Description Usage Arguments Details Value Note Note Author(s) References See Also Examples
fit.mpt
fits binary multinomial processing tree models (MPT models; e.g., Riefer & Batchelder, 1988) from an external model file and (optional) external restrictions using the generalpurpose quasiNewton boxconstraint optimization routine provided by Byrd et al. (1995). Additionally, measures for model selection (AIC, BIC, FIA) can be computed.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  fit.mpt(
data,
model.filename,
restrictions.filename = NULL,
n.optim = 5,
fia = NULL,
ci = 95,
starting.values = NULL,
output = c("standard", "fia", "full"),
reparam.ineq = TRUE,
fit.aggregated = TRUE,
sort.param = TRUE,
show.messages = TRUE,
model.type = c("easy", "eqn", "eqn2"),
multicore = c("none", "individual", "n.optim", "fia"), sfInit = FALSE, nCPU = 2,
control = list(), args.fia = list(), numDeriv = TRUE
)

data 
Either a numeric 
model.filename 
A character 
restrictions.filename 

n.optim 
Number of optimization runs. Can be parallelized via 
fia 
Number of random samples to be drawn in the Monte Carlo algorithm to estimate the Fisher Information Approximation (FIA), a minimum description length based measure of model complexity (see Wu, Myung & Batchelder, 2010). The default is 
ci 
A scalar corresponding to the size of the confidence intervals for the parameter estimates. Default is 95 which corresponds to 95% confidence intervals. 
starting.values 
A 
output 
If "fia", 
reparam.ineq 
Logical. Indicates whether or not inequality restrictions (when present in the model file) should be enforced while fitting the model. If 
fit.aggregated 
Logical. Only relevant for multiple datasets (i.e., 
sort.param 
Logical. If TRUE, parameters are alphabetically sorted in the parameter table. If FALSE, the first parameters in the parameter table are the nonrestricted ones, followed by the restricted parameters. Default is TRUE. 
show.messages 
Logical. If TRUE the time the fitting algorithms takes is printed to the console. 
model.type 
Character vector specifying whether the model file is formatted in the easy way ( 
multicore 
Character vector. If not 
sfInit 
Logical. Relevant if 
nCPU 
Scalar. Only relevant if 
control 
list containing control arguments passed on to 
args.fia 
named list of further arguments passed to 
numDeriv 
logical. Should the Hessian matrix of the maximum likelihood estimates be estimated numerically using 
The model file is either of the easy format (see http://www.psychologie.unifreiburg.de/Members/singmann/R/mptinr) or the "classical" EQN format (see below).
In the easy format (the default) the model file contains all trees of the model. Trees are separated by at least one empty line. Everything to the right of a hash (#) is ignored (this behavior is new since version 0.9.2). Lines starting with a # are treated as empty. Each line in each tree corresponds to all branches of this tree (concatenated by a +) that correspond to one of the possible response categories. The position of each line must correspond to the position of this response category in the data object (for multiindividual fit to the respective column).
The difference between both types of EQN format ("eqn"
or"eqn2"
) is the way the first line of the model file is treated. If model.file
is set to "eqn"
, MPTinR
will ignore the first line of the model file and will read the rest of the file (as does multiTree; Moshagen, 2010). If model.file
is set to "eqn2"
MPTinR will only read as many lines as indicated in the first line of the EQN model file (as does e.g., HMMTree; Stahl & Klauer, 2007). As default fit.mpt
expects the easy format, but if the filename ends with .eqn or .EQN and model.type
is "easy"
, model.type
is set to "eqn"
For the EQN format consult one of the corresponding papers (see e.g., Moshagen, 2010; Stahl & Klauer, 2007). The positions in the data object (number of column for multiindividual fit) must correspond to the category number in the EQN file.
Note that names of parameters in the model file should not start with hank.
. Variables with these names can lead to unforeseen problems as variables starting with these letters are internally used. Furthermore, any reserved
names (e.g., NA
) are not allowed in model files of any types (i.e., also not as category labels in .eqn files). All names in models need to be valid R variable names (see make.names
).
The restrictions file may contain (sequential) equality (i.e., =) and inequality (i.e., <) restrictions and must adhere to the following rules:
1. Inequalities first.
2. If a variable appears in an inequality restriction, it can not be on the left hand side (LHS) of any further restriction.
