lsirm2pl_normal_fixed_gamma {lsirm12pl}

2PL LSIRM fixing gamma to 1 with normal likelihood

Description

lsirm2pl_normal_fixed_gamma is used to fit 2PL LSIRM with gamma fixed to 1 for continuous variable. lsirm2pl_normal_fixed_gamma factorizes item response matrix into column-wise item effect, row-wise respondent effect and further embeds interaction effect in a latent space. Unlike 1PL model, 2PL model assumes the item effect can vary according to respondent, allowing additional parameter multiplied with respondent effect. The resulting latent space provides an interaction map that represents interactions between respondents and items.

Usage

lsirm2pl_normal_fixed_gamma(
  data,
  ndim = 2,
  niter = 15000,
  nburn = 2500,
  nthin = 5,
  nprint = 500,
  jump_beta = 0.4,
  jump_theta = 1,
  jump_alpha = 1,
  jump_z = 0.5,
  jump_w = 0.5,
  pr_mean_beta = 0,
  pr_sd_beta = 1,
  pr_mean_theta = 0,
  pr_mean_alpha = 0.5,
  pr_sd_alpha = 1,
  pr_a_theta = 0.001,
  pr_b_theta = 0.001,
  pr_a_eps = 0.001,
  pr_b_eps = 0.001,
  verbose = FALSE
)

Arguments

data

Matrix; a binary or continuous item response matrix for analysis. Each row represents a respondent, and each column contains responses to the corresponding item.

ndim

Integer; the dimension of the latent space. Default is 2.

niter

Integer; the total number of MCMC iterations to run. Default is 15000.

nburn

Integer; the number of initial MCMC iterations to discard as burn-in. Default is 2500.

nthin

Integer; the number of MCMC iterations to thin. Default is 5.

nprint

Integer; the interval at which MCMC samples are displayed during execution. Default is 500.

jump_beta

Numeric; the jumping rule for the beta proposal density. Default is 0.4.

jump_theta

Numeric; the jumping rule for the theta proposal density. Default is 1.0.

jump_alpha

Numeric; the jumping rule for the alpha proposal density. Default is 1.0.

jump_z

Numeric; the jumping rule for the z proposal density. Default is 0.5.

jump_w

Numeric; the jumping rule for the w proposal density. Default is 0.5.

pr_mean_beta

Numeric; the mean of the normal prior for beta. Default is 0.

pr_sd_beta

Numeric; the standard deviation of the normal prior for beta. Default is 1.0.

pr_mean_theta

Numeric; the mean of the normal prior for theta. Default is 0.

pr_mean_alpha

Numeric; the mean of the log normal prior for alpha. Default is 0.5.

pr_sd_alpha

Numeric; the standard deviation of the log normal prior for alpha. Default is 1.0.

pr_a_theta

Numeric; the shape parameter of the inverse gamma prior for the variance of theta. Default is 0.001.

pr_b_theta

Numeric; the scale parameter of the inverse gamma prior for the variance of theta. Default is 0.001.

pr_a_eps

Numeric; the shape parameter of inverse gamma prior for variance of data likelihood. Default is 0.001.

pr_b_eps

Numeric; the scale parameter of inverse gamma prior for variance of data likelihood. Default is 0.001.

verbose

Logical; If TRUE, MCMC samples are printed for each nprint. Default is FALSE.

Details

lsirm2pl_normal_fixed_gamma models the continuous value of response by respondent j to item i with item effect \beta_i, respondent effect \theta_j and the distance between latent position w_i of item i and latent position z_j of respondent j in the shared metric space. For 2pl model, the the item effect is assumed to have additional discrimination parameter \alpha_i multiplied by \theta_j:

Y_{j,i} = \theta_j+\beta_i-\gamma||z_j-w_i|| + e_{j,i}

where the error e_{j,i} \sim N(0,\sigma^2)

Value

lsirm2pl_normal_fixed_gamma returns an object of list containing the following components:

data

A data frame or matrix containing the variables used in the model.

bic

A numeric value representing the Bayesian Information Criterion (BIC).

mcmc_inf

Details about the number of MCMC iterations, burn-in periods, and thinning intervals.

map_inf

The log maximum a posteriori (MAP) value and the iteration number at which this MAP value occurs.

beta_estimate

Posterior estimates of the beta parameter.

theta_estimate

Posterior estimates of the theta parameter.

sigma_theta_estimate

Posterior estimates of the standard deviation of theta.

z_estimate

Posterior estimates of the z parameter.

w_estimate

Posterior estimates of the w parameter.

beta

Posterior samples of the beta parameter.

theta

Posterior samples of the theta parameter.

theta_sd

Posterior samples of the standard deviation of theta.

z

Posterior samples of the z parameter, represented as a 3-dimensional matrix where the last axis denotes the dimension of the latent space.

w

Posterior samples of the w parameter, represented as a 3-dimensional matrix where the last axis denotes the dimension of the latent space.

accept_beta

Acceptance ratio for the beta parameter.

accept_theta

Acceptance ratio for the theta parameter.

accept_z

Acceptance ratio for the z parameter.

accept_w

Acceptance ratio for the w parameter.

sigma_estimate

Posterior estimates of the standard deviation.

sigma

Posterior samples of the standard deviation.

alpha_estimate

Posterior estimates of the alpha parameter.

alpha

Posterior estimates of the alpha parameter.

accept_alpha

Acceptance ratio for the alpha parameter.

Examples

# generate example (continuous) item response matrix
data     <- matrix(rnorm(500, mean = 0, sd = 1),ncol=10,nrow=50)

lsrm_result <- lsirm2pl_normal_fixed_gamma(data)

# The code following can achieve the same result.
lsirm_result <- lsirm(data ~ lsirm2pl(spikenslab = FALSE, fixed_gamma = TRUE))