ldknn: Run the ldknn algorithm
In vwr: Useful functions for visual word recognition research

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

The ldknn algorithm is used to detect bias in the composition of a lexical decison task, using k-nearest neighbor classification and the Levenshtein distance metric.

1	ldknn(stimuli, types, reference, k = 1, method='levenshtein', parallel = FALSE)

`stimuli`	character strings corresponding to the stimuli in the experiment.
`types`	factor corresponding to the type of each stimulus in the experiment.
`reference`	a character string giving the reference level. Must be a level of the factor in `types`
`k`	a value for the k parameter. Set to 1 by default.
`method`	"levenshtein": uses `levenshtein.distance to calculate distances` 'levenshtein.damerau': uses `levenshtein.damerau.distance` to calculate distances
`parallel`	with parallel=TRUE, `ldknn` will run in parallel an multiple cores. The number of parallel processes is specified by `detectCores(logical = FALSE)`.

Combining k nearest neighbor classification with the Levenshtein distance produces an algorithm which can be described as follows. For an experiment containing a number of stimuli, which can be words or nonwords:

Compute the Levenshtein distances between the currently presented stimulus and all previously presented stimuli.
Identify the previously presented stimuli that are at the k nearest distances from the current stimulus.
Compute the probability of a word response for the given stimulus based on the relative frequency of words among the nearest neighbors.

A list with class ldknn.run.

`data`	A data frame containing the results of the run. `stimulus` gives the stimulus values, `type` gives the types of the stimuli, `p` gives the probability for a `reference.level` response for that stimulus.
`reference level`	The reference level used for the simulation.
`Odds`	The odds, z value, and p value for a reference level response, resulting from a logistic regression in which the probabilities generated by the ldknn algorithm are used to predict stimulus types.

plot and print methods are available for objects of class ld1nn.run

Emmanuel Keuleers

Keuleers, E., & Brysbaert, M. (2011). Detecting inherent bias in lexical decision experiments with the LD1NN algorithm. The Mental Lexicon, 6(1), 34<e2><80><93>52.

levenshtein.distance, levenshtein.damerau.distance

data(english.words)
data(basque.words)
# set up a mock experiment: English stimuli are words, Basque stimuli are nonwords
experiment<-data.frame(stimulus=c(sample(english.words,500),
 sample(basque.words,500)),
 type=factor(rep(c('Word','Nonword'),each=500),levels=c('Word','Nonword')))
# randomize the trials
experiment<-experiment[sample(1:1000,1000),]
# run the ldknn algorithm
results<-ldknn(experiment$stimulus,experiment$type,'Word')
print(results)
plot(results)