prob | R Documentation |
prob
Returns the probability of correct respond to an
item or multiple items with given parameters for a given ability or
abilities, i.e. θ. For polytomous models, where there are
multiple possible responses, probability of each response category will be
returned.
prob(ip, theta, derivative = 0) ## S4 method for signature 'Item' prob(ip, theta, derivative = 0) ## S4 method for signature 'Rasch' prob(ip, theta, derivative = 0) ## S4 method for signature ''1PL'' prob(ip, theta, derivative = 0) ## S4 method for signature ''2PL'' prob(ip, theta, derivative = 0) ## S4 method for signature ''3PL'' prob(ip, theta, derivative = 0) ## S4 method for signature ''4PL'' prob(ip, theta, derivative = 0) ## S4 method for signature 'GRM' prob(ip, theta, derivative = 0) ## S4 method for signature 'PCM' prob(ip, theta, derivative = 0) ## S4 method for signature 'GPCM' prob(ip, theta, derivative = 0) ## S4 method for signature 'GPCM2' prob(ip, theta, derivative = 0) ## S4 method for signature 'Itempool' prob(ip, theta, derivative = 0) ## S4 method for signature 'Testlet' prob(ip, theta, derivative = 0) ## S4 method for signature 'numMatDfListChar' prob(ip, theta, derivative = 0)
ip |
An |
theta |
An object containing the ability parameters. |
derivative |
Whether to calculate the first or second derivative of probability of a response.
|
Item probabilities at given theta will be returned.
Emre Gonulates
theta <- rnorm(1) item1 <- generate_item(model = "Rasch") # Probability of correct response prob(item1, theta) # First derivative of probability of correct response: prob(item1, theta, derivative = 1) # Second derivative of probability of correct response: prob(item1, theta, derivative = 2) # Multiple theta values theta_n <- rnorm(5) prob(item1, theta_n) prob(item1, theta_n, derivative = 1) prob(item1, theta_n, derivative = 2) theta <- rnorm(1) item1 <- generate_item(model = "1PL") # Probability of correct response prob(item1, theta) # First derivative of probability of correct response: prob(item1, theta, derivative = 1) # Second derivative of probability of correct response: prob(item1, theta, derivative = 2) # Multiple theta values theta_n <- rnorm(5) prob(item1, theta_n) prob(item1, theta_n, derivative = 1) prob(item1, theta_n, derivative = 2) theta <- rnorm(1) item1 <- generate_item(model = "2PL") # Probability of correct response prob(item1, theta) # First derivative of probability of correct response: prob(item1, theta, derivative = 1) # Second derivative of probability of correct response: prob(item1, theta, derivative = 2) # Multiple theta values theta_n <- rnorm(5) prob(item1, theta_n) prob(item1, theta_n, derivative = 1) prob(item1, theta_n, derivative = 2) theta <- rnorm(1) item1 <- generate_item(model = "3PL") # Probability of correct response prob(item1, theta) # First derivative of probability of correct response: prob(item1, theta, derivative = 1) # Second derivative of probability of correct response: prob(item1, theta, derivative = 2) # Multiple theta values theta_n <- rnorm(5) prob(item1, theta_n) prob(item1, theta_n, derivative = 1) prob(item1, theta_n, derivative = 2) theta <- rnorm(1) item1 <- generate_item(model = "4PL") # Probability of correct response prob(item1, theta) # First derivative of probability of correct response: prob(item1, theta, derivative = 1) # Second derivative of probability of correct response: prob(item1, theta, derivative = 2) # Multiple theta values theta_n <- rnorm(5) prob(item1, theta_n) prob(item1, theta_n, derivative = 1) prob(item1, theta_n, derivative = 2) theta <- rnorm(1) item1 <- generate_item(model = "GRM") # Probability of correct response prob(item1, theta) # First derivative of probability of correct response: prob(item1, theta, derivative = 1) # Multiple theta values theta_n <- rnorm(5) prob(item1, theta_n) prob(item1, theta_n, derivative = 1) item4 <- generate_item(model = "GRM", n_categories = 5) prob(item4, theta) # Partial Credit Model theta <- rnorm(1) item1 <- generate_item(model = "PCM") # Probability of correct response prob(item1, theta) # First derivative of probability of correct response: prob(item1, theta, derivative = 1) # Second derivative of probability of correct response: prob(item1, theta, derivative = 2) # Multiple theta values theta_n <- rnorm(5) prob(item1, theta_n) prob(item1, theta_n, derivative = 1) prob(item1, theta_n, derivative = 2) item3 <- generate_item(model = "GPCM2", n_categories = 3) prob(item3, theta) theta <- rnorm(1) item1 <- generate_item(model = "GPCM") # Probability of correct response prob(item1, theta) # First derivative of probability of correct response: prob(item1, theta, derivative = 1) # Second derivative of probability of correct response: prob(item1, theta, derivative = 2) # Multiple theta values theta_n <- rnorm(5) prob(item1, theta_n) prob(item1, theta_n, derivative = 1) prob(item1, theta_n, derivative = 2) # Probability of each response category for Generalized Partial Credit Model item2 <- generate_item(model = "GPCM", n_categories = 4) prob(item2, theta) # First derivative of each response category prob(item2, theta, derivative = 1) # Second derivative of each response category prob(item2, theta, derivative = 2) theta <- rnorm(1) item1 <- generate_item(model = "GPCM2") # Probability of correct response prob(item1, theta) # First derivative of probability of correct response: prob(item1, theta, derivative = 1) # Second derivative of probability of correct response: prob(item1, theta, derivative = 2) # Multiple theta values theta_n <- rnorm(5) prob(item1, theta_n) prob(item1, theta_n, derivative = 1) prob(item1, theta_n, derivative = 2) theta <- rnorm(1) ip <- generate_ip(model = "3PL") # Probability of correct response prob(ip, theta) # First derivative of probability of correct response: prob(ip, theta, derivative = 1) # Second derivative of probability of correct response: prob(ip, theta, derivative = 2) # Multiple theta theta_n <- rnorm(3) prob(ip, theta_n) prob(ip, theta_n, derivative = 1) prob(ip, theta_n, derivative = 2) # Extract probabilities of correct response (i.e. response is "1") sapply(prob(ip, theta_n), `[`, TRUE, "1") # Probabilities of incorrect response sapply(prob(ip, theta_n), `[`, TRUE, "0") # Probability of each response category for Generalized Partial Credit Model ip <- generate_ip(model = "GPCM", n = 4, n_categories = c(3, 4, 6, 5)) prob(ip, theta) # First derivative of each response category prob(ip, theta, derivative = 1) # Second derivative of each response category prob(ip, theta, derivative = 2) # Probability of a mixture of items models ip <- generate_ip(model = c("GPCM", "2PL", "3PL", "GPCM"), n_categories = c(4, 2, 2, 3)) prob(ip, theta) # Multiple theta prob(ip, theta_n) # Extract probabilities of score "2" for each theta value sapply(prob(ip, theta_n), `[`, TRUE, "2") theta <- rnorm(1) t1 <- generate_testlet(model_items = "3PL") # Probability of correct response prob(t1, theta) # First derivative of probability of correct response: prob(t1, theta, derivative = 1) # Second derivative of probability of correct response: prob(t1, theta, derivative = 2)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.