3. If a variable appears on the right hand side (RHS) of an equality restriction, it can not appear on LHS of an equality restriction.
Note that only "<" is supported as inequality operator but not ">"!
Examples of restrictions are (the following could all appear in one restrictions file):
D1 < D2 < D3
D4 = D3
B1 = B3 = 0.3333
X4 = X5 = D3
Restrictions file may contain comments (i.e., everything to the right of a # will be ignored; new behavior since version 0.9.2)
Restrictions can also be specified in line as a list. The same restrictions as the one above as a list
would be list("D1 < D2 < D3", "D4 = D3", "B1 = B3 = 0.3333", "X4 = X5 = D3")
(simply use this list
as the restrictions.filename
argument).
For equality restrictions, the equality restricted parameters are simply exchanged with their restrictions before the fitting.
For inequality restricted parameters, the model is reparameterized so that only the rightmost parameter of an inequality restriction remains the original parameter. Each instance of the other parameters in this restriction is replaced by the product of the rightmost parameter and dummy parameters (see Knapp & Batchelder, 2004). This procedure (which is equivalent to method A described in Knapp & Batchelder, 2004) leads to an equivalent model (although the binary MPT structure is not apparent in the resulting equations).
To prohibit this reparameterization (i.e., if the inequality restrictions hold without reparameterization), you can set reparam.ineq
to FALSE
. This can be useful for obtaining the FIA (see examples in Wu, Myung, & Batchelder, 2010).
Both models and restrictions can be specified as textConnection
s instead of as external files.
Furthermore, restrictions can be specified directly as a list
containing the restrictions (quoted, i.e. as characters).
fit.model
contains additional examples showing model and restrictions specification within the code.
Note that when setting some parameters equal and also restricting their order, the parameters set equal which are not the rightmost element in the order (i.e., inequality) restriction, are computed correctly, but are marked as inequality restricted instead of equality restricted in the output (this did not work at all before v1.0.1). An example: For the restrictions list("G2 < G3 < G5", "G1 = G2", "G4 = G5")
, G1
would be computed correctly, but marked as inequality restricted. In contrast, G4
would be marked as equal to G5
(and also computed correctly).
To obtain a measure of the model's complexity beyond the number of parameters (and taking inequality restrictions into account), set fia
to a (reasonably high) scalar integer (i.e., a number). Then, fit.mpt
will obtain the Fisher Information Approximation (FIA), a Minimum Description Length (MDL) based measure of model complexity, using the algorithm provided by Wu, Myung, & Batchelder (2010a, 2010b) ported from Matlab to R. When performing modelselection, this measure is superior to other methods such as the Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC) which basically only take the number of parameters into account.
To get the FIA, fit.mpt
performs the following steps:
1. The representation of the model as equations is transformed into the string representation of the model in the contextfree language of MPT models (LBMPT; Purdy & Batchelder, 2009). For this step to be successful it is absolutely necessary that the equations representing the model perfectly map the tree structure of the MPT. That is, the model file is only allowed to contain parameters, their inverse (e.g., Dn
and (1  Dn)
) and the operators + and *, but nothing else. Simplifications of the equations will seriously distort this step. Similarly, unnecessary brackets will distort the results. Brackets must only be used to indicate the inverse of a parameter (i.e. (1  parameter)). This step is achieved by make.mpt.cf
.
2. The context free representation of the model is then fed into the MCMC function computing the FIA (the port of BMPTFIA provided by Wu, Myung & Batchelder (2010a), see bmpt.fia
).
(Actually, both steps are achieved by a call to get.mpt.fia
)
Note that FIA can sometimes be nonconsistent (i.e., larger FIA penalty values for restricted versions of a model than for the superordinate model; see Navarro, 2004). This may specifically happens for small ns and is for example the case for the Broder & Schutz example below. In these cases FIA cannot be used! Therefore, always check for consistency of the FIA penalty terms.
Once again: If one wants to compute the FIA, it is absolutely necessary, that the representation of the model via equations in the model file exactly maps on the structure of the binary MPT (see make.mpt.cf
for more details).
Confidence intervals (CI) are based on the observed Hessian matrix produced by the symbolically derived function for the Hessian (i.e., the second derivative of the likelihood function). If it is based on a numerically estimated Hessian, a warning will be given.
