fit.model
fits MPT and other cognitive models for categorical data (e.g., SDT models) that can be specified in a model file.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  fit.model(
data,
model.filename,
restrictions.filename = NULL,
n.optim = 5,
fia = NULL,
ci = 95,
starting.values = NULL,
lower.bound = 0,
upper.bound = 1,
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(),
use.gradient = TRUE, use.hessian = FALSE, check.model = TRUE,
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) for MPTs only. See Details at 
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 "full" 
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 
lower.bound 
numeric scalar or vector. Can be used in 
upper.bound 
numeric scalar or vector. Can be used in 
control 
list containing control arguments passed on to 
use.gradient 
logical. Whether or not the symbolically derived function returning the gradient should be used for fitting. Default is 
use.hessian 
logical. Whether or not the symbolically derived function returning the Hessian matrix should be used for fitting. Default is 
check.model 
logical. Should model be checked with random values whether or not the expected values sum to one per tree? Default is 
args.fia 
named list of further arguments passed to 
numDeriv 
logical. Should the Hessian matrix of the maximum likelihood estimates be estimated numerically using 
This functions should be used when fitting a model that is not an MPT model or when fitting using fit.mpt
fails. For fitting MPT models and information on fitting MPT models see fit.mpt
.
The model file for nonMPT models should be of the easy
format. That is the ordinal number or rank of each line should correspond to this column/position in the data object. Model files can contain any visible function (i.e., including selfdefined functions). However, note that the derivation that is needed for the gradient and Hessian function can only be done for those functions that D
can handle. If derivation fails a warning will be given and fitting will be done without gradient and/or Hessian function.
Equations that correspond to one item type/category must be not be separated by an empty line. Equations that do not correspond to the same item type/category must be separated by at least one empty line.
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.
The restrictions file may contain (sequential) equality (i.e., =) and inequality (i.e., <) restrictions (see fit.mpt
for more general info on the restrictions files). Note that inequality restrictions usually will lead to catastrophic results when used for nonMPT models. Our recommendation: Do never use inequality restrictions for nonMPT models. Equality restrictions or fixing parameters should be no problem though.
For equality restrictions, the equality restricted parameters are simply exchanged with their restrictions (i.e., another parameter or a number) before the fitting.
Restrictions or model files can contain comments (i.e., everything to the right of a # will be ignored; new behavior since version 0.9.2)
Both models and restrictions can be specified as textConnection
s instead of as external files (see examples). Note that textConnections get "consumed" so you may need to specify them each time you fit a model using a connection (see Examples
for how to avoid this).
Confidence intervals (CI) are based on the 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.
To set the starting values for the fitting process (e.g., to avoid local minima) one can set starting.values
to a vector of length 2 and n.optim > 1
. Then, starting values are randomly drawn from a uniform distribution from starting.values[1]
to starting.values[2]
.
Alternatively, one can supply a list with two elements to starting.values
. Both elements need to be either of length 1 or of length equal to the number of parameters (if both are of length 1, it is the same as if you supply a vector of length 2). For each parameter n (in alphabetical order), a starting value is randomly drawn from a uniform distribution starting.values[[1]][n]
to starting.values[[2]][n]
(if length is 1, this is the border for all parameters).
The least interesting option is to specify the starting values individually by supplying a vector with the same length as the number of parameters. Starting values must be ordered according to the alphabetical order of the parameter names. Use check.mpt
for a function that returns the alphabetical order of the parameters. If one specifies the starting values like that, n.optim
will be set to 1 as all other values would not make any sense (the optimization routine will produce identical results with identical starting values).
The lower.bound
and upper.bound
needs to be of length 1 or equal to the number of free
parameters. If length > 1, parameters are mapped to the bounds in alphabetic order of the parameters. Use check.mpt
to obtain the alphabetical order of parameters for your model.
This function is basically a comfortable wrapper for fit.mptinr
producing the appropriate objective, gradient, hessian, and prediction function from the model equations (passed via model.filename
) whilst allowing for custom lower or upper bounds on the parameters. You can specify whether or not gradient or hessian function should be used for fitting with use.gradient
or use.hessian
, respectively.
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.
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 the model fitting to check if the results are stable.
The likelihood returned does not include the factorial constants of the multinomial probabilitymass functions.
All (model or restriction) files should end with an empty line, otherwise a warning will be shown.
Henrik Singmann and David Kellen.
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
Wickens, T. D. (2002). Elementary Signal Detection Theory. Oxford; New York: Oxford University Press.
check.mpt
for a function that can help in constructing models.
fit.mptinr
for a function that can fit arbitrary objective functions.
fit.mpt
for the function to fit MPTs (it should be slightly faster for MPTs).
roc6 for more examples fitting different SDT models.
