### Ancova
fanova(dat=mtcars, y='mpg', between=c('vs', 'am'), covar='hp');

### Repeated measures anova; first generate datafile
dat <- mtcars[, c('am', 'drat', 'wt')];
names(dat) <- c('factor', 't0_dependentVar' ,'t1_dependentVar');
dat$factor <- factor(dat$factor);

### Then do the repeated measures anova
fanova(dat, y=c('t0_dependentVar' ,'t1_dependentVar'),
       between='factor', plot=TRUE);