For inequality restricted parameters, the CIs are computed using the parameter estimates' variance bounds (see Baldi & Batchelder, 2003; especially Equation 19). Note that these bounds represent the "worst case scenario" variances, and can lead to CIs outside parameter boundaries if the set of inequalities is large and/or the variances for the reparameterized model are large (Note that CIs for nonrestricted parameters can be outside the parameter boundaries as well due to large variances).
To avoid local minima and instead find the maximum likelihood estimates it is useful to set n.optim
> 1 with random starting values (see below). If n.optim
> 1, the summary
of the vector containing the LogLikelihood values returned by each run of nlminb
is added to the output (to check whether local minima were present). If the model is rather big, n.optim
> 1 can be slow.
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/).
Note that you should close snowfall via sfStop()
after using MPTinR.
The fitting/optimization is achieved via nlminb
(Fox, Hall, & Schryer, 1978) a Newton based algorithm using the analytically derived gradient. In some cases (e.g., in case of empty cells) nlminb
will not converge successfully in which fit.mpt
will retry fitting using a numerically estimated gradient (with warning).
fit.mpt
is just a comfortable wrapper around the workhorse fit.mptinr
. fit.mpt
produces the appropriate objective function, gradient function, hessian function, and prediction function that are handed over to fit.mptinr
(functions are produced by symbolical derivation, see D
). A function similar to fit.mpt
is fit.model
which has the additional arguments lower.bound
and upper.bound
allowing to fit other models than just MPTs and the possibility to indicate whether or not to use the analytically derived gradient or hessian for fitting (here this is automatically handled). Note that for MPTs (where upper and lower bounds of parameters are set to 0 and 1, respectively) fit.mpt
is probably faster as the objective function is slightly faster (i.e., more optimized). However, for datasets with many empty cells trying fit.model
with or without gradient or hessian can be worth a try.
Note that fit.mptinr
can fit models with arbitrary (i.e., custom) objective functions.
The old version of this function using optim
's LBFGSB algorithm is fit.mpt.old
.
For individual fits (i.e., data
is a vector
) a list
containing one or more of the following components from the best fitting model:
goodness.of.fit 
A 
information.criteria 
A 
model.info 
A 
parameters 
A data.frame containing the parameter estimates and corresponding confidence intervals. If a restriction file was present, the restricted parameters are marked. 
data 
A 
For multidataset fits (i.e., data
is a matrix
or data.frame
) a list
with similar elements, but the following differences:
The first elements, goodness.of.fit
, information.criteria
, and model.info
, contain the same information as for individual fits, but each are lists
with three elements containing the respective values for: each individual in the list element individual
, the sum of the individual values in the list element sum
, and the values corresponding to the fit for the aggregated data in the list element aggregated
.
parameters
is a list containing:
individual 
A 3dimensional array containing the parameter estimates ([,1,]), confidence intervals [,2:3,], and, if restrictions not 
mean 
A 
aggregated 
A data.frame containing the parameter estimates and corresponding confidence intervals for the aggregated data. If a restriction file was present, the restricted parameters are marked. 
The element data
contains two matrices, one with the observed
, and one with the predicted
data (or is a list containing lists with individual
and aggregated
observed
and predicted
data).
If n.optim
> 1, the summary
of the vector (matrix for multiindividual fit) containing the LogLikelihood values returned by each run of optim
is added to the output: fitting.runs
When output == "full"
the list contains the additional items:
optim.runs 
A list (or list of lists for multiple datasets) containing the outputs from all runs by 
best.fits 
A list (or list of lists for multiple datasets) containing the outputs from the runs by 
hessian 
A list containing the Hessian matrix or matrices of the final parameter estimates. 
Warnings may relate to the optimization routine (e.g., Optimization routine [...] did not converge successfully
).
In these cases it is recommended to rerun fit.mpt to check if the results are stable.
All (model or restriction) files should end with an empty line, otherwise a warning will be shown.
The likelihood returned does not include the factorial constants of the multinomial probabilitymass functions.
Henrik Singmann and David Kellen with help from 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.
Broeder, A., & Schuetz, J. (2009). Recognition ROCs are curvilinearor are they? On premature arguments against the twohighthreshold model of recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(3), 587. doi:10.1037/a0015279
Byrd, R. H., Lu, P., Nocedal, J., & Zhu, C. (1995). A limited memory algorithm for bound constrained optimization. SIAM J. Scientific Computing, 16, 11901208.
