This generic function fits binary multinomial processing tree models (MPT models; e.g., Riefer & Batchelder, 1988) from an external model file and (optional) external restrictions. Additionally, measures for model selection (AIC, BIC, FIA) can be computed.
1 2 3 4 5 6  ## S4 method for signature 'character'
fit.mpt(model, data, restrictions.filename = NULL, model.type = c("easy", "eqn", "eqn2"), start.parameters = NULL, ...)
## S4 method for signature 'bmpt.model'
fit.mpt(model, data, ci = 95, n.optim = list("auto", 5), start.parameters = NULL, ...)
## S4 method for signature 'mpt.model'
fit.mpt(model, data, n.optim = 5, ci = 95, start.parameters = NULL, method = c("LBFGSB", "nlminb"), multicore = c("none", "individual", "n.optim"), sfInit = FALSE, nCPU = 2, ...)

model 
Either a 
data 
Either a numeric 
restrictions.filename 

model.type 
Character vector specifying whether the model file is formatted in the easy way ( 
start.parameters 
A 
ci 
A scalar corresponding to the size of the default confidence intervals for the parameter estimates. Default is 95 which corresponds to 95% confidence intervals. See also 
n.optim 
List or numeric. Number of optimization runs. See Details 
method 
character. Only relevant for models not members of LBMPT (see 
multicore 
Character vector. If not 
sfInit 
Logical. Relevant if 
nCPU 
Scalar. Only relevant if 
... 
Used to pass arguments from the method for 
For details on model.filename
, restrictions.filename
, or model.type
, see make.mpt
.
When calling fit.mpt
with a filename as the model
argument, make.mpt
is called to create a model object (see bmpt.modelclass
). For models of class bmpt.model
the fast Fortran routine implementing an EMalgorithm is used for fitting and the results are returned in an object of class bmpt
. For models of class mpt.model
the fitting routine that was already implemented in the first version of MPTinR using optim
's LBFGSB is used (this version of MPTinR allows to use nlminb
as an alternative to LBFGSB) an don object of class mpt
is returned. Note that most of the advanced features of MPTinR, such as the FIA and parametric and nonparametric bootstrapped CIs (all methods currently not implemented), are only available for models that are members of LBMPT (Purdy & Batchelder, 2009; i.e., of type bmpt.model
).
The index of each datapoint (or the column for matrices) must correspond to the row or number of this response catgeory in the model file.
start.parameters
are used as the initial values when fitting the model.
For models of class bmpt.model
the start.parameters
argument must be either NULL
or a numeric vector of at least length = number of free parameters. If length(start.parameters)
is larger than the number of free parameters it is truncated. The start.parameters
are mapped on the free parameters based on the order (see check(model)[["free.parameters"]]
where model can be either a model object or fitted object).
For models of class mpt.model
multiple cores can be used for fitting via the multicore
argument. Multicore fitting is achieved via the snowfall
package and needs to be initialized via sfInit
. As initialization needs some time, you can either initialize multicore facilities yourself using sfInit()
and setting the sfInit
argument to FALSE
(the default) or let MPTinR initialize multicore facilities by setting the sfInit
argument to TRUE
. The former is recommended as initializing snowfall
takes some time and only needs to be done once if you run fit.mpt
multiple times. If there are any problems with multicore fitting, first try to initialize snowfall
outside MPTinR (e.g., sfInit( parallel=TRUE, cpus=2 )
). If this does not work, the problem is not related to MPTinR but to snowfall (for support and references visit: http://www.imbi.unifreiburg.de/parallel/).
When using multicore facilties set argument nCPU
to the desired (and available) number of CPUs.
Note that you should close snowfall via sfStop()
after using MPTinR.
An object of class bmpt
or mpt
. See bmptclass
for corresponding methods.
Whenever possible try to write your model as a member of LBMPT (Purdy & Batchelder, 2009) to use the full functionality of MPTinR.
Henrik Singmann and David Kellen.
