MCMCmixfactanal  R Documentation 
This function generates a sample from the posterior distribution of a mixed data (both continuous and ordinal) factor analysis model. Normal priors are assumed on the factor loadings and factor scores, improper uniform priors are assumed on the cutpoints, and inverse gamma priors are assumed for the error variances (uniquenesses). The user supplies data and parameters for the prior distributions, and a sample from the posterior distribution is returned as an mcmc object, which can be subsequently analyzed with functions provided in the coda package.
MCMCmixfactanal(
x,
factors,
lambda.constraints = list(),
data = parent.frame(),
burnin = 1000,
mcmc = 20000,
thin = 1,
tune = NA,
verbose = 0,
seed = NA,
lambda.start = NA,
psi.start = NA,
l0 = 0,
L0 = 0,
a0 = 0.001,
b0 = 0.001,
store.lambda = TRUE,
store.scores = FALSE,
std.mean = TRUE,
std.var = TRUE,
...
)
x 
A onesided formula containing the manifest variables. Ordinal
(including dichotomous) variables must be coded as ordered factors. Each
level of these ordered factors must be present in the data passed to the
function. NOTE: data input is different in 
factors 
The number of factors to be fitted. 
lambda.constraints 
List of lists specifying possible equality or
simple inequality constraints on the factor loadings. A typical entry in the
list has one of three forms: 
data 
A data frame. 
burnin 
The number of burnin iterations for the sampler. 
mcmc 
The number of iterations for the sampler. 
thin 
The thinning interval used in the simulation. The number of iterations must be divisible by this value. 
tune 
The tuning parameter for the MetropolisHastings sampling. Can
be either a scalar or a 
verbose 
A switch which determines whether or not the progress of the
sampler is printed to the screen. If 
seed 
The seed for the random number generator. If NA, the Mersenne
Twister generator is used with default seed 12345; if an integer is passed
it is used to seed the Mersenne twister. The user can also pass a list of
length two to use the L'Ecuyer random number generator, which is suitable
for parallel computation. The first element of the list is the L'Ecuyer
seed, which is a vector of length six or NA (if NA a default seed of

lambda.start 
Starting values for the factor loading matrix Lambda. If

psi.start 
Starting values for the error variance (uniqueness) matrix.
If 
l0 
The means of the independent Normal prior on the factor loadings.
Can be either a scalar or a matrix with the same dimensions as

