ez: ezPerm – R documentation

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ezPerm

Perform a factorial permutation test

Description

This function provides easy non-parametric permutation test analysis of data from factorial experiments, including purely within-Ss designs (a.k.a. “repeated measures”), purely between-Ss designs, and mixed within-and-between-Ss designs.

Usage

ezPerm(
    data
    , dv
    , wid
    , within = NULL
    , between = NULL
    , perms = 1e3
    , parallel = FALSE
    , alarm = FALSE
)

Arguments

`data`	Data frame containing the data to be analyzed.
`dv`	Name of the column in `data` that contains the dependent variable. Values in this column must be numeric.
`wid`	Name of the column in `data` that contains the variable specifying the case/Ss identifier.
`within`	Names of columns in `data` that contain predictor variables that are manipulated (or observed) within-Ss. If a single value, may be specified by name alone; if multiple values, must be specified as a .() list.
`between`	Names of columns in `data` that contain predictor variables that are manipulated (or observed) between-Ss. If a single value, may be specified by name alone; if multiple values, must be specified as a .() list.
`perms`	An integer `>` 0 specifying the number of permutations to compute.
`parallel`	Logical. If TRUE, computation will be parallel, assuming that a parallel backend has been specified (as in `library(doMC);options(cores=4);registerDoMC()`. Likely only to work when running R from a unix terminal.)
`alarm`	Logical. If TRUE, call the `alarm` function when `ezPerm` completes.

Value

A data frame containing the permutation test results.

Warning

ezPerm() is a work in progress. Under the current implementation, only main effects may be trusted.

Author(s)

Michael A. Lawrence mike.lwrnc@gmail.com
Visit the ez development site at http://github.com/mike-lawrence/ez
for the bug/issue tracker and the link to the mailing list.

Examples

library(plyr)
#Read in the ANT data (see ?ANT).
data(ANT)
head(ANT)
ezPrecis(ANT)

#Compute some useful statistics per cell.
cell_stats = ddply(
    .data = ANT
    , .variables = .( subnum , group , cue , flank )
    , .fun = function(x){
        #Compute error rate as percent.
        error_rate = mean(x$error)*100
        #Compute mean RT (only accurate trials).
        mean_rt = mean(x$rt[x$error==0])
        #Compute SD RT (only accurate trials).
        sd_rt = sd(x$rt[x$error==0])
        to_return = data.frame(
            error_rate = error_rate
            , mean_rt = mean_rt
            , sd_rt = sd_rt
        )
        return(to_return)
    }
)

#Compute the grand mean RT per Ss.
gmrt = ddply(
    .data = cell_stats
    , .variables = .( subnum , group )
    , .fun = function(x){
        to_return = data.frame(
            mrt = mean(x$mean_rt)
        )
        return(to_return)
    }
)

#Run a purely between-Ss permutation test on the mean_rt data.
mean_rt_perm = ezPerm(
    data = gmrt
    , dv = mrt
    , wid = subnum
    , between = group
    , perms = 1e1 #1e3 or higher is best for publication
)

#Show the Permutation test.
print(mean_rt_perm)

ez

Easy Analysis and Visualization of Factorial Experiments

v4.4-0

GPL (>= 2)

Authors

Michael A. Lawrence <mike.lwrnc@gmail.com>

Initial release

2016-11-01