View source: R/classification.R
classification_agreement_index | R Documentation |
Calculate agreement index
classification_agreement_index( true_score, estimated_score, cut_scores, cat_labels = NULL )
true_score |
A numeric vector of examinees' true score values. Values can be in theta scale or summed scores. |
estimated_score |
A numeric vector of examinees' estimated score values. Values can be in theta scale or summed scores. |
cut_scores |
A sorted (ascending order) numeric vector of cut score values. Values can be in theta scale or summed scores. Do not include 0 or the maximum possible score of the test. |
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:
ca_table
A classification table where rows are true categories and columns are estimated categories. The values are the number of examinees.
ca_table
A classification table where rows are true categories and columns are estimated categories. The values are the proportion of examinees.
ca
Classification agreement index
Emre Gonulates
ip <- generate_ip(model = sample(c("GPCM", "2PL"), 20, TRUE)) n_examinee <- 1000 true_theta <- rnorm(n_examinee) observed_theta <- true_theta + runif(n_examinee, -.5, .5) theta_cs <- c(-1, 0, 1.5) raw_cs <- round(rsss(ip = ip, scale_score = theta_cs)) true_raw_score <- round(rsss(ip = ip, scale_score = true_theta)) observed_raw_score <- round(rsss(ip = ip, scale_score = observed_theta)) # Theta scores classification_agreement_index(true_score = true_theta, estimated_score = observed_theta, cut_scores = theta_cs) # Summed scores classification_agreement_index(true_score = true_raw_score, estimated_score = observed_raw_score, cut_scores = raw_cs) # Add labels classification_agreement_index(true_score = true_theta, estimated_score = observed_theta, cut_scores = theta_cs, cat_labels = c("Unsatisfactory", "Basic", "Mastery", "Advanced"))
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