The Fortran code was written by Karl Christoph Klauer
Baldi, P. & Batchelder, W. H. (2003). Bounds on variances of estimators for multinomial processing tree models. Journal of Mathematical Psychology, 47, 467470.
Byrd, R. H., Lu, P., Nocedal, J., & Zhu, C. (1995). A limited memory algorithm for bound constrained optimization. SIAM J. Scientific Computing, 16, 11901208.
Knapp, B. R., & Batchelder, W. H. (2004). Representing parametric order constraints in multitrial applications of multinomial processing tree models. Journal of Mathematical Psychology, 48, 215229.
Moshagen, M. (2010). multiTree: A computer program for the analysis of multinomial processing tree models. Behavior Research Methods, 42, 4254.
Purdy, B. P., & Batchelder, W. H. (2009). A contextfree language for binary multinomial processing tree models. Journal of Mathematical Psychology, 53, 547561.
Riefer, D. M., & Batchelder, W. H. (1988). Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318339.
Stahl, C. & Klauer, K. C. (2007). HMMTree: A computer program for latentclass hierarchical multinomial processing tree models. Behavior Research Methods, 39, 267 273.
Wu, H., Myung, J.I., & Batchelder, W.H. (2010a). Minimum description length model selection of multinomial processing tree models. Psychonomic Bulletin & Review, 17, 275286.
Wu, H., Myung, J.I., & Batchelder, W.H. (2010b). On the minimum description length complexity of multinomial processing trees. Journal of Mathematical Psychology, 54, 291303.
make.mpt
for the function to create the model objects and bmpt.modelclass
for their methods.
bmptclass
for methods of objects returned by this function.
http://www.psychologie.unifreiburg.de/Members/singmann/R/mptinr for additional information on model files and restriction files
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 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118  ## Not run:
# The first example fits the MPT model presented in Riefer and Batchelder (1988, Figure 1)
# to the data presented in Riefer and Batchelder (1988, Table 1)
# Note that Riefer and Batchelder (1988, pp. 328) did some hypotheses tests, that are not done here.
# Rather, we use each condition (i.e., row in Table 1) as a different individual.
# load the data
data(rb.fig1.data, package = "MPTinR2")
#make model objects, once using the easy format, once using the eqn format.
(model1 < make.mpt(system.file("extdata", "rb.fig1.model", package = "MPTinR2")))
(model1.eqn < make.mpt(system.file("extdata", "rb.fig1.model.eqn", package = "MPTinR2")))
#both models are identical:
identical(model1, model1.eqn)
# specify the same model via textConnection
model1.txtCon < make.mpt(textConnection("p * q * r
p * q * (1r)
p * (1q) * r
p * (1q) * (1r) + (1p)"))
identical(model1, model1.txtCon)
# see ?make.mpt for more on how to specify a model and restrictions
# just fit the first "individual":
fit.mpt(model1, rb.fig1.data[1,])
#fit all "individuals":
fit.mpt(model1, rb.fig1.data)
#fit all "individuals" using the .EQN model file:
fit.mpt(model1.eqn, rb.fig1.data)
#fit all "individuals" using the .txtCon model file:
fit.mpt(model1.txtCon, rb.fig1.data)
