Package {BoundIRT}

The 'BoundIRT' package.

Description

Bounded continuous data are encountered in many areas of test application. Examples include visual analogue scales used in the measurement of personality, mood, depression, and quality of life; item response times from tests with item deadlines; confidence ratings; and pain intensity ratings. Using this package, item response theory (IRT) models suitable for bounded continuous item scores can be fitted to data within a Bayesian framework. The package draws on posterior sampling facilities provided by R-package 'rstan' (Stan Development Team, 2025). Available models include the Beta IRT model by Noel and Dauvier (2007), the continuous response model by Samejima (1973), the unbounded normal model by Mellenbergh (1994), and the Simplex IRT model by Flores et al. (2020). All models can be fitted with or without zero-one inflation (Molenaar et al., 2022). Model fit comparisons can be conducted using the Watanabe–Akaike information criterion (WAIC), the leave-one-out cross-validation information criterion (LOOIC), and the fully marginalized likelihood (i.e., Bayes factors).

Author(s)

Maintainer: Dylan Molenaar d.molenaar@uva.nl

References

Flores, S., Bazan, J.L., & Bolfarine, H. (2020). A hierarchical joint model for bounded response time and response accuracy. In M. Wiberg, D. Molenaar, J. González,U. Bockenholt, & K. S. Kim (Eds.), Quantitative psychology: The 84th Annual Meeting of the Psychometric Society, Santiago de Chile, Chile (pp. 95-109). Springer. doi:10.1007/978-3-030-43469-4_8

Mellenbergh, G. J. (1994). A unidimensional latent trait model for continuous item responses. Multivariate Behavioral Research, 29(3), 223-236. doi:10.1207/s15327906mbr2903_2

Molenaar, D., Cúri, M., & Bazán, J.L. (2022). Zero and One Inflated Item Response Theory Models for Bounded Continuous Data. Journal of Educational and Behavioral Statistics, 47, 693-735. doi:10.3102/10769986221108455

Noel, Y., & Dauvier, B. (2007). A beta item response model for continuous bounded responses. Applied Psychological Measurement, 31(1), 47-73. doi:10.1177/0146621605287691

Samejima, F. (1973). Homogeneous case of the continuous response model. Psychometrika, 38(2), 203-219.doi:10.1007/BF02291114

Stan Development Team (2025). RStan: the R interface to Stan. R package version 2.32.7. https://mc-stan.org/

Adjectives Check List data

Description

This dataset contains the bounded continuous items responses of 244 subjects to 218 items from 22 subscales of the Adjectives Check List (ACL; Gough & Heilbrun, 1980) including the covariate female which is code 0 and 1

Format

The item scores are in object ACL and have originally been scored on a 60 milimeter line segment on which the subjects had to indicate to what degree each adjective applies to them. These scores have been recoded into a [0,1] interval. The Abasement scale does not contain 1 scores but it does contain 0 scores. The item content (adjectives) are in the column names of the data matrix. The responses from items with an * in the item content are contra indicative items and have been reversly coded.

The object ACL_index contains an index matrix with each i-th row corresponding to the i-th column in the ACL data matrix. In the index matrix it is indicated for each item which of the 22 ACL factors it is supposed to measure. The factor numbering is as follows:

1. Communality

2. Achievement

3. Dominance

4. Endurance

5. Order

6. Intraception

7. Nurturance

8. Affiliation

9. Exhibition

10. Autonomy

11. Aggression

12. Change

13. Succorance

14. Abasement

15. Deference

16. Personal Adjustment

17. Ideal Self

18. Critical Parent

19. Nurturant Parent

20. Adult

21. Free Child

22. Adapted Child

All subscales of the ACL have been analyzed using bounded continuous IRT models with zero-one inflation by Molenaar et al. (2022; see Table 10 for the model fit statistics of all ACL subscales and see Table 11 for the parameter estimates).

For ease of the examples in this package, the item responses of the Abasement susbcale are added to a seperate dataset accesible via data(Abasement). In addition, a zero-inflated Beta IRT model has been pre-estimated on the Abasement scale. The fitBIRT output object containing the estimation results is accesible via data(out_beta). Note that the results by Molenaar et al. (2022; Tables 10 and 11) are based on a model with both zero and one inflation. Therefore, the estimates in out_beta and the subsequent model fit statistics obtained using bridgeBIRT are slightly different.

out_beta has been obtain using this code:

set.seed(1310)
data(Abasement)
out_beta = fitBIRT(Abasement, model="beta")

References

Gough, H. G., & Heilbrun, A. B. (1980). The adjective check list, manual 1980 Edition. Consulting Psychologists Press.

