Description Usage Arguments Value References Examples
pcirm
is a partially confirmatory approach to item response models (Chen, 2020),
which estimates the intercept for continuous and dichotomous data. Similar to PCFA and GPCFA,
there are two major model variants with different constraints for identification. One assumes local
independence (LI) with a more exploratory tendency, which can be also called the Estep.
The other allows local dependence (LD) with a more confirmatory tendency, which can be also
called the Cstep. Parameters are obtained by sampling from the posterior distributions with
the Markov chain Monte Carlo (MCMC) techniques. Different Bayesian Lasso methods are used to
regularize the loading pattern and LD. The estimation results can be summarized with summary.lawbl
and the factorial eigenvalue can be plotted with plot_lawbl
.
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dat 
A N \times J data matrix or data.frame consisting of the responses of N individuals to J items. Only continuous and dichotomous data are supported. 
Q 
A J \times K design matrix for the loading pattern with K factors and J items.
Elements are 1, 1, and 0 for specified, unspecified, and zerofixed loadings, respectively. For models with
LI or the Estep, one can specify a few (e.g., 2) loadings per factor. For models with LD or the Cstep, the
sufficient condition of one specified loading per item is suggested, although there can be a few items
without any specified loading. See 
LD 
logical; 
cati 
The set of dichotomous items in sequence number (i.e., 1 to J);

PPMC 
logical; 
burn 
Number of burnin iterations before posterior sampling. 
iter 
Number of formal iterations for posterior sampling (> 0). 
update 
Number of iterations to update the sampling information. 
missing 
Value for missing data (default is 
rseed 
An integer for the random seed. 
digits 
Number of significant digits to print when printing numeric values. 
alas 
logical; for adaptive Lasso or not. The default is 
verbose 
logical; to display the sampling information every

pcirm
returns an object of class lawbl
with item intercepts. It contains a lot of information about
the posteriors that can be summarized using summary.lawbl
.
Chen, J. (2020). A partially confirmatory approach to the multidimensional item response theory with the Bayesian Lasso. Psychometrika. 85(3), 738774. DOI:10.1007/s11336020097243.
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  ####################################
# Example 1: Estimation with LD #
####################################
dat < sim24ccfa21$dat
J < ncol(dat)
K < 3
Q<matrix(1,J,K);
Q[1:8,1]<Q[9:16,2]<Q[17:24,3]<1
m0 < pcirm(dat = dat, Q = Q, LD = TRUE, cati = 1, burn = 2000,iter = 2000)
summary(m0) # summarize basic information
summary(m0, what = 'qlambda') #summarize significant loadings in pattern/Qmatrix format
summary(m0, what = 'offpsx') #summarize significant LD terms
####################################
# Example 2: Estimation with LD #
####################################
Q<cbind(Q,1);
Q[15:16,4]<1
m1 < pcirm(dat = dat, Q = Q, LD = FALSE, cati = 1, burn = 2000,iter = 2000)
summary(m1) # summarize basic information
summary(m1, what = 'qlambda') #summarize significant loadings in pattern/Qmatrix format
summary(m1, what = 'offpsx') #summarize significant LD terms

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