dlp: Gradient of the log of the prior distribution for a combined...

In peterhalpin/BearShare: Psychometric models of small group collaborations

Description

This function computes the derivative of the log of the prior for a combined assessment, in which the 2PL model is used for the individual component of the assessment and the one-parameter RSC model is used for the group component of the assessment. A standard normal prior is used for individual ability, and for the logit of the weight of the RSC model a normal prior is used with standard deviation sigma.

Usage

dlp(u, theta1, theta2, sigma = 3)

Arguments

u	the logit of the weight parameter of the RSC model.
theta1	the latent trait of member 1.
theta2	the latent trait of member 2.
sigma	prior standard deviation for logit of weight.

Value

3 * K - vector of first derivatives, with K = length(u), ordered as rep(c(u_k, theta1_k, theta2_k), times = K).

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