| fitted {ltm} | R Documentation |
Computes the expected frequencies for vectors of response patterns.
## S3 method for class 'grm':
fitted(object, resp.patterns = NULL,
type = c("expected", "marginal-probabilities",
"conditional-probabilities"), ...)
## S3 method for class 'ltm':
fitted(object, resp.patterns = NULL,
type = c("expected", "marginal-probabilities",
"conditional-probabilities"), ...)
## S3 method for class 'rasch':
fitted(object, resp.patterns = NULL,
type = c("expected", "marginal-probabilities",
"conditional-probabilities"), ...)
## S3 method for class 'tpm':
fitted(object, resp.patterns = NULL,
type = c("expected", "marginal-probabilities",
"conditional-probabilities"), ...)
object |
an object inheriting from either class grm, class ltm, class rasch, or
class tpm. |
resp.patterns |
a matrix or a data.frame of response patterns with columns denoting the
items; if NULL the expected frequencies are computed for the observed response patterns. |
type |
if type == "marginal-probabilities" the marginal probabilities for each response are
computed; these are given by int { prod_{i = 1}^p Pr(x_i = 1 | z)^{x_i} times
(1 - Pr(x_i = 1 | z))^{1 - x_i} }p(z) dz, where x_i denotes
the ith item and z the latent variable. If type == "expected" the expected frequencies
for each response are computed, which are the marginal probabilities times the number of sample units. If
type == "conditional-probabilities" the conditional probabilities for each response and item are
computed; these are Pr(x_i = 1 | hat{z}), where hat{z} is the ability estimate . |
... |
additional arguments; currently none is used. |
a numeric matrix or a list containing either the response patterns of interest with their expected
frequencies or marginal probabilities, if type == "expected" || "marginal-probabilities" or the conditional
probabilities for each response pattern and item, if type == "conditional-probabilities".
Dimitris Rizopoulos dimitris.rizopoulos@med.kuleuven.be
residuals.grm,
residuals.ltm,
residuals.rasch,
residuals.tpm
fit <- grm(Science[c(1,3,4,7)]) fitted(fit, resp.patterns = matrix(1:4, nr = 4, nc = 4)) fit <- rasch(LSAT) fitted(fit, type = "conditional-probabilities")