Molenaar, D., Cúri, M., & Bazán, J.L. (2022). Zero and One Inflated Item Response Theory Models for Bounded Continuous Data. Journal of Educational and Behavioral Statistics, 47, 693-735. doi:10.3102/10769986221108455

Examples

data(ACL)
# show the item responses for the items measuring the first ACL factor (Communality)
head(ACL[,ACL_index[,3]==1])

Estimate fully marginalized log-likelihood using bridge sampling.

Description

This function uses bridge sampling as discussed by Gronau et al. (2017) to obtain the fully marginalized likelihood of an estimated bounded IRT model.

Usage

bridgeBIRT(object,niter=10000,tol=1e-7,silent=FALSE)

Arguments

Details

In bridge sampling, a so-called bridge function is used to estimate the fully marginalized likelihood by combining existing posterior samples with additional samples drawn from a proposal distribution. In bridgeBIRT, a multivariate normal proposal distribution is used. Using these proposal samples, the final expression for the marginal likelihood involves an iterative procedure (see Equation 4.1 from Meng & Wong, 1996) which stops if the change in marginal likelihood between iterations does not exceed the tolerance.

To improve numerical stability and avoid overflow in the exp() function, we follow the approach described in Appendix B of Gronau et al. (2017). Specifically, the median log-likelihood value, l^\star, is subtracted from the log-marginal likelihood, and only the remaining component, r_1, is estimated. After the iterative scheme has converged, these two components are recombined to obtain the final estimate of the marginal likelihood.

Value

A list containing:

Author(s)

Dylan Molenaar d.molenaar@uva.nl

References

Gronau, Q. F., et al. (2017). A tutorial on bridge sampling. Journal of mathematical psychology, 81, 80-97. doi:10.1016/j.jmp.2017.09.005

Meng, X. L., & Wong, W. H. (1996). Simulating ratios of normalizing constants via a simple identity: a theoretical exploration. Statistica Sinica, 6, 831-860. https://www.jstor.org/stable/24306045

See Also

fitBIRT to estimate various bounded continuous IRT models to data.

plot.BoundIRT to plot the item and test information curves and predicted densities for the different models.

coef.BoundIRT to extract item parameter estimates from a BoundIRT object.

latregBIRT to conduct a latent regression of the person parameters on some observed predictor variables.

rBIRT simulate data according to a (zero/one inflated) bounded IRT model.

Examples

  
  # read in the pre-estimated object containing the Abasement results
  # for the Beta IRT model and estimate the marginal likelihood
  
  data(out_beta)
  bridgeBIRT(out_beta)
  
  

Obtain item parameter estimates

Description

Extracts a matrix of posterior means for the item parameters of a BoundIRT-object

Usage

## S3 method for class 'BoundIRT'
coef(object,sd=FALSE, ...)

Arguments

Value

A matrix containing the posterior mean (and standard deviations if sd=TRUE) of the item parameters from a bounded IRT model fit with the fitBIRT() function.

Author(s)

Dylan Molenaar d.molenaar@uva.nl

See Also

fitBIRT to estimate various bounded continuous IRT models to data.

plot.BoundIRT to plot the item and test information curves and predicted densities for the different models.

coef.BoundIRT to extract item parameter estimates from a BoundIRT object.

bridgeBIRT to use bridge sampling to calculate the marginal loglikelihood for a model in a BoundIRT object.

latregBIRT to conduct a latent regression of the person parameters on some observed predictor variables.

rBIRT simulate data according to a (zero and/or one inflated) bounded IRT model.

Examples

  # read in the pre-estimated object containing the Abasement results
  data(out_beta)

  coef(out_beta)

Fit various IRT models for bounded continuous items to data

Description

This function fits various IRT models for bounded continuous items using MCMC sampling in R-package 'rstan' (Stan Development Team, 2025). Available models include the Beta IRT model by Noel and Dauvier (2007), the continuous response model by Samejima (1973), the unbounded normal model by Mellenbergh (1994), and the Simplex IRT model by Flores et al. (2020). All models can be fitted with or without a zero and/or one inflation (Molenaar et al., 2022).