Fox, P. A., Hall, A. P., & Schryer, N. L. (1978). The PORT Mathematical Subroutine Library. CM Trans. Math. Softw., 4, 104126. http://doi.acm.org/10.1145/355780.355783
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.
Navarro, D. J. (2004). A Note on the Applied Use of MDL Approximations. Neural Computation, 16(9), 17631768.
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.
check.mpt
for a function that can help in constructing models.
select.mpt
for the function that performs model selection on results from fit.mpt
.
fit.model
for a similar wrapper for which you can specify upper and lower bounds of parameters (and whether or not nlminb
uses the symbolically derived gradient and hessian)
fit.mptinr
is the workhorse with which you can also fit your own objective functions.
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182  # 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 not replicated here.
# Instead, we use each condition (i.e., row in Table 1) as a different dataset.
# load the data
data(rb.fig1.data, package = "MPTinR")
#get the character string with the position of the model:
model1 < system.file("extdata", "rb.fig1.model", package = "MPTinR")
model1.eqn < system.file("extdata", "rb.fig1.model.eqn", package = "MPTinR")
# just fit the first dataset:
fit.mpt(rb.fig1.data[1,], model1, n.optim = 1)
fit.model(rb.fig1.data[1,], model1, n.optim = 1)
#fit all datasets:
fit.mpt(rb.fig1.data, model1, n.optim = 1)
fit.model(rb.fig1.data, model1, n.optim = 1)
#fit all datasets using the .EQN model file:
fit.mpt(rb.fig1.data, model1.eqn, n.optim = 1)
#fit using a textConnection (i.e., you can specify the model in your script/code):
model1.txt < "p * q * r
p * q * (1r)
p * (1q) * r
p * (1q) * (1r) + (1p)"
fit.mpt(rb.fig1.data, textConnection(model1.txt), n.optim = 1)
# 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 = "MPTinR")
# positions of model and restriction files:
model2 < system.file("extdata", "rb.fig2.model", package = "MPTinR")
model2r.r.eq < system.file("extdata", "rb.fig2.r.equal", package = "MPTinR")
model2r.c.eq < system.file("extdata", "rb.fig2.c.equal", package = "MPTinR")
# The full (i.e., unconstrained) model
(ref.model < fit.mpt(rb.fig2.data, model2))
# All r equal
(r.equal < fit.mpt(rb.fig2.data, model2, model2r.r.eq))
# All c equal
(c.equal < fit.mpt(rb.fig2.data, model2, model2r.c.eq))
# is setting all r equal a good idea?
(g.sq.r.equal < r.equal[["goodness.of.fit"]][["G.Squared"]] 
ref.model[["goodness.of.fit"]][["G.Squared"]])
(df.r.equal < r.equal[["goodness.of.fit"]][["df"]] 
ref.model[["goodness.of.fit"]][["df"]])
(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 < c.equal[["goodness.of.fit"]][["G.Squared"]] 
ref.model[["goodness.of.fit"]][["G.Squared"]])
(df.c.equal < c.equal[["goodness.of.fit"]][["df"]] 
ref.model[["goodness.of.fit"]][["df"]])
(p.value.c.equal < pchisq(g.sq.c.equal, df.c.equal , lower.tail = FALSE))
# You can specify restrictions also via a list instead of an external file:
# All r equal
r.equal.2 < fit.mpt(rb.fig2.data, model2, list("r0 = r1 = r2= r3 = r4"), n.optim = 5)
all.equal(r.equal, r.equal.2)
# All c equal
c.equal.2 < fit.mpt(rb.fig2.data, model2, list("c0 = c1 = c2 = c3= c4"))
all.equal(c.equal, c.equal.2)
