Read multiTree files
Function to import MPT models from standard .eqn model files as used, for instance, by multiTree (Moshagen, 2010).
readEQN(file, restrictions = NULL, paramOrder = FALSE, parse = FALSE)
file |
The (full path to the) file that specifies the MPT model (standard .eqn syntax). Note that category labels must start with a letter (different to multiTree) and match the column names of |
restrictions |
Specifies which parameters should be
(a) constant (e.g., |
paramOrder |
if TRUE, the order of MPT parameters as interally used is printed. |
parse |
whether to return a parsed MPT model description in terms of the matrices a and b (the powers of the θ and (1-θ), respectively, and the vector of constants c. Each branch probability is then given as c_{i} ∏_{s} θ^{a_{i,s}}(1-θ)^{b_{i,s}}) |
The file format should adhere to the standard .eqn-syntax (note that the first line is skipped and can be used for comments). In each line, a separate branch of the MPT model is specified using the tree label, category label, and the model equations in full form (multiplication sign '*' required; not abbreviations such as 'a^2' allowed).
As an example, the standard two-high threshold model (2HTM) is defined as follows:
Target |
Hit |
Do |
||
Target |
Hit |
(1-Do)*g |
||
Target |
Miss |
(1-Do)*(1-g) |
||
Lure |
FalseAlarm |
(1-Dn)*g |
||
Lure |
CorrectReject |
(1-Dn)*(1-g) |
||
Lure |
CorrectReject |
Dn
|
Daniel Heck, Denis Arnold, Nina Arnold
Moshagen, M. (2010). multiTree: A computer program for the analysis of multinomial processing tree models. Behavior Research Methods, 42, 42-54.
# Example: Standard Two-High-Threshold Model (2HTM) EQNfile <- system.file("MPTmodels/2htm.eqn", package="TreeBUGS") readEQN(file = EQNfile, paramOrder = TRUE) # with equality constraint: readEQN(file = EQNfile, restrictions = list("Dn=Do", "g=0.5"), paramOrder = TRUE) # define MPT model directly within R model <- "2-High Threshold Model (2HTM) old hit d old hit (1-d)*g old miss (1-d)*(1-g) new fa (1-d)*g new cr (1-d)*(1-g) new cr d" readEQN(model, paramOrder=TRUE)
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