Usage

fitBIRT(z,
        model=c("beta","samejima","simplex","normal"),
        inflated=c("auto","zero","one","both","none"),
        iter=2000,
        warmup=NULL,
        nchains=1,
        prior=NULL,
        silent=FALSE,
        ...)

Arguments

Details

Function fitBIRT fits the models discussed by Molenaar et al. (2022) including the facilities for zero and/or one inflation. If \alpha_{i}, \beta_{i}, and \phi_{i} are respectively the discrimination, easiness, and dispersion parameters of item i, \theta_{p} is the latent variable position of subject p, and z_{pi} is the bounded continuous item score of subject p on item i, then the different models can be given as follows:

Beta model

z_{pi} \sim \text{Beta} (a_{pi},b_{pi})

a_{pi} = \exp(.5(\alpha_{i}\theta_{p}+\beta_{i} + \log(\phi_i)))

b_{pi} = \exp(.5(-\alpha_{i}\theta_{p}-\beta_{i} + \log(\phi_i)))

Samejima model

z_{pi}^{'} \sim \text{Normal} (\alpha_{i}\theta_{p}+\beta_{i},\phi_i)

z_{pi}^{'} = \text{logit}(z_{pi})

Simplex model

z_{pi} \sim \text{Simplex}(1/(1+\exp(-(\alpha_{i}\theta_{p}+\beta_{i}))),\phi_i)

Normal model

z_{pi} \sim \text{Normal} (\alpha_{i}\theta_{p}+\beta_{i},\phi_i)

Zero and/or one inflation

To these models, zero and/or one inflation can be added as follows:

A = P(z_{pi}=0|\theta_{p}) = 1/(1+\exp(-(\gamma_{0i}-\alpha_{i}\theta_{p})))

B = P(z_{pi}=1|\theta_{p}) = 1-1/(1+\exp(-(\gamma_{1i}-\alpha_{i}\theta_{p})))

where \gamma_{0i} and \gamma_{1i} model the amount of zero and one inflation respectively. The conventional models above are accommodated as follows:

f(z_{pi}|\theta_{p}) = A for z_{pi}=0

f(z_{pi}|\theta_{p}) = B for z_{pi}=1

f(z_{pi}|\theta_{p}) = [ (1-B) - A ] \times k(z_{pi}|\theta_{p}) for z_{pi}\in(0,1)

where k(.) is the distribution according to the conventional model above. See Molenaar et al. (2022) for details.

In estimating the models above, normal priors are specified for \theta_p, \log(\alpha_i), \beta_i, \log(\phi_i), \gamma_{0i}, \text{and } \gamma_{1i}. All priors have mean 0, and by default, a standard deviation of 1 for \theta_p (for identification purposes) and 10 for the other parameters. Changing (one of) the defaults involves specifying a list with elements sT, sA, sB, sP and/or sG, which overrides the default. It is thus possible to stick to the defaults, but change the prior standard deviation for \log\alpha_i and \beta_i by using for instance prior=list(sA=5,sB=5). Note that setting sG affects both the prior of \gamma_{0i} \text{ and } \gamma_{1i}.

Value

An object of class BoundIRT with values:

For the BoundIRT object, the following methods are available:

Author(s)

Dylan Molenaar d.molenaar@uva.nl

References

Flores, S., Bazan, J.L., & Bolfarine, H. (2020). A hierarchical joint model for bounded response time and response accuracy. In M. Wiberg, D. Molenaar, J. González, U. Bockenholt, & K. S. Kim (Eds.), Quantitative psychology: The 84th Annual Meeting of the Psychometric Society, Santiago de Chile, Chile (pp. 95-109). Springer. doi:10.1007/978-3-030-43469-4_8

Mellenbergh, G. J. (1994). A unidimensional latent trait model for continuous item responses. Multivariate Behavioral Research, 29(3), 223-236. doi:10.1207/s15327906mbr2903_2

Molenaar, D., Cúri, M., & Bazán, J.L. (2022). Zero and One Inflated Item Response Theory Models for Bounded Continuous Data. Journal of Educational and Behavioral Statistics, 47, 693-735. doi:10.3102/10769986221108455

