## S3 method for class 'gpcm':
fitted(object, resp.patterns = NULL,
type = c("expected", "marginal-probabilities",
"conditional-probabilities"), ...)
## 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"), ...)gpcm, class grm, class ltm, class rasch, or
class tpm.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 == "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 $x_i$ dematrix 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".residuals.gpcm,
residuals.grm,
residuals.ltm,
residuals.rasch,
residuals.tpmfit <- 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")Run the code above in your browser using DataLab