Description Usage Arguments Value Warning Author(s) See Also Examples
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.
1 2 3 4 5 6 7 8 9 10 |
data |
Data frame containing the data to be analyzed. |
dv |
Name of the column in |
wid |
Name of the column in |
within |
Names of columns in |
between |
Names of columns in |
perms |
An integer |
parallel |
Logical. If TRUE, computation will be parallel, assuming that a parallel backend has been specified (as in |
alarm |
Logical. If TRUE, call the |
A data frame containing the permutation test results.
ezPerm()
is a work in progress. Under the current implementation, only main effects may be trusted.
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.
link{ezANOVA}
, ezBoot
, ezMixed
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | 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)
|
subnum group block trial cue flank location direction rt
1 1 Treatment 1 1 None Neutral up left 398.6773
2 1 Treatment 1 2 Center Neutral up left 389.1822
3 1 Treatment 1 3 Double Neutral up left 333.2186
4 1 Treatment 1 4 Spatial Neutral up left 419.7640
5 1 Treatment 1 5 None Congruent up left 446.4754
6 1 Treatment 1 6 Center Congruent up left 338.9766
error
1 0
2 0
3 0
4 0
5 0
6 0
Data frame dimensions: 5760 rows, 10 columns
type missing values min max
subnum factor 0 20 1 20
group factor 0 2 Control Treatment
block numeric 0 6 1 6
trial numeric 0 48 1 48
cue factor 0 4 None Spatial
flank factor 0 3 Neutral Incongruent
location factor 0 2 down up
direction factor 0 2 left right
rt numeric 0 5760 179.5972 657.6986
error numeric 0 2 0 1
| | 0%
|===== | 10% ~0 s remaining
|========== | 20% ~0 s remaining
|=============== | 30% ~0 s remaining
|==================== | 40% ~0 s remaining
|========================== | 50% ~0 s remaining
|=============================== | 60% ~0 s remaining
|==================================== | 70% ~0 s remaining
|========================================= | 80% ~0 s remaining
|============================================== | 90% ~0 s remaining
|====================================================|100% ~0 s remaining
|====================================================|100% Completed after 0 s
Effect p p<.05
1 group 0 *
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.