Noel, Y., & Dauvier, B. (2007). A beta item response model for continuous bounded responses. Applied Psychological Measurement, 31(1), 47-73. doi:10.1177/0146621605287691

Samejima, F. (1973). Homogeneous case of the continuous response model. Psychometrika, 38(2), 203-219.doi:10.1007/BF02291114

Stan Development Team (2025). RStan: the R interface to Stan. R package version 2.32.7. https://mc-stan.org/

See Also

plot.BoundIRT to plot the item and test information curves and predicted densities for the different models.

coef.BoundIRT to extract item parameter estimates from a BoundIRT object.

loglikBIRT to calculate the log-likelihood for each person and each posterior sample. Needed to calculate WAIC and LOOIC for a given model.

repBIRT to simulate replicated datasets using (a subset of) the posterior parameter samples. For posterior predictive model checking.

bridgeBIRT to use bridge sampling to calculate the marginal loglikelihood for a model in a BoundIRT object.

latregBIRT to conduct a latent regression of the person parameters on some observed predictor variables.

rBIRT simulate data according to a (zero/one inflated) bounded IRT model.

Examples

 #load the Abasement data which contains zero inflation
 data(Abasement)

 # fit a zero inflated beta IRT model
 # for illustrative purposes, we only use 20 iterations.
 # In practice use many more!

 res=fitBIRT(Abasement,model="beta",iter=20)
 print(res)   # quick overview
 coef(res)   # extract posterior item parameter means
 summary(res,c("alpha","beta"))   # request the rstan summary

Multivariate latent regression with one or more BoundIRT latent variables and observed covariates

Description

This function conducts a factor score regression using latent and observed variables (if any). The latent variables need to be esimated with BoundIRT but they can differ in their measurement model, as long as they are estimated on subscales administered to the same sample of subjects.

Usage

latregBIRT(formula,
           observed,
           latent,
           method=c("bayes", "freq"),
           nboot=1000,
           prob=c(.025,.25,.50,.75,.975))

Arguments

Details

latregBIRT fits a multivariate regression model to the posterior latent variable means from one or more bounded continuous IRT models:

\bold{y}=\bold{Bx}+\epsilon

where \bold{y} and \bold{x} are vectors of respectively outcome and predictor variables, \bold{B} is a matrix of regression parameters, and \epsilon is a vector of residuals. Both \bold{y} and \bold{x} may contain one or more latent variables estimated on subsets of the item level data using function fitBIRT. The resulting BoundIRT-objects need to be collected in a list and provided to latregBIRT using argument latent. Using the posterior latent variable means and standard deviations, the regression model above is fit correcting for the posterior uncertainty in the latent variables. That is, as the latent variable posteriors are misspecified with respect to the predictor variables, they are only unbiased if the number of items approaches infinity. As applications of (bounded continuous) IRT models involve a finite number of items, the posterior covariance matrix of the latent variables and the predictor variables is adjusted for the nonzero variance in the latent variable posteriors. The correction is based on the two-step approach by Croon (2002) in which the factor scores from a linear factor model are estimated in step 1 and used in a regression model in step 2. As such, Croon's method is a statistically valid alternative to fitting the factor model and latent regression model simultaneously. Vermunt (2025) discusses how Croon's method can be generalized to models with heterogenous measurement error like IRT models. latregBIRT implements this idea by correcting the covariance matrix of the latent and observed variables using the formula from Vermunt (2025) and estimating the posterior distribution using Bayesian bootstrapping (or the sampling distribution in the case of method="freq".

Value

Author(s)

Dylan Molenaar d.molenaar@uva.nl

References

Croon, M. (2002). Using predicted latent scores in general latent structure models. In G. Marcoulides and I. Moustaki (Eds.), Latent Variable and Latent Structure Models (pp. 195-224). Lawrence Erlbaum.

Vermunt J.K. (2025). Stepwise estimation of latent variable models: An overview of approaches. Statistical Modelling, 25(6),530-551. doi:10.1177/1471082X251355693

See Also

fitBIRT to estimate various bounded continuous IRT models to data.

plot.BoundIRT to plot the item and test information curves and predicted densities for the different models.

coef.BoundIRT to extract item parameter estimates from a BoundIRT object.

loglikBIRT to calculate the log-likelihood for each person and each posterior sample. Needed to calculate WAIC and LOOIC for a given model.

bridgeBIRT to use bridge sampling to calculate the marginal loglikelihood for a model in a BoundIRT object.

rBIRT simulate data according to a (zero/one inflated) bounded IRT model.

