View source: R/ability_estimation.R
est_ability | R Documentation |
est_ability
estimates ability using various methods such as
Owen's Bayesian estimation, Maximum Likelihood estimation,
Expected-a-Posteriori.
est_ability( resp, ip = NULL, method = c("eap", "ml", "map", "bm", "owen", "sum_score"), ..., prior_dist = c("norm", "unif", "lnorm", "gamma", "t", "cauchy"), prior_pars = c(0, 1), theta_range = c(-5, 5), number_of_quads = 41, tol = 1e-06, output_type = c("list", "data.frame", "tibble") )
resp |
A |
ip |
An |
method |
The method that will be used to estimate the ability.
The default value is Current methods are:
|
... |
Additional arguments passed to specific methods |
prior_dist |
The shape of the prior distribution. Currently following distributions can be specified:
Default value is |
prior_pars |
Parameters of the prior distribution. Default value is
If method is |
theta_range |
The limits of the ability estimation scale. The estimation
result will be limited to this interval. The default is |
number_of_quads |
Number of quadratures. The default value is 41. As
this number increases, the precision of the estimate will also increase.
The default value is |
tol |
The precision level of ability estimate. The final ability
estimates will be rounded to remove the precision that is smaller than the
|
output_type |
A string that specifies the output type of the function.
The default value is
|
est
The ability estimated. If the response vector for a
subject contains all NA
s, then, in order to differentiate all
incorrect and all NA, the est
returned will be NA.
se
The standard error(s) of the ability estimate(s). For
"sum_score"
method, all of the standard errors will be NA
.
For Bayesian methods (like EAP or Owen's) this value is the square root
of the posterior variance.
Emre Gonulates
Owen, R. J. (1975). A Bayesian sequential procedure for quantal response in the context of adaptive mental testing. Journal of the American Statistical Association, 70(350), 351-356.
Vale, C. D., & Weiss, D. J. (1977). A Rapid Item-Search Procedure for Bayesian Adaptive Testing. Research Report 77-4. Minneapolis, MN.
ip <- generate_ip(n = 7) resp <- sim_resp(ip, theta = rnorm(3)) ### EAP estimation ### est_ability(resp, ip) est_ability(resp, ip, number_of_quads = 81) # The default prior_dist is 'norm'. prior_pars = c(mean, sd) est_ability(resp, ip, prior_pars = c(0, 3)) # prior_pars = c(min, max) est_ability(resp, ip, prior_dist = 'unif', prior_pars = c(-3, 3)) # prior_pars = c(df) est_ability(resp, ip, prior_dist = 't', prior_pars = 3) # prior_pars = c(location, scale) est_ability(resp, ip, prior_dist = 'cauchy', prior_pars = c(0, 1)) ### MAP estimation (Bayes Modal estimation) ### est_ability(resp, ip, method = "map") # The default prior_dist is 'norm'. prior_pars = c(mean, sd) est_ability(resp, ip, method = "map", prior_pars = c(0, 2)) ### Maximum Likelihood estimation ### est_ability(resp, ip, method = 'ml') est_ability(resp, ip, method = 'ml', tol = 1e-8) est_ability(resp = rep(1, length(ip)), ip, method = 'ml') est_ability(resp = rep(1, length(ip)), ip, method = 'ml', theta_range = c(-3, 3)) ### Owen's Bayesian ability estimation ### est_ability(resp, ip, method = 'owen') est_ability(resp, ip, method = 'owen', prior_pars = c(0, 3))
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