This is an example output object from running the
fit.model.byaes
function by using the Bayesian estimator option. It is a result of calibrating
an oral reading fluency data set from Nese and Kamata (2014-2018) with
xxxx students on 150 passages.
Format
two lists: $pass.param with 150 rows and $hyper.param with 4 variables
$pass.param
- a
parameter controlling binomial success probabilities
- b
parameter controlling binomial success probabilities
- alpha
parameter controlling reading times
- beta
parameter controlling reading times
- se_a
standard error of a
- se_b
standard error of b
- se_alpha
standard error of alpha
- se_beta
standard error of beta
- passage.id
passage ID
- nwords.p
the total
$hyper.param
- vartau
variance of latent reading ability tau
- rho
correlation between two latent variables
- se_vartau
standard error of vartar
- se_rho
standard error of rho
References
Nese, J. F. T. & Kamata, A. (2014-2018). Measuring Oral Reading Fluency: Computerized Oral Reading Evaluation (Project No. R305A140203) [Grant]. Institute of Education Sciences, U.S. Department of Education. https://ies.ed.gov/funding/grantsearch/details.asp?ID=1492