rsc2: Estimation of the one-parameter RSC model, with latent traits...
In peterhalpin/cirt: Psychometric models of small group collaborations

Description Usage Arguments Details Value

This function calls optim to estimate the one-parameter RSC model from the (conjunctively-scored) repsonses of dyads to a group assessment.

1 2	rsc2(resp, parms, theta1, theta2, method = "MAP", obs = F, sigma = 3, parallel = F)

`resp`	a matrix or data.frame containing the (conjunctively-scored) binary item responses.
`parms`	a data.frame with columns `parms$alpha` and `parms$beta` corresponding to the discrimination and difficulty parameters of the 2PL model, respectively.
`theta1`	the latent trait of member 1.
`theta2`	the latent trait of member 2.
`method`	one of `c("ML", "MAP")`. The latter isrecommended.
`obs`	logical: should standard errors be computed using the observed (`TRUE`) or expected (`FALSE`) Fisher information?
`sigma`	prior standard deviation for logit of weight.
`parallel`	logical: call `parallel:mclapply` instead of looping over `nrow(resp)`?

Estimation is via either maximum likelihood (ML) or modal a'posteriori (MAP), with the latter being prefered. For MAP, a two-parameter Beta prior is used with the parameter of the RSC model, in which both parameters are equal to 1 + epsilon. Standard errors (or posterior standard deviations) are computed via the inverse of the analytically computed second derivatives of the objective function, at the parameter estimates. The value of the objective function at the estimate is is also provided. If parallel = T, the call to optim is parallelized via parallel::mclapply.

An named nrow(resp) by 3 data.frame containing the estimates, their standard errors, and the value of the log-likelihood of the RSC model at the solution (not the log posterior with MAP).

peterhalpin/cirt documentation built on May 25, 2019, 1:50 a.m.