betaMPTcpp {TreeBUGS} | R Documentation |
C++ Sampler for Hierarchical Beta-MPT Model
Description
Fast Gibbs sampler in C++ that is tailored to the beta-MPT model.
Usage
betaMPTcpp(
eqnfile,
data,
restrictions,
covData,
corProbit = FALSE,
n.iter = 20000,
n.burnin = 2000,
n.thin = 5,
n.chains = 3,
ppp = 0,
shape = 1,
rate = 0.1,
parEstFile,
posteriorFile,
cores = 1
)
Arguments
eqnfile |
The (relative or 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 |
data |
The (relative or full) path to the .csv file with the data (comma separated; category labels in first row). Alternatively: a data frame or matrix (rows=individuals, columns = individual category frequencies, category labels as column names) |
restrictions |
Specifies which parameters should be (a) constant (e.g.,
|
covData |
Data that contains covariates, for which correlations with
individual MPT parameters will be sampled. Either the path to a .csv file
(comma-separated: rows=individuals in the same order as |
corProbit |
whether to use probit-transformed MPT parameters to compute
correlations (probit-values of |
n.iter |
Number of iterations per chain (including burnin samples). See
|
n.burnin |
Number of samples for burnin (samples will not be stored and removed from n.iter) |
n.thin |
Thinning rate. |
n.chains |
number of MCMC chains (sampled in parallel). |
ppp |
number of samples to compute posterior predictive p-value (see
|
shape |
shape parameter(s) of Gamma-hyperdistribution for the
hierarchical beta-parameters |
rate |
rate parameter(s) of Gamma-hyperdistribution |
parEstFile |
Name of the file to with the estimates should be stored (e.g., "parEstFile.txt") |
posteriorFile |
path to RData-file where to save the model including
MCMC posterior samples (an object named |
cores |
number of CPUs to be used |
Author(s)
Daniel Heck
Examples
## Not run:
# fit beta-MPT model for encoding condition (see ?arnold2013):
EQNfile <- system.file("MPTmodels/2htsm.eqn", package = "TreeBUGS")
d.encoding <- subset(arnold2013, group == "encoding", select = -(1:4))
fit <- betaMPTcpp(EQNfile, d.encoding,
n.thin = 5,
restrictions = list("D1=D2=D3", "d1=d2", "a=g")
)
# convergence
plot(fit, parameter = "mean", type = "default")
summary(fit)
## End(Not run)