L0 
The precisions (inverse variances) of the independent Normal prior
on the factor loadings. Can be either a scalar or a matrix with the same
dimensions as 
a0 
Controls the shape of the inverse Gamma prior on the uniqueness.
The actual shape parameter is set to 
b0 
Controls the scale of the inverse Gamma prior on the uniquenesses.
The actual scale parameter is set to 
store.lambda 
A switch that determines whether or not to store the factor loadings for posterior analysis. By default, the factor loadings are all stored. 
store.scores 
A switch that determines whether or not to store the factor scores for posterior analysis. NOTE: This takes an enormous amount of memory, so should only be used if the chain is thinned heavily, or for applications with a small number of observations. By default, the factor scores are not stored. 
std.mean 
If 
std.var 
If 
... 
further arguments to be passed 
The model takes the following form:
Let i=1,\ldots,N
index observations and j=1,\ldots,K
index response variables within an observation. An observed
variable x_{ij}
can be either ordinal with a total of
C_j
categories or continuous. The distribution of X
is
governed by a N \times K
matrix of latent variables X^*
and a series of cutpoints \gamma
. X^*
is assumed to be
generated according to:
x^*_i = \Lambda \phi_i + \epsilon_i
\epsilon_i \sim \mathcal{N}(0,\Psi)
where x^*_i
is the k
vector of latent variables
specific to observation i
, \Lambda
is the k \times
d
matrix of factor loadings, and \phi_i
is the
d
vector of latent factor scores. It is assumed that the
first element of \phi_i
is equal to 1 for all i
.
If the j
th variable is ordinal, the probability that it takes the
value c
in observation i
is:
\pi_{ijc} = \Phi(\gamma_{jc}  \Lambda'_j\phi_i) 
\Phi(\gamma_{j(c1)}  \Lambda'_j\phi_i)
If the j
th variable is continuous, it is assumed that x^*_{ij}
= x_{ij}
for all i
.
The implementation used here assumes independent conjugate priors for each
element of \Lambda
and each \phi_i
. More
specifically we assume:
\Lambda_{ij} \sim \mathcal{N}(l_{0_{ij}}, L_{0_{ij}}^{1}),
i=1,\ldots,k, j=1,\ldots,d
\phi_{i(2:d)} \sim \mathcal{N}(0, I), i=1,\dots,n
MCMCmixfactanal
simulates from the posterior distribution using a
MetropolisHastings within Gibbs sampling algorithm. The algorithm employed
is based on work by Cowles (1996). Note that the first element of
\phi_i
is a 1. As a result, the first column of
\Lambda
can be interpretated as negative item difficulty
parameters. Further, the first element \gamma_1
is
normalized to zero, and thus not returned in the mcmc object. The
simulation proper is done in compiled C++ code to maximize efficiency.
Please consult the coda documentation for a comprehensive list of functions
that can be used to analyze the posterior sample.
As is the case with all measurement models, make sure that you have plenty of free memory, especially when storing the scores.
An mcmc object that contains the posterior sample. This object can be summarized by functions provided by the coda package.
Kevin M. Quinn. 2004. “Bayesian Factor Analysis for Mixed Ordinal and Continuous Responses.” Political Analysis. 12: 338353.
Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park. 2011. “MCMCpack: Markov Chain Monte Carlo in R.”, Journal of Statistical Software. 42(9): 121. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v042.i09")}.
M. K. Cowles. 1996. “Accelerating Monte Carlo Markov Chain Convergence for Cumulativelink Generalized Linear Models." Statistics and Computing. 6: 101110.
Valen E. Johnson and James H. Albert. 1999. “Ordinal Data Modeling." Springer: New York.
Daniel Pemstein, Kevin M. Quinn, and Andrew D. Martin. 2007. Scythe Statistical Library 1.0. http://scythe.lsa.umich.edu.
Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines. 2006. “Output Analysis and Diagnostics for MCMC (CODA)”, R News. 6(1): 711. https://CRAN.Rproject.org/doc/Rnews/Rnews_20061.pdf.
plot.mcmc
, summary.mcmc
,
factanal
, MCMCfactanal
,
MCMCordfactanal
, MCMCirt1d
,
MCMCirtKd
## Not run:
data(PErisk)
post < MCMCmixfactanal(~courts+barb2+prsexp2+prscorr2+gdpw2,
factors=1, data=PErisk,
lambda.constraints = list(courts=list(2,"")),
burnin=5000, mcmc=1000000, thin=50,
verbose=500, L0=.25, store.lambda=TRUE,
store.scores=TRUE, tune=1.2)
plot(post)
summary(post)
library(MASS)
data(Cars93)
attach(Cars93)
new.cars < data.frame(Price, MPG.city, MPG.highway,
Cylinders, EngineSize, Horsepower,
RPM, Length, Wheelbase, Width, Weight, Origin)
rownames(new.cars) < paste(Manufacturer, Model)
detach(Cars93)
# drop obs 57 (Mazda RX 7) b/c it has a rotary engine
new.cars < new.cars[57,]
# drop 3 cylinder cars
new.cars < new.cars[new.cars$Cylinders!=3,]
# drop 5 cylinder cars
new.cars < new.cars[new.cars$Cylinders!=5,]
new.cars$log.Price < log(new.cars$Price)
new.cars$log.MPG.city < log(new.cars$MPG.city)
new.cars$log.MPG.highway < log(new.cars$MPG.highway)
new.cars$log.EngineSize < log(new.cars$EngineSize)
new.cars$log.Horsepower < log(new.cars$Horsepower)
new.cars$Cylinders < ordered(new.cars$Cylinders)
new.cars$Origin < ordered(new.cars$Origin)
post < MCMCmixfactanal(~log.Price+log.MPG.city+
log.MPG.highway+Cylinders+log.EngineSize+
log.Horsepower+RPM+Length+
Wheelbase+Width+Weight+Origin, data=new.cars,
lambda.constraints=list(log.Horsepower=list(2,"+"),
log.Horsepower=c(3,0), weight=list(3,"+")),
factors=2,
burnin=5000, mcmc=500000, thin=100, verbose=500,
L0=.25, tune=3.0)
plot(post)
summary(post)
## End(Not run)
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