View source: R/classification.R
classification_indices  R Documentation 
Calculate classification accuracy and consistency
classification_indices(
method = "recursive",
ip = NULL,
theta = NULL,
theta_cs = NULL,
raw_cs = NULL,
resp = NULL,
se = NULL,
perf_categories = NULL,
n_theta = 100,
theta_lower_bound = 6,
theta_upper_bound = 6,
cat_labels = NULL
)
method 
The method of classification accuracy and consistency calculation method. Following methods are available:

ip 
An 
theta 
A numeric vector representing the abilities of examinees.
Required for 
theta_cs 
A sorted (ascending order) numeric vector representing the
theta scale cut scores. Do not include 
raw_cs 
A sorted (ascending order) numeric vector of summedscore cut score values. Do not include 0 or the maximum possible score of the test in this vector. Required for 'recursive' method if 'theta_cs' is not provided. 
resp 
A 
se 
A numeric vector representing the standard errors of ability
estimates. Required for 
perf_categories 
An integer vector representing the performance
categories of examinees. The number 1 should represent the lowest category.
For example if there are three cut scores the valid values can only be: 0,
1, 2 and 3. This vector will be used 
n_theta 
An integer representing the number of equally spaced theta
points between cut scores. The default value is 100. Use larger values to
increase accuracy but larger numbers will also slow the speed of
calculation. Can optionally be provided for the 
theta_lower_bound 
A number representing the lower bound for cut
scores. The default value is 6. Can optionally be provided for the

theta_upper_bound 
A number representing the upper bound for cut
scores. The default value is 6. Can optionally be provided for the

cat_labels 
A string vector representing the labels of the categories.
The length of the vector should be one more than the length of the cut
scores. The default value is 
A list of following elements:
category_prob
A numeric vector representing the performance category classification probabilities of each examinee.
ca
Marginal (overall) classification accuracy index
cc
Marginal (overall) classification consistency index
ind_cs_ca
Individual cut score classification accuracy indices. This value will only be calculated when there are more than one cut score.
ind_cs_ca
Individual cut score classification consistency indices. This value will only be calculated when there are more than one cut score.
Emre Gonulates
Guo, F. (2006). Expected classification accuracy using the latent distribution. Practical Assessment, Research, and Evaluation, 11(1), 6.
Lee, W. C. (2010). Classification consistency and accuracy for complex assessments using item response theory. Journal of Educational Measurement, 47(1), 117.
Rudner, L. M. (2000). Computing the expected proportions of misclassified examinees. Practical Assessment, Research, and Evaluation, 7(1), 14.
Rudner, L. M. (2005). Expected classification accuracy. Practical Assessment, Research, and Evaluation, 10(1), 13.
Wyse, A. E., & Hao, S. (2012). An evaluation of item response theory classification accuracy and consistency indices. Applied Psychological Measurement, 36(7), 602624.
ip < generate_ip(model = sample(c("GPCM", "2PL"), 20, TRUE))
n_examinee < 100
true_theta < rnorm(n_examinee)
resp_set < generate_resp_set(ip = ip, theta = true_theta, prop_missing = .2)
theta_est < est_ability(resp = resp_set, ip = ip, method = "eap")
se < theta_est$se
theta_est < theta_est$est
raw_score < est_ability(resp = resp_set, method = "sum_score")$est
# Cut score
theta_cs < c(1, 0, 1.5)
raw_cs < round(rsss(ip = ip, scale_score = theta_cs))
# Rudner (2000, 2005) based indices:
classification_indices(method = "rudner", theta = theta_est, se = se,
theta_cs = theta_cs)
# Guo (2006) based indices:
classification_indices(method = "guo", ip = ip, resp = resp_set,
theta = theta_est, theta_cs = theta_cs)
# Recursive method based indices:
classification_indices(method = "recursive", ip = ip, theta = theta_est,
theta_cs = theta_cs)
# Use raw score cut scores with recursive method
classification_indices(method = "recursive", ip = ip, theta = theta_est,
raw_cs = raw_cs)
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