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  ## Not run:
#####################################
## Fit responsebias or payoff ROC ##
#####################################
# Example from Broder & Schutz (2009)
# We fit the data from the 40 individuals from their Experiment 3
# We fit three different models:
# 1. Their SDT Model: br.sdt
# 2. Their 2HTM model: br.2htm
# 3. A restricted 2HTM model with Dn = Do: br.2htm.res
# 4. A 1HTM model (i.e., Dn = 0): br.1htm
data(d.broeder, package = "MPTinR")
m.2htm < system.file("extdata", "5points.2htm.model", package = "MPTinR")
# We specify the SDT model in the code using a textConnection.
# However, textConnection is only called in the function call on the string.
m.sdt < "
1pnorm((cr1mu)/ss)
pnorm((cr1mu)/ss)
1pnorm(cr1)
pnorm(cr1)
1pnorm((cr2mu)/ss)
pnorm((cr2mu)/ss)
1pnorm(cr2)
pnorm(cr2)
1pnorm((cr3mu)/ss)
pnorm((cr3mu)/ss)
1pnorm(cr3)
pnorm(cr3)
1pnorm((cr4mu)/ss)
pnorm((cr4mu)/ss)
1pnorm(cr4)
pnorm(cr4)
1pnorm((cr5mu)/ss)
pnorm((cr5mu)/ss)
1pnorm(cr5)
pnorm(cr5)
"
# How does the model look like?
check.mpt(textConnection(m.sdt))
# fit the SDT (unequal variance version)
br.uvsdt < fit.model(d.broeder, textConnection(m.sdt),
lower.bound = c(rep(Inf, 5), 0, 1), upper.bound = Inf)
# Is there any effect of studying the items?
br.uvsdt.2 < fit.model(d.broeder, textConnection(m.sdt),
restrictions.filename = list("mu = 0", "ss = 1"),
lower.bound = Inf, upper.bound = Inf)
(diff.g2 < br.uvsdt.2[["goodness.of.fit"]][["sum"]][["G.Squared"]] 
br.uvsdt[["goodness.of.fit"]][["sum"]][["G.Squared"]])
(diff.df < br.uvsdt.2[["goodness.of.fit"]][["sum"]][["df"]] 
br.uvsdt[["goodness.of.fit"]][["sum"]][["df"]])
1  pchisq(diff.g2, diff.df)
# fit the equal variance SDT model:
br.evsdt < fit.model(d.broeder, textConnection(m.sdt),
lower.bound = c(rep(Inf, 5), 0), upper.bound = Inf,
restrictions.filename = list("ss = 1"))
# fit the MPTs (see also ?fit.mpt).
# In contrast to ?fit.mpt we specify the restrictions using a textConnection or a list!
br.2htm < fit.mpt(d.broeder, m.2htm)
br.2htm.res < fit.mpt(d.broeder, m.2htm, textConnection("Do = Dn"))
br.1htm < fit.mpt(d.broeder, m.2htm, list("Dn = 0"))
select.mpt(list(uvsdt = br.uvsdt, evsdt = br.evsdt, two.htm = br.2htm,
two.htm.res = br.2htm.res, one.htm = br.1htm), output = "full")
# the restricted 2HTM "wins" for individual data (although evsdt does not perform too bad),
# but the 2htm and restricted 2htm restricted "win" for aggregated data.
###################################
## Fit confidence rating ROC SDT ##
###################################
#(see ?roc6 for more examples)
# We fit example data from Wickens (2002, Chapter 5)
# The example data is from Table 5.1, p. 84
# (data is entered in somewhat different order).
# Note that criteria are defined as increments to
# the first (i.e., leftmost) criterion!
# This is the only way to do it in MPTinR.
# Data
dat < c(47, 65, 66, 92, 136, 294, 166, 161, 138, 128, 63, 43)
# UVSDT
m.uvsdt < "
pnorm(cr1, mu, sigma)
pnorm(cr1+cr2, mu, sigma)  pnorm(cr1, mu, sigma)
pnorm(cr3+cr2+cr1, mu, sigma)  pnorm(cr2+cr1, mu, sigma)
pnorm(cr4+cr3+cr2+cr1, mu, sigma)  pnorm(cr3+cr2+cr1, mu, sigma)
pnorm(cr5+cr4+cr3+cr2+cr1, mu, sigma)  pnorm(cr4+cr3+cr2+cr1, mu, sigma)
1  pnorm(cr5+cr4+cr3+cr2+cr1, mu, sigma)
pnorm(cr1)
pnorm(cr2+cr1)  pnorm(cr1)
pnorm(cr3+cr2+cr1)  pnorm(cr2+cr1)
pnorm(cr4+cr3+cr2+cr1)  pnorm(cr3+cr2+cr1)
pnorm(cr5+cr4+cr3+cr2+cr1)  pnorm(cr4+cr3+cr2+cr1)
1  pnorm(cr5+cr4+cr3+cr2+cr1)
"
check.mpt(textConnection(m.uvsdt))
# Model fitting
(cr_sdt < fit.model(dat, textConnection(m.uvsdt),
lower.bound=c(Inf, rep(0, 5), 0.1), upper.bound=Inf))
# To obtain the criteria (which match those in Wickens (2002, p. 90)
# obtain the cumulative sum:
cumsum(cr_sdt$parameters[paste0("cr",1:5), 1, drop = FALSE])
## End(Not run)

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