# The second example fits the MPT model presented in Riefer and Batchelder (1988, Figure 2)
# to the data presented in Riefer and Batchelder (1988, Table 3)
# First, the model without restrictions is fitted: ref.model
# Next, the model with all r set equal is fitted: r.equal
# Then, the model with all c set equal is fitted: c.equal
# Finally, the inferential tests reported by Riefer & Batchelder, (1988, p. 332) are executed.
# get the data
data(rb.fig2.data, package = "MPTinR2")
# make model objects
model2.file < system.file("extdata", "rb.fig2.model", package = "MPTinR2")
model2 < make.mpt(model2.file)
model2.r.eq < make.mpt(model2.file, system.file("extdata", "rb.fig2.r.equal", package = "MPTinR2"))
model2.c.eq < make.mpt(model2.file, system.file("extdata", "rb.fig2.c.equal", package = "MPTinR2"))
# The full (i.e., unconstrained) model
(ref.model < fit.mpt(model2, rb.fig2.data))
# All r equal
(r.equal < fit.mpt(model2.r.eq, rb.fig2.data))
# All c equal
(c.equal < fit.mpt(model2.c.eq, rb.fig2.data))
# is setting all r equal a good idea?
(g.sq.r.equal < goodness.of.fit(r.equal)[["G.Squared"]]  goodness.of.fit(ref.model)[["G.Squared"]])
(df.r.equal < goodness.of.fit(r.equal)[["df.model"]]  goodness.of.fit(ref.model)[["df.model"]])
(p.value.r.equal < pchisq(g.sq.r.equal, df.r.equal , lower.tail = FALSE))
# is setting all c equal a good idea?
(g.sq.c.equal < goodness.of.fit(c.equal)[["G.Squared"]]  goodness.of.fit(ref.model)[["G.Squared"]])
(df.c.equal < goodness.of.fit(c.equal)[["df.model"]]  goodness.of.fit(ref.model)[["df.model"]])
(p.value.c.equal < pchisq(g.sq.c.equal, df.c.equal , lower.tail = FALSE))
# Example from Broeder & Schuetz (2009)
# We fit the data from the 40 individuals from their Experiment 3
# We fit three different models:
# 1. Their 2HTM model: br.2htm
# 2. A restricted 2HTM model with Dn = Do: br.2htm.res
# 3. A 1HTM model (i.e., Dn = 0): br.1htm
# We fit the models with, as well as without, applied inequality restrictions (see Details)
# that is, for some models (.ineq) we impose: G1 < G2 < G3 < G4 < G5
# As will be apparent, the inequality restrictions do not hold for all individuals.
data(d.broeder, package = "MPTinR2")
m.2htm < system.file("extdata", "5points.2htm.model", package = "MPTinR2")
r.2htm < system.file("extdata", "broeder.2htm.restr", package = "MPTinR2")
r.1htm < system.file("extdata", "broeder.1htm.restr", package = "MPTinR2")
i.2htm < system.file("extdata", "broeder.2htm.ineq", package = "MPTinR2")
ir.2htm < system.file("extdata", "broeder.2htm.restr.ineq", package = "MPTinR2")
ir.1htm < system.file("extdata", "broeder.1htm.restr.ineq", package = "MPTinR2")
# fit the original 2HTM
br.2htm < fit.mpt(m.2htm, d.broeder)
br.2htm.ineq < fit.mpt(m.2htm, d.broeder, i.2htm)
# do the inequalities hold for all participants?
parameters(br.2htm.ineq, sort.alphabetical = TRUE)[["individual"]][,"estimates",]
parameters(br.2htm)[["individual"]][,"estimates",]
# See the difference between forced and nonforced inequality restrictions:
round(parameters(br.2htm)[["individual"]][,"estimates",]
parameters(br.2htm.ineq, sort.alphabetical = TRUE)[["individual"]][,"estimates",],2)
# The same for the other two models
# The restricted 2HTM
br.2htm.res < fit.mpt(m.2htm, d.broeder, r.2htm)
br.2htm.res.ineq < fit.mpt(m.2htm, d.broeder, ir.2htm)
round(parameters(br.2htm.res, sort.alphabetical = TRUE)[["individual"]][,"estimates",]
parameters(br.2htm.res.ineq, sort.alphabetical = TRUE)[["individual"]][,"estimates",],2)
# The 1HTM
br.1htm < fit.mpt(m.2htm, d.broeder, r.1htm)
br.1htm.ineq < fit.mpt(m.2htm, d.broeder, ir.1htm)
round(parameters(br.1htm, sort.alphabetical = TRUE)[["individual"]][,"estimates",]
parameters(br.1htm.ineq, sort.alphabetical = TRUE)[["individual"]][,"estimates",],2)
## End(Not run)

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