Examples

 ######################################
 # EXAMPLE 1: Multiple regression     #
 ######################################

 # open pre-estimated Beta IRT model on Abasement data (see ?Abasement)
  data(out_beta)

 # simulate a latent regression with 2 covariates

 EAP=out_beta$theta[,1]
 N=length(EAP)
 age=.4*EAP+sqrt(1-.4^2)*rnorm(N)
 ses=.3*EAP+sqrt(1-.3^2)*rnorm(N)

 # conduct latent regression
 predictors=cbind(age,ses)
 latregBIRT(Abasement~age+ses,predictors,list(Abasement=out_beta))


 ######################################
 # EXAMPLE 2: Multivariate regression #
 ######################################
 
   # the following shows a more sophisticated latent regression model
   data(ACL)                               # see ?ACL for details about the data
   female = ACL[,1]
   Comm_items = ACL[,ACL_index[,3]==1]     # ACL factor: Communality
   Achiev_items = ACL[,ACL_index[,3]==2]   # ACL factor: Achievement
   Dom_items = ACL[,ACL_index[,3]==3]      # ACL factor: Dominance

   # fit measurement models to each of the subscales
   # (note, you can use different models for the different latent variables)
   # please also note that for illustrative purposes, only 20 mcmc samples are drawn.
   # in practice use (many) more

   set.seed(1234)
   Comm_res = fitBIRT(Comm_items, "beta",iter=20)
   Achiev_res = fitBIRT(Achiev_items, "samejima",iter=20)
   Dom_res = fitBIRT(Dom_items, "simplex",iter=20)

   observed=cbind(female)
   latent=list(Communality = Comm_res,
                Achievement = Achiev_res,
                Dominance = Dom_res)

   #conduct latent regression
   latreg_res = latregBIRT(Communality + Achievement ~ Dominance + female,
                           observed, latent)
   print(latreg_res)
   # cbind(Communality,Achievement) will also work
  

Calculate the log-likelihood values of each person for each posterior parameter sample

Description

To facilitate model fit checking (e.g., leave one out cross-validation, WAIC), this function can be used to calculate the log-likelihood values of each person for each posterior parameter sample in a BoundIRT-object.

Usage

loglikBIRT(object, marginal=TRUE,nquad=15, silent=FALSE)

Arguments

Details

For latent variable models like the ones in BoundIRT, Merkle et al., (2019) recommend the use of the likelihood function that is marginalized over the persons parameters (i.e., not over the item parameters) in calculating fit statistics like the WAIC and LOOIC. Using marginal=TRUE will output this marginal likelihood value for each person and each sample of the posterior. If the interest is in calculating the conventional fit measures (e.g., conditional WAIC and DIC), marginal=FALSE should be used to obtain the likelihood values for each person conditional on the person and item parameters of each posterior sample.

Value

The function returns a matrix of dimensions (nsamples) by (N) containing the (marginal) log-likelihood values of the subjects for each posterior sample.).

Author(s)

Dylan Molenaar d.molenaar@uva.nl

References

Gabry J, Simpson D, Vehtari A, Betancourt M, Gelman A (2019). Visualization in Bayesian workflow. Journal of the Royal Statistical Society, A, 182, 389-402. doi:10.1111/rssa.12378

Merkle EC, Furr D, Rabe-Hesketh S. (2019).Bayesian Comparison of Latent Variable Models: Conditional Versus Marginal Likelihoods. Psychometrika, 84(3), 802-829. doi:10.1007/s11336-019-09679-0

See Also

fitBIRT to estimate various bounded continuous IRT models to data.

plot.BoundIRT to plot the item and test information curves and predicted densities for the different models.

coef.BoundIRT to extract item parameter estimates from a BoundIRT object.

repBIRT to simulate replicated datasets using (a subset of) the posterior parameter samples. For posterior predictive model checking.

bridgeBIRT to use bridge sampling to calculate the marginal loglikelihood for a model in a BoundIRT object.

latregBIRT to conduct a latent regression of the person parameters on some observed predictor variables.

