Computes the expected frequencies for vectors of response patterns.

```
# S3 method for gpcm
fitted(object, resp.patterns = NULL,
type = c("expected", "marginal-probabilities",
"conditional-probabilities"), …)
```# S3 method for grm
fitted(object, resp.patterns = NULL,
type = c("expected", "marginal-probabilities",
"conditional-probabilities"), …)

# S3 method for ltm
fitted(object, resp.patterns = NULL,
type = c("expected", "marginal-probabilities",
"conditional-probabilities"), …)

# S3 method for rasch
fitted(object, resp.patterns = NULL,
type = c("expected", "marginal-probabilities",
"conditional-probabilities"), …)

# S3 method for tpm
fitted(object, resp.patterns = NULL,
type = c("expected", "marginal-probabilities",
"conditional-probabilities"), …)

object

an object inheriting either from class `gpcm`

, 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 \(i\)th 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"`

.

`residuals.gpcm`

,
`residuals.grm`

,
`residuals.ltm`

,
`residuals.rasch`

,
`residuals.tpm`

```
# NOT RUN {
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")
# }
```

Run the code above in your browser using DataLab