primingHeid: Primed lexical decision latencies for neologisms ending in...
In languageR: Analyzing Linguistic Data: A Practical Introduction to Statistics

Description Usage Format References Examples

Primed lexical decision latencies for Dutch neologisms ending in the suffix -heid.

1	data(primingHeid)

A data frame with 832 observations on the following 13 variables.

Subject: a factor with subjects as levels.
Word: a factor with words as levels.
Trial: a numeric vector for the rank of the trial in its experimental list.
RT: a numeric vector with log-transformed lexical decision latencies.
Condition: a factor coding the priming treatmen, with levels baseheid (prime is the base word) and heid (the prime is the neologism)
Rating: a numeric vector for subjective frequency estimates.
Frequency: a numeric vector for log-transformed frequencies of the whole word.
BaseFrequency: a numeric vector for the log-transformed frequencies of the base word.
LengthInLetters: a numeric vector coding orthographic length in letters.
FamilySize: a numeric vector for the log-transformed count of the word's morphological family.
NumberOfSynsets: a numeric vector for the number of synonym sets in WordNet in which the base is listed.
ResponseToPrime: a factor with levels correct and incorrect for the response to the prime.
RTtoPrime: a numeric vector for the log-transformed reaction time to the prime.

De Vaan, L., Schreuder, R. and Baayen, R. H. (2007) Regular morphologically complex neologisms leave detectable traces in the mental lexicon, The Mental Lexicon, 2, in press.

## Not run: 
data(primingHeid)

require(lme4)
require(lmerTest)
require(optimx)

primingHeid.lmer = lmer(RT ~ RTtoPrime * ResponseToPrime + Condition +
  (1|Subject) + (1|Word), 
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
  data = primingHeid)
summary(primingHeid.lmer)

# model criticism

primingHeid.lmer = lmer(RT ~ RTtoPrime * ResponseToPrime + Condition +
  (1|Subject) + (1|Word), 
  control=lmerControl(optimizer="optimx",optCtrl=list(method="nlminb")),
  data = primingHeid[abs(scale(resid(primingHeid.lmer)))<2.5,])
summary(primingHeid.lmer)

## End(Not run)