Examples

 #read in the prefitted results from the Abasement data (see ?Abasement and ?out_beta)
 data(out_beta)

 # Calculate marginal log-likelihood
 
   logL=loglikBIRT(out_beta)

   #logL can now be used for e.g.,model fit assessment using loo() from R-package loo

   #library loo should be available as it is part of rstan which is an import of BoundIRT
   if (requireNamespace("loo", quietly = TRUE)) {
     library(loo)
     loo(logL)   # gives the marginal LOOIC
     waic(logL) # gives the marginal WAIC
 }
 
 

Plotting function for BoundIRT-objects.

Description

This function can be used to produce item and test information curves, and the model implied densities of a fitted bounded continuous IRT model.

Usage

## S3 method for class 'BoundIRT'
plot(x,
     what=c("icc","iic","tic","density"),
     items=NULL,
     separate=FALSE,
     range=c(-3,3),
     by=.001,
     ymax=NULL,
     xlab=NULL,
     ylab=NULL,
     main=NULL,
     col=NULL,
     lwd_prob=2,
     ...)

Arguments

.

Details

Plotting the model implied densities is relatively slow due to the numerical integration involved in marginalizing over the latent variable. In the presence of zero and/or one inflation, model implied densities inclulde the probability of zero and/or one scores by a vertical line. As this vertical line represents a probability and the rest of the plot is about density, the zero/one probabilities may be masked if density values are large even if the zero/one inflation is quite substantial. See Examples how to extract the zero/one probabilities in the case these are hard to see from the plot.

Value

A matrix containing x-axis values (either the latent variable or the observed variable) and the y-axis values for each item.

Author(s)

Dylan Molenaar d.molenaar@uva.nl

See Also

fitBIRT to estimate various bounded continuous IRT models to data.

coef.BoundIRT to extract item parameter estimates from a BoundIRT object.

loglikBIRT to calculate the log-likelihood for each person and each posterior sample. Needed to calculate WAIC and LOOIC for a given model.

repBIRT to simulate replicated datasets using (a subset of) the posterior parameter samples. For posterior predictive model checking.

bridgeBIRT to use bridge sampling to calculate the marginal loglikelihood for a model in a BoundIRT object.

latregBIRT to conduct a latent regression of the person parameters on some observed predictor variables.

rBIRT simulate data according to a (zero/one inflated) bounded IRT model.

Examples

 #load results for zero inflated beta IRT model fit to the Abasement data
 # see ?Abasement and ?out_beta
 data(out_beta)

 # plots for item 1
 oldpar=par(mfrow=c(2,2))
 plot(out_beta,what="icc",items=1)
 plot(out_beta,what="iic",items=1)
 plot(out_beta,what="tic",items=1)
 den_plot=plot(out_beta,what="density",items=1)
 par(oldpar)

 #extract zero probabilities (there are no ones in the Abasement data)
 den_plot$out[1]        # zero probabilities

Simulate data according to various IRT models for bounded continuous items

Description

This function simulates data according to various IRT models for bounded continuous items. Available models include the Beta IRT model by Noel and Dauvier (2007), the continuous response model by Samejima (1973), the unbounded normal model by Mellenbergh (1994), and the Simplex IRT model by Flores et al. (2020). All models can be simulated with or without zero and/or one inflation (Molenaar et al., 2022).

Usage

rBIRT(N,nit,alpha,beta,phi,theta,
      gamma0=NULL,
      gamma1=NULL,
      model=c("beta","samejima","simplex","normal"))

Arguments

Details

If arguments gamma0 and gamma1 are not provided, data is simulated according to the conventional models. Zero and one inflation can be introduced seperately by only providing gamma0 or gamma1, or zero and one inflation can both be introduced by specifying both gamma0 and gamma1. If the interest is in simulating some -but not all- items with zero and/or one inflation, the uninflated items can be coded as -Inf in gamma0 and Inf in gamma1. See fitBIRT for the exact parameterization of the models.

Value

A matrix of size N by nit containing the simulated item responses.