## Not run:
# Example from Broder & Schutz (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.
# Finally, we compute the FIA for all models, taking inequalities into account.
data(d.broeder, package = "MPTinR")
m.2htm < system.file("extdata", "5points.2htm.model", package = "MPTinR")
r.2htm < system.file("extdata", "broeder.2htm.restr", package = "MPTinR")
r.1htm < system.file("extdata", "broeder.1htm.restr", package = "MPTinR")
i.2htm < system.file("extdata", "broeder.2htm.ineq", package = "MPTinR")
ir.2htm < system.file("extdata", "broeder.2htm.restr.ineq", package = "MPTinR")
ir.1htm < system.file("extdata", "broeder.1htm.restr.ineq", package = "MPTinR")
# fit the original 2HTM
br.2htm < fit.mpt(d.broeder, m.2htm)
br.2htm.ineq < fit.mpt(d.broeder, m.2htm, i.2htm)
# do the inequalities hold for all participants?
br.2htm.ineq[["parameters"]][["individual"]][,"estimates",]
br.2htm[["parameters"]][["individual"]][,"estimates",]
# See the difference between forced and nonforced inequality restrictions:
round(br.2htm[["parameters"]][["individual"]][,"estimates",] 
br.2htm.ineq[["parameters"]][["individual"]][,"estimates",],2)
# The same for the other two models
# The restricted 2HTM
br.2htm.res < fit.mpt(d.broeder, m.2htm, r.2htm)
br.2htm.res.ineq < fit.mpt(d.broeder, m.2htm, ir.2htm)
round(br.2htm.res[["parameters"]][["individual"]][,"estimates",] 
br.2htm.res.ineq[["parameters"]][["individual"]][,"estimates",],2)
# The 1HTM
br.1htm < fit.mpt(d.broeder, m.2htm, r.1htm)
br.1htm.ineq < fit.mpt(d.broeder, m.2htm, ir.1htm)
round(br.2htm.res[["parameters"]][["individual"]][,"estimates",] 
br.2htm.res.ineq[["parameters"]][["individual"]][,"estimates",],2)
# identical to the last fit of the 1HTM (using a list as restriction):
br.1htm.ineq.list < fit.mpt(d.broeder, m.2htm, list("G1 < G2 < G3 < G4 < G5", "Dn = 0"))
all.equal(br.1htm.ineq, br.1htm.ineq.list) # TRUE
# These results show that inequality restrictions do not hold for all datasets.
# (It would look differently if we excluded critical cases,
# i.e., 2, 6, 7, 10, 18, 21, 25, 29, 32, 34, 35, 37, 38)
# Therefore, we get the FIA for the models as computed above
br.2htm.fia < fit.mpt(d.broeder, m.2htm, fia = 200000)
br.2htm.ineq.fia < fit.mpt(d.broeder, m.2htm, i.2htm, fia = 200000)
br.2htm.res.fia < fit.mpt(d.broeder, m.2htm, r.2htm, fia = 200000 )
br.2htm.res.ineq.fia < fit.mpt(d.broeder, m.2htm, ir.2htm, fia = 200000)
br.1htm.fia < fit.mpt(d.broeder, m.2htm, r.1htm, fia = 200000)
br.1htm.ineq.fia < fit.mpt(d.broeder, m.2htm, ir.1htm, fia = 200000)
# Model selection using the FIA
(br.select < select.mpt(list(br.2htm.fia, br.2htm.ineq.fia, br.2htm.res.fia,
br.2htm.res.ineq.fia, br.1htm.fia, br.1htm.ineq.fia)))
# The same results, ordered by FIA
br.select[order(br.select[,"delta.FIA.sum"]),]
# Note that FIA for individual data (.sum) is not consistent (i.e., the penalty
# for the nested model br.1htm.ineq.fia is not really smaller than the penalty
# for the superordinate model br.2htm.ineq.fia).
# Hence, one should use the aggregated data for this analysis (not shown here)!
# Compare this with the model selection not using FIA:
select.mpt(list(br.2htm, br.2htm.ineq, br.2htm.res, br.2htm.res.ineq, br.1htm, br.1htm.ineq))
# Only use the aggregated data:
d.broeder.agg < colSums(d.broeder)
br.2htm.agg < fit.mpt(d.broeder.agg, m.2htm)
br.2htm.res.agg < fit.mpt(d.broeder.agg, m.2htm, r.2htm)
br.1htm.agg < fit.mpt(d.broeder.agg, m.2htm, r.1htm)
select.mpt(list(br.2htm.agg, br.2htm.res.agg, br.1htm.agg), output = "full")
# compare speed of no multicore versus multicore for multiple datasets:
require(snowfall)
# change number of CPUs if more are available
nCPU = 2
sfInit( parallel=TRUE, cpus=nCPU, type = "SOCK" )
# NO multicore
system.time(fit.mpt(d.broeder, m.2htm))
# multicore:
system.time(fit.mpt(d.broeder, m.2htm, multicore = "individual"))
sfStop()
## End(Not run)

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