ltm (version 0.8-9)

coef: Extract Estimated Loadings

Description

Extracts the estimated parameters from either grm, ltm, rasch or tpm objects.

Usage

## S3 method for class 'grm':
coef(object, \dots)

## S3 method for class 'ltm':
coef(object, standardized = FALSE, prob = FALSE, order = FALSE, \dots)

## S3 method for class 'rasch':
coef(object, prob = FALSE, order = FALSE, \dots)

## S3 method for class 'tpm':
coef(object, prob = FALSE, order = FALSE, \dots)

Arguments

object

an object inheriting from either class grm, class ltm, class rasch or class tpm.

standardized

logical; if TRUE the standardized loadings are also returned. See Details for more info.

prob

logical; if TRUE the probability of a positive response for the median individual (i.e., $Pr(x_i = 1 | z = 0)$, with $i = 1, \ldots, p$ denoting the items) is also returned.

order

logical; if TRUE the items are sorted according to the difficulty estimates.

...

additional arguments; currently none is used.

Value

• A list or a matrix of the estimated parameters for the fitted model.

Details

The standardization of the factor loadings is useful in order to form a link to the Underlying Variable approach. In particular, the standardized form of the factor loadings represents the correlation coefficient between the latent variables and the underlying continuous variables based on which the dichotomous outcomes arise (see Bartholomew and Knott, 1999, p.87-88 or Bartholomew et al., 2002, p.191). The standardized factor loadings are computed only for the linear one- and two-factor models, fitted by ltm().

References

Bartholomew, D. and Knott, M. (1999) Latent Variable Models and Factor Analysis, 2nd ed. London: Arnold. Bartholomew, D., Steel, F., Moustaki, I. and Galbraith, J. (2002) The Analysis and Interpretation of Multivariate Data for Social Scientists. London: Chapman and Hall.

See Also

grm, ltm, rasch, tpm

Examples

fit <- grm(Science[c(1,3,4,7)])
coef(fit)

fit <- ltm(LSAT ~ z1)
coef(fit, TRUE, TRUE)

m <- rasch(LSAT)
coef(fit, TRUE, TRUE)