var_rsc: Computes variance of latent traits and the one-parameter RSC...

In peterhalpin/BearShare: Psychometric models of small group collaborations

Description

Computes variance of latent traits and the one-parameter RSC model for a combined assessment. #' @param resp a data.frame containing the binary item responses of both the individual assessment and the (conjunctively scored) group assessment. See details of rsc for information on formatting.

Usage

var_rsc(resp, u, parms, theta1, theta2, method = "MAP", obs = F,
  sigma = 3)

Arguments

u	the logit of the weight parameter of the RSC model.
parms	a data.frame with columns parms$alpha and parms$beta corresponding to the discrimination and difficulty parameters of the 2PL model, respectively. See details of rsc for information on formatting.
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) Hessian?
sigma	prior standard deviation for logit of weight.

Value

3 * K by 3 * K covariance matrix of the parameter estimates, with K = length(u) and rows/cols ordered as rep(c(u_k, theta1_k, theta2_k), times = K).

peterhalpin/BearShare documentation built on May 25, 2019, 12:48 a.m.