Author(s)

Dylan Molenaar d.molenaar@uva.nl

References

Flores, S., Bazan, J.L., & Bolfarine, H. (2020). A hierarchical joint model for bounded response time and response accuracy. In M. Wiberg, D. Molenaar, J. González, U. Bockenholt, & K. S. Kim (Eds.), Quantitative psychology: The 84th Annual Meeting of the Psychometric Society, Santiago de Chile, Chile (pp. 95-109). Springer. doi:10.1007/978-3-030-43469-4_8

Mellenbergh, G. J. (1994). A unidimensional latent trait model for continuous item responses. Multivariate Behavioral Research, 29(3), 223-236. doi:10.1207/s15327906mbr2903_2

Molenaar, D., Cúri, M., & Bazán, J.L. (2022). Zero and One Inflated Item Response Theory Models for Bounded Continuous Data. Journal of Educational and Behavioral Statistics, 47, 693-735. doi:10.3102/10769986221108455

Noel, Y., & Dauvier, B. (2007). A beta item response model for continuous bounded responses. Applied Psychological Measurement, 31(1), 47-73. doi:10.1177/0146621605287691

Samejima, F. (1973). Homogeneous case of the continuous response model. Psychometrika, 38(2), 203-219.doi:10.1007/BF02291114

Stan Development Team (2025). RStan: the R interface to Stan. R package version 2.32.7. https://mc-stan.org/

See Also

fitBIRT to estimate various bounded continuous IRT models to data.

plot.BoundIRT to plot the item and test information curves and predicted densities for the different models.

coef.BoundIRT to extract item parameter estimates from a BoundIRT object.

loglikBIRT to calculate the log-likelihood for each person and each posterior sample. Needed to calculate WAIC and LOOIC for a given model.

repBIRT to simulate replicated datasets using (a subset of) the posterior parameter samples. For posterior predictive model checking.

bridgeBIRT to use bridge sampling to calculate the marginal loglikelihood for a model in a BoundIRT object.

latregBIRT to conduct a latent regression of the person parameters on some observed predictor variables.

Examples

 # data sim with 100 subjects and 10 items according to
 # beta IRT model with zero and one inflation

 nit=10
 N=100
 beta=runif(nit,.5,1)
 alpha=runif(nit,.5,.7)
 gamma0=runif(nit,-3,-1.5)
 gamma1=runif(nit,1.5,3)

 theta=rnorm(N)

 phi=seq(1.5,2.5,length=nit)
 z=rBIRT(N,nit,alpha,beta,phi,theta,gamma0,gamma1,model="beta")

Simulate replicated datasets using the posterior parameter samples from a BoundIRT-object

Description

To facilitate posterior predictive model checking, this function can be used to simulate replicated datasets for each of the samples from the posterior parameter distribution in a BoundIRT-object.

Usage

repBIRT(object, nrep=1000, silent=FALSE)

Arguments

Details

repBIRT is a wrapper around rBIRT extracting the posterior parameter samples, and simulating data for a subset of the samples. That is, for many posterior predictive purposes, not all posterior parameter samples are needed. Therefore, only the last nrep samples will be used. Setting nrep to be equal to the total number of posterior samples will use all of them.

Value

An array of size nrep by N by nit containing the replicated datasets.

Author(s)

Dylan Molenaar d.molenaar@uva.nl

References

Gabry J, Simpson D, Vehtari A, Betancourt M, Gelman A (2019). Visualization in Bayesian workflow. Journal of the Royal Statistical Society, A, 182, 389-402. doi:10.1111/rssa.12378

See Also

fitBIRT to estimate various bounded continuous IRT models to data.

plot.BoundIRT to plot the item and test information curves and predicted densities for the different models.

coef.BoundIRT to extract item parameter estimates from a BoundIRT object.

loglikBIRT to calculate the log-likelihood for each person and each posterior sample. Needed to calculate WAIC and LOOIC for a given model.

bridgeBIRT to use bridge sampling to calculate the marginal loglikelihood for a model in a BoundIRT object.

latregBIRT to conduct a latent regression of the person parameters on some observed predictor variables.

Examples

 #load the Abasement data and fitting results for the Beta IRT model
 data(Abasement)
 data(out_beta)

 #generate 10 replicated datasets
 z_rep=repBIRT(out_beta,nrep=10)

 #these can be used for e.g., posterior predictive checks
 #using the suggested bayesplot library
 if (requireNamespace("bayesplot", quietly = TRUE)) {
   bayesplot::ppc_dens_overlay(Abasement[,1],z_rep[,,1])  #for item 1
   bayesplot::ppc_dens_overlay(Abasement[,1],z_rep[,,2])  #for item 2, etc.
 }