quasif: Simulated data set with subjects and items requiring quasi-F...
In languageR: Analyzing Linguistic Data: A Practical Introduction to Statistics
Description
Usage
Format
Source
Examples
Description
Simulated lexical decision latencies with SOA as treatment, traditionally
requiring an analysis using quasi-F ratios, as available in
Raaijmakers et al. (1999).
Usage
1
Format
A data frame with 64 observations on the following 4 variables.
Subjecta factor coding subjects.
RTa numeric vector for simulated reaction times
in lexical decision.
Itema factor coding items.
SOAa factor coding SOA treatment with levels long
and short.
Source
Raaijmakers, J.G.W., Schrijnemakers, J.M.C. & Gremmen, F. (1999)
How to deal with "The language as fixed effect fallacy":
common misconceptions and alternative solutions,
Journal of Memory and Language, 41, 416-426.
Examples
1
2
3
4
5
## Not run:
data(quasif)
items.quasif.fnc(quasif)
## End(Not run)
languageR documentation built on May 2, 2019, 10:02 a.m.
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Description Usage Format Source Examples
Description
Simulated lexical decision latencies with SOA as treatment, traditionally requiring an analysis using quasi-F ratios, as available in Raaijmakers et al. (1999).
Usage
1 |
Format
A data frame with 64 observations on the following 4 variables.
Subjecta factor coding subjects.
RTa numeric vector for simulated reaction times in lexical decision.
Itema factor coding items.
SOAa factor coding SOA treatment with levels
longandshort.
Source
Raaijmakers, J.G.W., Schrijnemakers, J.M.C. & Gremmen, F. (1999) How to deal with "The language as fixed effect fallacy": common misconceptions and alternative solutions, Journal of Memory and Language, 41, 416-426.
Examples
1 2 3 4 5 | ## Not run:
data(quasif)
items.quasif.fnc(quasif)
## End(Not run)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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